How to save on AWS costs is a question that many project managers will have likely asked themselves since Amazon launched its Elastic Compute Cloud (EC2) in 2006. Indeed, as the number of organizations using the EC2 platform has grown – and the amount each organization spends on EC2 balloons – the question has probably been asked more frequently than you might imagine.

There are a number of answers to this question. Organizations can take advantage of Amazon´s flexible pricing plans to assign instances to the most cost-effective option or, if they have the organizational foresight to plan three years ahead, use Amazon´s Reserved Instances pricing model to save on AWS costs.

Where organizations use a high proportion of non-production instances, one way how to save on AWS costs is to reassign resources in order to develop scheduling scripts. Although this solution may be counter-productive in terms of reducing costs, it is a lot more reliable than asking teams to switch off their non-production instances before they go home at night.

Another way how to save on AWS costs is to implement an AWS management solution from ParkMyCloud. ParkMyCloud is a lightweight Software-as-a-Service app that automates the scheduling process in a simple user-friendly process called “parking”. The app even suggests which non-production instances are most suitable for parking – saving internal resources as well as AWS costs.

ParkMyCloud is also an exceptionally versatile cloud management solution. It enables administrators to set permission levels for development teams, provides a single-view dashboard across all accounts for easy governance, and allows for the schedule to be snoozed when developers want to access their development, testing and staging instances while they are parked.

To find out more about how to save on AWS costs with ParkMyCloud, contact us today.

AWS Neptune Preview – Amazon’s Graph Database Service

At the AWS DC Meetup we organized last week, we got a preview of AWS Neptune, Amazon’s new managed graph database service. It was announced at AWS re:Invent 2017, is currently in preview and will launch for general availability this summer.

What is a graph database?

A graph database is a database optimized to store and process highly connected data – in short, it’s about relationships. The data structure for these databases consists of vertices and direct links called edges.

Use cases for such highly-connected data include social networking, restaurant recommendations, retail fraud detection, knowledge graphs, life sciences, and network & IT ops. For a restaurant recommendations use case, for example, you may be interested in the relationships between various users, where those users live, what types of restaurants those users like, where the restaurants are located, what sort of cuisine they serve, and more. With a graph database, you can use the relationships between these data points to provide contextual restaurant recommendations to users.

Tired of SQL?

If you’re tired of SQL, AWS Neptune may be for you. A graph database is fundamentally different from SQL. There are no tables, columns, or rows – it feels like a NoSQL database. There are only two data types: vertices and edges, both of which have properties stored as key-value pairs.

AWS Neptune is fully managed, which means that database management tasks like hardware provisioning, software patching, setup, configuration, and backups are taken care of for you.

It’s also highly available and shows up in multiple availability zones. This is very similar to Aurora, the relational database from Amazon, in its architecture and availability.

Neptune supports Property Graph and W3C’s RDF. You can use these to build your own web of data sets that you care about, and build networks across the data sets in the way that makes sense for your data, not with arbitrary presets. You can do this using the graph models’ query languages: Apache TinkerPop Gremlin and SPARQL.

There is no cost to use Neptune during the preview period. Once it’s generally available, pricing will rely on On Demand EC2 instances – which means ParkMyCloud will be looking into ways to assist Neptune users with cost control.

If you’re interested in the new service, you can check out more about AWS Neptune and sign up for the preview.

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7 Ways Cloud Services Pricing is Confusing

Beware the sticker shock – cloud services pricing is nothing close to simple, especially as you come to terms with the dollar amount on your monthly cloud bill. While cloud service providers like AWS, Azure, and Google were meant to provide compute resources to save enterprises money on their infrastructure, cloud services pricing is complicated, messy, and difficult to understand. Here are 7 ways that cloud providers obscure pricing on your monthly bill:  

1 – They use varying terminology

For the purpose of this post, we’ll focus on the three biggest cloud service providers: AWS, Azure, and Google. Between these three cloud providers alone, different analogies are used for just about every component of services offered.

For example, when you think of a virtual machine (VM), that’s what AWS calls an “instance,” Azure calls a “virtual machine,” and Google calls a “virtual machine instance.” If you have a group of these different machines, or instances, in Amazon and Google they’re called “auto-scaling” groups, whereas in Azure they’re called “scale sets.” There’s also different terminology for their pricing models. AWS offers on-demand instances, Azure calls it “pay as you go,” and Google refers to it as “sustained use.” You’ve also got “reserved instances” in AWS, “reserved VM instances” in Azure, and “committed use” in Google. And you have spot instances in AWS, which are the same as low-priority VMs in Azure, and preemptible instances in Google.

2 – There’s a multitude of variables

Operating systems, compute, network, memory, and disk space are all different factors that go into the pricing and sizing of these instances. Each of these virtual machine instances also have different categories: general purpose, compute optimized, memory optimized, disk optimized and other various types. Then, within each of these different instance types, there are different families. In AWS, the cheapest and smallest instances are in the “t2” family, in Azure they’re called the “A” family. On top of that, there are different generations within each of those families, so in AWS there’s t2, t3, m2, m3, m4, and within each of those processor families, different sizes (small, medium, large, and extra large). So there’s lots of different options available. Oh, and lots confusion, too.  

3 – It’s hard to see what you’re spending

If you aren’t familiar with AWS, Azure, or Google Cloud’s consoles or dashboards, it can be hard to find what you’re looking for. To find specific features, you really need to dig in, but event just trying to figure out the basics of how much you’re currently spending, and predicting how much you will be spending – all can be very hard to understand. You can go with the option of building your own dashboard by pulling in from their APIs, but that takes a lot of upfront effort, or you can purchase an external tool to manage overall cost and spending.

4 – It’s based on what you provision…not what you use

Cloud services pricing can charge on a per-hour, per-minute, or per-second basis. If you’re used to the on-prem model where you just deploy things and leave them running 24/7, then you may not be used to this kind of pricing model. But when you move to the cloud’s on-demand pricing models, everything is based on the amount of time you use it.

When you’re charged per hour, it might seem like 6 cents per hour is not that much, but after running instances for 730 hours in a month, it turns out to be a lot of money. This leads to another sub-point: the bill you get at the end of the month doesn’t come until 5 days after the month ends, and it’s not until that point that you get to see what you’ve used. As you’re using instances (or VMs) during the time you need them, you don’t really think about turning them off or even losing servers. We’ve had customers who have servers in different regions, or on different accounts that don’t get checked regularly, and they didn’t even realize they’ve been running all this time, charging up bill after bill.

You might also be overprovisioning or oversizing resources — for example, provisioning multiple extra large instances thinking you might need them someday or use them down the line. If you’re used to that, and overprovisioning everything by twice as much as you need, it can really come back to bite you when you go look at the bill and you’ve been running resources without utilizing them, but are still getting charged for them – constantly.

5 – They change the pricing frequently

Cloud services pricing has changed quite often. So far, they have been trending downward, so things have been getting cheaper over time due to factors like competition and increased utilization of data centers in their space. However, don’t jump to conclude that price changes will never go up.

Frequent price changes make it hard to map out usage and costs over time. Amazon has already made changes to their price more than 60 times since they’ve been around, making it hard for users to plan a long-term approach. Also for some of these instances, if you have them deployed for a long time, the prices of instances don’t display in a way that is easy to track, so you may not even realize that there’s been a price change if you’ve been running the same instances on a consistent basis.

6 – They offer cost savings options… but they’re difficult to understand (or implement)

In AWS, there are some cost savings measures available for shutting things down on a schedule, but in order to run them you need to be familiar with Amazon’s internal tools like Lambda and RDS. If you’re not already familiar, it may be difficult to actually implement this just for the sake of getting things to turn off on a schedule.  

One of the other things you can use in AWS is Reserved Instances, or with Azure you can pay upfront for a full year or two years. The problem: you need to plan ahead for the next 12 to 24 months and know exactly what you’re going to use over that time, which sort of goes against the nature of cloud as a dynamic environment where you can just use what you need. Not to mention, going back to point #2, the obscure terminology for spot instances, reserved instances, and what the different sizes are.

7 – Each service is billed in a different way

Cloud services pricing shifts between IaaS (infrastructure as a service), which uses VMs that are billed one way, and PaaS (platform as a service) gets billed another way. Different mechanisms for billing can be very confusing as you start expanding into different services that cloud providers offer.

As an example, the Lambda functions in AWS are charged based on the number of requests for your functions, the duration, and the time it takes for your code to execute. The Lambda free tier includes 1M free requests per month and 400,000 GB-seconds of compute time per month, or you can get 1M request free and $0.20 per 1M requests thereafter, OR use “duration” tier and get 400,000 GB-seconds per month free, $0.00001667 for every GB-second used thereafter – simple, right? Not so much.

Another example comes from the databases you can run in Azure. Databases can run as a single server or can be priced by elastic pools, each with different tables based on the type of database, then priced by storage, number of databases, etc.

With Google Kubernetes clusters, you’re getting charged per node in the cluster, and each node is charged based on size. Nodes are auto-scaled, so price will go up and down based on the amount that you need. Once again, there’s no easy way of knowing how much you use or how much you need, making it hard to plan ahead.

What can you do about it?

Ultimately, cloud service offerings are there to help enterprises save money on their infrastructures, and they’re great options IF you know how to use them. To optimize your cloud environment and save money on costs, we have a few suggestions:

    • Get a single view of your billing. You can write your own scripts (but that’s not the best answer) or use an external tool.  
    • Understand how each of the services you use is billed. Download the bill, look through it, and work with your team to understand how you’re being billed.
    • Make sure you’re not running anything you shouldn’t be. Shut things down when you don’t need them, like dev and test instance on nights and weekends.Try to plan out as much as you can in advance.
    • Review regularly to plan out usage and schedules as much as you can in advance
    • Put governance measures in place so that users can only access certain features, regions, and limits within the environment. 

Cloud services pricing is tricky, complicated, and hard to understand. Don’t let this confusion affect your monthly cloud bill. Try ParkMyCloud for an automated solution to cost control.

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How to Use Terraform Provisioning and ParkMyCloud to Manage AWS

Recently, I’ve been on a few phone calls where I get asked about cost management of resources built in AWS using Terraform provisioning. One of the great things about working with ParkMyCloud customers is that I get a chance to talk to a lot of different technical teams from various types of businesses. I get a feel for how the modern IT landscape is shifting and trending, plus I get exposed to the variety of tools that are used in real-world use cases, like Atlassian Bamboo, Jenkins, Slack, Okta, and Hashicorp’s Terraform.

Terraform seems to be the biggest player in the “infrastructure as code” arena. If you’re not already familiar with it, the utilization is fairly straightforward and the benefits quickly become apparent. You take a text file, use it to describe your infrastructure down to the finest detail, then run “terraform apply” and it just happens. Then, if you need to change your infrastructure, or revoke any unwanted changes, Terraform can be updated or roll back to a known state. By working together with AWS, Azure, VMware, Oracle, and much more, Terraform can be your one place for infrastructure deployment and provisioning.

How to Use Terraform Provisioning and ParkMyCloud with AWS Autoscaling Groups

I’ve talked to a few customers recently, and they utilize Terraform as their main provisioning tool, while ParkMyCloud is their ongoing cloud governance and cost control tool. Using these two systems together is great, but one main confusion comes in with AWS’s AutoScaling Groups. The question I usually get asked is around how Terraform handles the changes that ParkMyCloud makes when scheduling ASGs, so let’s take a look at the interaction.

When ParkMyCloud “parks” an ASG, it sets the Min/Max/Desired to 0/0/0 by default, then sets the values for “started” to the values you had originally entered for that ASG. If you run “terraform apply” while the ASG is parked, then terraform will complain that the Min/Max/Desired values are 0 and will change them to the values you state. Then, when ParkMyCloud notices this during the next time it pulls from AWS (which is every 10 minutes), it will see that it is started and stop the ASG as normal.

If you change the value of the Min/Max/Desired in Terraform, this will get picked up by ParkMyCloud as the new “on” values, even if the ASG was parked when you updated it. This means you can keep using Terraform to deploy and update the ASG, while still using ParkMyCloud to park the instances when they’re idle.

How to Use Terraform to Set Up ParkMyCloud

If you currently leverage Terraform provisioning for AWS resources but don’t have ParkMyCloud connected yet, you can also utilize Terraform to do the initial setup of ParkMyCloud. Use this handy Terraform script to create the necessary IAM Role and Policy in your AWS account, then paste the ARN output into your ParkMyCloud account for easy setup. Now you’ll be deploying your instances as usual using Terraform provisioning while parking them easily to save money!

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$12.9 Billion in wasted cloud spend this year.

Wake up and smell the wasted cloud spend. The cloud shift is not exactly a shift anymore, it’s an evident transition. It’s less of a “disruption” to the IT market and more of an expectation. And with enterprises following a visible path headed towards the cloud, it’s clear that their IT spend is going in the same direction: up.

Enterprises have a unique advantage as their cloud usage continues to grow and evolve. The ability to see where IT spend is going is a great opportunity to optimize resources and minimize wasted cloud spend, and one of the best ways to do that is by identifying and preventing cloud waste.

So, how much cloud waste is out there and how big is the problem? What difference does this make to the enterprises adopting cloud services at an ever-growing rate? Let’s take a look.

The State of the Cloud Market in 2018

The numbers don’t lie. For a real sense of how much wasted cloud spend there is, the first step is to look at how much money enterprises are spending in this space at an aggregate level.

Gartner’s latest IT spending forecast predicts that worldwide IT spending will reach $3.7 trillion in 2018, up 4.5 percent from 2017. Of that number, the portion spent in the public cloud market is expected to reach $305.8 billion in 2018, up $45.6 billion from 2017.

The last time we examined the numbers back in 2016, the global public cloud market was sitting at around $200 billion and Gartner had predicted that the cloud shift would affect $1 trillion in IT spending by 2020. Well, with an updated forecast and over $100 billion dollars later, growth could very well exceed predictions.

The global cloud market and the portion attributed to public cloud spend are what give us the ‘big picture’ of the cloud shift, and it just keeps growing, and growing, and growing. You get the idea. To start understanding wasted cloud spend at an organizational level, let’s break this down further by looking at an area that Gartner says is driving a lot of this growth: infrastructure as a service (IaaS).

Wasted Cloud Spend in IaaS

As enterprises increasingly turn to cloud service providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) to provide compute resources for hosting components of their infrastructures, IaaS plays a significant role in both cloud spend and cloud waste.

Of the forecasted $305.8 billion dollar public cloud market  for 2018, $45.8 billion of that will be spent on IaaS, ⅔ of which goes directly to compute resources. This is where we get into the waste part:

  • 44% of compute resources are used for non-production purposes (i.e. development, staging, testing, QA)
  • The majority of servers used for these functions only need to run during the typical 40-hour work week (Monday through Friday, 9 to 5) and do not need to run 24/7
  • Cloud service providers are still charging you by the hour (or minute, or even by the second) for providing compute resources

The bottom line: for the other 128 hours of the week (or 7,680 minutes, or 460,800 seconds) – you’re getting charged for resources you’re not even using. And there’s a large percent of your waste!

What You Can Do to Prevent Wasted Cloud Spend

Turn off your cloud resources.

The easiest and fastest way to save money on your idle cloud resources when  is by simply by not using them. In other words, turn them off. When you think of the cloud as a utility like electricity, it’s as simple as turning off the lights every night and when you’re not at home. With ParkMyCloud you can automatically schedule your cloud resources to turn off when you don’t need them, like nights and weekends, and eliminate 65% or more on your monthly bill with AWS, Azure, and Google. Wham. bam.

Turn on your SmartParking.

You already know that you don’t need your servers to be on during nights and weekends, so you shut them off. That’s great, but what if you could save even more with valuable insight and information about your exact usage over time?

With ParkMyCloud’s new SmartParking feature, the platform will track your utilization data, look for patterns and create recommended schedules for each instance, allowing you to turn them off when they’re typically idle.

There’s a lot of cloud waste out there, but there’s also something you can do about it: try ParkMyCloud today.

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The Cost of Cloud Computing Is, in Fact, Dropping Dramatically

You might read the headline statement that the cost of cloud computing is dropping  and say “Well, duh!”. Or maybe you’re on the other side of the fence. A coworker recently referred me to a very interesting blog on the Kapwing site that states Cloud costs aren’t actually dropping dramatically. The author defines“dramatically” based on the targets set by Moore’s Law or the more recently proposed Bezos’ Law, which states that “a unit of [cloud] computing power price is reduced by 50 percent approximately every three years.” The blog focused on the cost of the Google Cloud Platform (GCP) n1-standard-8 machine type, and illustrated historical data for the Iowa region:

Date N1-standard-8 Cost per Hour
January 2016 $0.40
January 2017 $0.40
January 2018 $0.38

The Kapwing blog also illustrates that the GCP storage and network egress costs have not changed at all in three years. These figures certainly add up to a conclusion that Bezos’ Law is not working…at least not for GCP.

Whose law is it anyway?

If we turn this around and try to apply Bezos’ Law to, well, Bezos’ Cloud we see a somewhat different story.

The approach to measuring AWS pricing changes needs to be a bit more systematic than for GCP, as the AWS instance types have been evolving quite a bit over their history. This evolution is shown by the digit that follows the first character in the instance type, indicating the version or generation number of the given instance type . For example, m1.large vs. m5.large. These are similar virtual machines in terms of specifications, with 2 vCPUs and about 8GB RAM, but the m1.large was released in October 2007, and the m5.large in November 2017. While  the “1” in the GCP n1-standard-8 could also be a version number,  it is still the only version I can see back to at least 2013. For AWS, changes in these generation numbers happen more frequently and likely reflect the new generations of underlying hardware on which the instance can be run.

Show me the data!

In any event, when we make use of the Internet Archive to look at  pricing changes of the specific instance type as well as the instance type “family” as it evolves, we see the following (all prices are USD cost per hour for Linux on-demand from the us-east-1 region in the earliest available archived month of data for the quoted year):

m1.large m3.large m4.large m5.large Reduction from previous year/generation 3-year reduction
2008 $0.40
2009 $0.40 0%
2010 $0.34  -18%
2011 $0.34 0% -18%
2012 $0.32 -6% -25%
2013 $0.26 -23% -31%
2014 $0.24 $0.23 -13% -46%
2015 $0.175 $0.14 -64% -103%
2016 $0.175 $0.133 $0.120 -17% -80%
2017 $0.175 $0.133 $0.108 -11% -113%
2018* $0.175 $0.133 $0.100 $0.096 -13% -46%

*Latest Internet Archive data from Dec 2017 but confirmed to match current Jan 2018 AWS pricing.

FWIW: The second generation m2.large instance type was skipped, though in October 2012 AWS released the “Second Generation Standard” instances for Extra Large and Double Extra Large – along with about an 18% price reduction for the first generation.

To confirm that we can safely compare these prices, we need to look at how the mX.large family has evolved over the years:

Instance type Specifications
m1.large (originally defined as the “Standard Large” type) 2vCPU w/ECU of 4, 7.5GB RAM
m3.large 2vCPU w/ECU of 6.5, 7.5GB RAM
m4.large 2vCPU w/ECU of 6.5, 8GB RAM
m5.large 2vCPU w/ECU of 10, 8GB RAM

A couple of notes on this:

  • ECU is “Elastic Compute Unit” –  a standardized measure AWS uses to support comparison between CPUs on different instance types. At one point, 1 ECU was defined as the compute-power of a 1GHz CPU circa 2007.
  • I realize that the AWS mX.large family is not equivalent to the GCP n1-standard-8 machine type mentioned earlier, but I was looking for an AWS machine type family with a long history and fairly consistent configuration(and this is not intended to be a GCP vs AWS cost comparison).

The drop in the cost of cloud computing looks kinda dramatic to me…

The net average of the 3-year reduction figures is -58% per year, so Bezos’ Law is looking pretty good. (And there is probably an interesting grad-student dissertation somewhere about  how serverless technologies fit into Bezos’ Law…)  When you factor the m1.large ECU of 4 versus the m5.large ECU of 10 into the picture, more than doubling the net computing power, one could easily argue that Bezos’ Law significantly understates the situation. Overall, there is a trend here of not just a significantly declining prices, but also greatly increased capability (higher ECU and more RAM), and certainly reflecting an increased value to the customer.

So, why has the pricing of the older m1 and m3 generations gone flat but is still so much more expensive? On the one hand, one could imagine that the older generations of underlying hardware consume more rack space and power, and thus cost Amazon more to operate. On the other hand, they have LONG since amortized this hardware cost, so maybe they could drop the prices. The reality is probably somewhere in between, where they are trying to motivate customers to migrate to newer hardware, allowing them to eventually retire the old hardware and reuse the rack space.

Intergenerational Rightsizing

There is definite motivation here to do a lateral inter-generation “rightsizing” move. We most commonly think of rightsizing as moving an over-powered/under-utilized virtual machine from one instance size to another, like m5.large to m5.medium, but intergenerational rightsizing can add up to some serious savings very quickly. For example, an older m3.large instance could be moved to an m5.large instance in about 1 minute or less (I just did it in 55 seconds: Stop instance, Change Instance Type, Start Instance), immediately saving 39%. This can frequently be done without any impact to the underlying OS. I essentially just pulled out my old CPU and RAM chips and dropped in new ones. Note that it is not necessarily this easy for all instance types – some older AMI’s can break the transition to a newer instance type because of network or other drivers, but it is worth a shot, and the AWS Console should let you know if the transition is not supported (of course: as always make a snapshot first!)

Conclusion

For the full view of cloud compute cost trends, we need to look at both the cost of specific instance types, and the continually evolving generations of that instance type. When we do this, we can see that the cost of cloud computing is, in fact, dropping dramatically…at least on AWS.

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ParkMyCloud Reviews – Customer Video Testimonials

A few weeks ago at the 2017 AWS re:Invent conference in Las Vegas, we had the opportunity to meet some of our customers at the booth, get their product feedback, and a few shared their ParkMyCloud reviews as video testimonials. As part of our ongoing efforts to save money on cloud costs with a fully automated, simple-to-use SaaS platform, we rely on our customers to give us insight into how ParkMyCloud has helped them. Here’s what they had to say:

TJ McAteer, Prosight Specialty Insurance

“It’s all very well documented. We got it set up within an afternoon with our trial, and then it was very easy to differentiate and show that value – and that’s really the most attractive piece of it.”

As the person responsible for running the cloud engineering infrastructure at ProSight Specialty Insurance, ParkMyCloud had everything TJ was looking for. Not only that, but it was easy to use, well managed, and demonstrated its value right away.

James LaRocque, Decision Resources Group

“What’s nice about it is the ability to track financials of what you’re actually saving, and open it up to different team members to be able to suspend it from the parked schedules and turn it back on when needed.”

As a Senior DevOps engineer at Decision Resources Group, James LaRocque discovered ParkMyCloud at the 2016 AWS re:Invent and has been a customer ever since. He noted that while he could have gone with scripting, ParkMyCloud offered the increased benefits of financial tracking and user capabilities.

“The return on investment is huge.”

Kurt Brochu, Sysco Foods

“We had instant gratification as soon as we enabled it.”

Kurt Brochu, Senior Manager of the Cloud Enablement Team at Sysco Foods, was immediately pleased to see ParkMyCloud saving money on cloud costs as soon as they put it into action. Once he was able to see how much they could save on their monthly cloud bill, the next step was simple.   

“We were able to save over $500 in monthly spend by just using it against one team. We are rolling out to 14 other teams over the course of the next 2 weeks.”

Mark Graff, Dolby Labs

“The main reason why we went for it was that it was easy to give our users the ability to start and stop instances without having to give them access to the console.”

Mike Graff, the Senior Infrastructure Manager at Dolby Labs, became a ParkMyCloud customer thanks to one of his engineers in Europe.

“We just give them credentials, they can hop into ParkMyCloud and go to start and stop instances. You don’t have to have any user permissions in AWS – that was a big win for us.”


We continue to innovate and improve our platform’s cloud cost management capabilities with the addition of SmartParking recommendations, SmartSizing, Alicloud and more. Customer feedback is essential to making sure that not only are we saving our customers time and money, but also gaining valuable insight into what makes ParkMyCloud a great tool.

If you use our platform, we’d love to get a ParkMyCloud review from you and hear about how ParkMyCloud has helped your business – there’s a hoodie in it for you! Please feel free to participate in the comments below or with a direct email to info@parkmycloud.com

 

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Introducing SmartParking: Automatic On/Off Schedules based on AWS CloudWatch Metrics

Today, we’re excited to bring you SmartParkingTM – automatic, custom on/off schedules for individual resources based on AWS CloudWatch metrics!

ParkMyCloud customers have always appreciated parking recommendations based on keywords found in their instance names and tags – for example, ParkMyCloud recommends that an instance tagged “dev” can be parked, as it’s likely not needed outside of a Monday-Friday workday.

Now, SmartParking will look for patterns in your utilization data from AWS CloudWatch, and create recommend schedules for each instance to turn them off when they are typically idle. This minimizes idle time to maximize savings on your resources.

With SmartParking, you eliminate the extra step of checking in with your colleagues to make sure the schedules you’re putting on their workloads doesn’t interfere with their needs. Now you can receive automatic recommendations to park resources when you know they won’t be used.

SmartParking schedules are provided as recommendations, which you can then click to apply. This release supports SmartParking for AWS resources, with plans to add Azure and Google Cloud SmartParking.

Customize Your Recommendations like your 401K

Different users will have different preferences about what they consider “parkable” times for an instance. So, like your investment portfolios, you can choose to receive SmartParking schedules that are “conservative”, “balanced”, or “aggressive”. And like an investment, a bigger risk comes with the opportunity for a bigger reward.

If you’d like to prioritize the maximum savings amount, then choose aggressive SmartParking schedules. You will park instances – and therefore save money – for the most time, with the “risk” of occasional inconvenience by having something turned off when someone needs it. Your users can always log in to ParkMyCloud and override the schedule with the “snooze button” if they need to use the instance when it’s parked.

On the other hand, if you would like to ensure that your instances are never parked when they might be needed, choose a conservative SmartParking schedule. It will only recommend parked times when the instance is never used. Choose “balanced” for a happy medium.

What People are Saying: Save More, Easier than Ever

Several existing ParkMyCloud customers have previewed the new functionality. “ParkMyCloud has helped my team save so much on our AWS bill already, and SmartParking will make it even easier,” said Tosin Ojediran, DevOps Engineer at a FinTech company. “The automatic schedules will save us time and make sure our instances are never running when they don’t need to be.”

Already a ParkMyCloud user? Log in to your account to try out the new SmartParking. Note that you will need to have AWS CloudWatch metrics enabled for several weeks in order for us to see your usage trends and make recommendations. If you haven’t already, you will need to update your AWS policy.

New to ParkMyCloud? Start a free trial here.

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ParkMyCloud’s Top 5 Blog Posts of 2017

Before we ring in the new year, ParkMyCloud is taking a look back at 2017. We get a lot of great feedback on our blogs so we decided to summarize our top 5 blog posts, as indicated by our readers (views and shares). In case you missed them, please take a moment and enjoy our most popular posts of 2017!  

Azure vs AWS 2017: Is Azure really surpassing AWS?

Azure vs AWS – what’s the deal? After both cloud providers reported their quarterly earnings at the same time, speculation grew as to whether Azure might have a shot at outpacing Amazon. Provocative headlines teased the idea that Azure is catching up with AWS, making it a great opportunity to compare two out of the ‘big three’ providers. While it may seem like AWS is the one to beat, this blog examines whether Azure is catching up, where they are gaining ground, and why the debate even matters.

AWS vs Google Cloud Pricing – A Comprehensive Look

When it comes to comparing cloud providers, a look at pricing is not only helpful, it’s imperative. AWS and Google Cloud Platform (GCP) use different terminology for their instances, different categories of compute sizing, and take marketing liberties in describing their offerings. To make matters even more confusing, each provider takes a different approach to pricing, charging you by the hour in some cases or by the minute in others, and both having minimums. This blog breaks down all of the jargon and gives you valuable insight into how AWS and GCP are charging you on their monthly cloud bill.

The Cloud Waste Problem That’s Killing Your Business (And What To Do About It)

As enterprises continue shifting to the cloud, service providers like AWS, GCP, Azure, and more offer cloud services as a valuable utility for cost savings. However, as a utility, the cloud has serious potential for waste if not used optimally. What is “cloud waste” and where does it come from? What are the consequences? What can you do to reduce it? This blog answers those burning questions and tells you how to prevent waste and optimize your cloud spend.

Start and Stop RDS Instances – and Schedule with ParkMyCloud

When Amazon announced the release of start and stop RDS instances, AWS users finally had the ability to ‘turn off’ their RDS instances and save money on their cloud bill – nice! However, they would still be charged for provisioned storage, manual snapshots, and automated backup storage. What if there was a solution to starting and stopping RDS instances on an automated schedule, ensuring that they’re not left running when not needed? This blog explains how ParkMyCloud offers automated cost control on a schedule, saving you even more on your monthly cloud bill.

Why We Love the AWS IoT Button

We talk a lot about how ParkMyCloud can save you money on your cloud bill, because we can, but we also love to share the exciting, fun, and innovative offerings that the could brings. The AWS IoT button is a device like no other. You can program it to integrate with any internet-connected device, opening up a whole world of possibilities for what you can do with it. Make a remote control for Netflix, brew coffee in the morning without getting out of bed, or place a takeout order for lunch, all with the push of a button. This blog tells you about how the button was created, how to use it, and some ways that creative developers are using the AWS IoT button.

To another great year…

As we wrap up 2017, the ParkMyCloud team is especially thankful to those of you who have made our blog and our Cloud Cost Control platform successful. We look forward to another great year of keeping up with the cloud, sharing our posts, and of course, saving you money on your cloud bill.

Cheers to 2018! Happy New Year from the ParkMyCloud team and keep an eye open for SmartParking and several great announcements in early January.    

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Spot Instance Hibernation – What It Means For You

At AWS re:Invent 2017, one of the announcements that was made is that spot instance hibernation is now available. This change to how AWS spot instances works can mean some tweaks to how you approach this instance type. Let’s explore the ramifications of this and see what it means for you and your infrastructure.

What are Spot Instances?

When you use a cloud provider like AWS, they run data centers so you don’t have to. In doing so, those data centers have similar side effects as traditional on-prem deployments, including spare compute power when utilization is low. AWS decided to let free market forces work to their advantage by offering these spare resources at auction-style prices.

How this worked in practice (prior to this recent hibernation announcement) involved naming your bid price for how much you were willing to pay for an EC2 instance. Once the price of a spot instance went below your bid price, your instance started up and began doing work. Later, when the cost was above your bid price, your instance would be terminated.

Typical Spot Instance Use Cases

As you can tell, spot instances introduce a different way of thinking about your resources.  There are some use cases that don’t make any sense for spot instances, but others that can work well. For instance, high-performance computing scenarios that need a lot of machines for a short period of time can work well with spot instances, as long as the result isn’t extremely urgent. Another possibility is batch processing, like video conversions or scientific analysis, which can typically be done in off-hours without a human present to manually tweak things.

Hibernation vs. Termination

As mentioned above, the loss of a spot instance used to result in termination of the instance, regardless of the data or state of the machine. With Amazon’s recent announcement, you can now have spot instances hibernate instead. This means the system’s memory will be saved to the root EBS volume, then reloaded when the machine is resumed. It’s like time travel, but for your cloud infrastructure!

From a practical perspective, this can change how you approach spot instances. The main benefit is that you don’t have to prepare for sudden termination of your virtual machine, so more workloads could use spot instances with less preparation. The downside to this is that while your workload will eventually finish, you can’t quite be sure of when.

Spot Instances vs. Parking Schedules

The “not being sure when” part is the big differentiator between spot instance hibernation versus on-demand EC2 instances with parking schedules applied via ParkMyCloud. This new hibernation features means lots of benefits and cost savings, but introduces a nebulous time frame that tends to make developers (and executives) nervous. By utilizing known parking schedules that are automatically applied to instances, the cost savings can be quite comparable while maintaining business-hour uptime. The additional flexibility of manual or automated overrides via ParkMyCloud’s UI or API can mean all the difference to your cloud infrastructure team and the application owners who are running these workloads.

AWS claims that you can save up to 90% on your instance costs with spot instances. In the real world, various reports seem to be in the 50%-70% range, based on some stats from large companies like Pinterest and Vimeo. With parking schedules, most development teams turn off systems on nights and weekends, which is around 65% of the time. This means you can get similar savings, but with different timing structures for your use. The best way to save the most is to use a combination, so check out Amazon’s spot instances and try out ParkMyCloud for cost optimization for all workload types!

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Amazon’s EC2 Scheduler – How Does it Compare with ParkMyCloud?

Amazon recently announced updates to their EC2 scheduler, responding to the already-answered question: “How do I automatically start and stop my Amazon EC2 instances?”

Been there, done that. ParkMyCloud has been scheduling instances and saving our customers 65% or more on their monthly cloud bills from Amazon, Azure, and Google since 2015. It looks like Amazon is stepping up to the plate with their EC2 scheduler, but are they?

The premise is simple: pay for what you use. It’s what we’ve been saying all along – cloud services are like any other utility (electricity, water, gas) – you should use them only when needed to avoid paying more than necessary. You wouldn’t leave your lights on all night, so why leave your instances running when you’re not using them?

Until now, Amazon had basic scripting suggestions for starting and stopping your instances. With the EC2 scheduler, you’re getting instructions for how to configure a custom start and stop scheduler for your EC2 instances. Implementing the solution will require some work on your part, but will inevitably reduce costs. Welcome to the club, Amazon? Sort of, not really.

EC2 Scheduler vs ParkMyCloud

While the EC2 scheduler sounds good, we think ParkMyCloud is better, and not just because we’re biased. We took a look at the deployment guide for the EC2 scheduler and noticed a few things we offer that Amazon still doesn’t

  • This solution requires knowledge and operation of DynamoDB, Lambda, CloudWatch custom metrics, and Cloudformation templates, including Python scripting and Cloudformation coding.
    • None of that is required with our simple, easy-to-use platform. You don’t need a developer background to use ParkMyCloud, in fact you can use your mobile phone (insert link) or tablet.
  • There’s no UI, so it’s not obvious which instances are on what schedules.
    • ParkMyCloud has a simple UI with an icon driven operational dashboard and reporting so you can easily see and manage not only your AWS resources in a single-pane but your Azure and Google resources as well.
  • Modifications require code changes and CloudFormation deployments, including simple overrides of schedules.
    • Again, ParkMyCloud is easy to use, no coding or custom scripting required. Users can also temporarily override schedules if they need to use an instance on short notice, but will only have access to the resources you grant. And you can use our API and Policy Engine to automate scheduling as part of your DevOps process.
  • No SSO, reporting, notifications
    • Check, check, check. Did we mention that ParkMyCloud added some new features recently? You can now see resource utilization data for EC2 instances, viewable through animated heatmaps.
  • Doesn’t have SmartParking – automated parking recommendations based on usage data.
    • We do.
  • You cannot “snooze” (temporarily override) schedules on parked instances. You would have to do that manually through the AWS interface.
    • You can snooze schedules in ParkMyCloud with a button click.
  • Doesn’t work with Azure or Google.
    • We do.
  • Doesn’t park ASG
    • We do.
  • No Slack integration.
    • We do.

Conclusion

Amazon – nice try.

If you’re looking for an alternative to writing your own scripts (which we’ve known for a long time is not the best answer), you’re purely using AWS and EC2 instances, and are comfortable with all the PaaS offerings mentioned, then you might be okay with the EC2 scheduler. The solution works, although it comes with a lot of the same drawbacks that custom scripting has when compared to ParkMyCloud.

If you’re using more than just EC2 instances or even working with multiple providers, if you’re looking for a solution where you don’t need to be scripting, and if you’d prefer an automated tool that will cut your cloud costs with ease of use, reporting, and parking recommendations, then it’s a no-brainer. Give ParkMyCloud a try.

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New in ParkMyCloud: Visualize AWS Usage Data Trends

We are excited to share the latest release in ParkMyCloud: animated heat map displays. This builds on our previous release of static heat maps displaying AWS EC2 instance utilization metrics from CloudWatch. Now, this utilization data is animated to help you better identify usage patterns over time and create automated parking schedules.

The heatmaps will display data from a sequence of weeks, in the form of an animated “video”, letting you see patterns of usage over a period of time. You can take advantage of this feature to better plan ParkMyCloud parking schedules based on your actual instance utilization.

Here is an example of an animated heatmap, which allows you to visualize when instances are used over a period of eight weeks:

The latest ParkMyCloud update also includes:

  • CloudWatch data collection improvements to reduce the number of API calls required to pull instance utilization metrics data
  • Various user interface improvements to a number of screens in the ParkMyCloud console.

As noted in our last release, utilization data also provides the necessary information that will allow ParkMyCloud to make optimal parking and rightsizing recommendations (SmartParking) when this feature is released next month, part of our ongoing efforts to do what we do best – save you money, automatically.

AWS users who sign up now can take advantage of the latest release as we ramp up for automated SmartParking. In order to give you the most optimal cost control over your cloud bill, start your ParkMyCloud trial today to collect several weeks’ worth of CloudWatch data, track your usage patterns, and get recommendations as soon as the SmartParking feature becomes available in a few weeks.

If you are an existing customer, be sure to update your AWS policies to enable ParkMyCloud to access your AWS CloudWatch data. Detailed instructions can be found in our support portal.

Feedback? Anything else you’d like to see ParkMyCloud do? Let us know

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New in ParkMyCloud: AWS Utilization Metric Tracking

We are happy to share the latest release in ParkMyCloud: you can now see resource utilization data for your AWS EC2 instances! This data is viewable through customizable heatmaps.

This update gives you information about how your resources are being used – and it also provides the necessary information that will allow ParkMyCloud to make optimal parking and rightsizing recommendations when this feature is released next month. This is part of our ongoing efforts to do what we do best – save you money, automatically.

Utilization metrics that ParkMyCloud will now report on include:

  • Average CPU utilization
  • Peak CPU utilization
  • Total instance store read operations
  • Total instance store write operations
  • Average network data in
  • Average network data out
  • Average network packets in
  • Average network packets out

Here is an example of an instance utilization heatmap, which allows you to see when your instances are used most often:

In a few weeks, we will release the ability for ParkMyCloud to recommend parking schedules for your instances based on these metrics. In order to take advantage of this, you will need to have several weeks’ worth of CloudWatch data already logged, so that we can recommend based on your typical usage. Start your ParkMyCloud trial today to start tracking your usage patterns so you can get usage-based parking recommendations.

If you are an existing customer, you will need to update your AWS policies to enable ParkMyCloud to access your AWS CloudWatch data. Detailed instructions can be found in our support portal.

Feedback? Anything else you’d like to see ParkMyCloud do? Let us know!

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Cloud Per-Second Billing – How Much Does It Really Save?

It has been a little over a month since Amazon and Google switched some of their cloud services to per-second billing and so the first invoices with the revised billing are hitting your inboxes right about now. If you are not seeing the cost savings you hoped for, it may be a good time to look again at what services were slated for the pricing change, and how you are using them.

Google Cloud Platform

Starting with the easiest one, Google Cloud Platform (GCP), you may not be seeing a significant change, as most of their services were already billing at the per-minute level, and some were already at the per-second level. The services moved to per-second billing (with a one-minute minimum) included Compute Engine, Container Engine, Cloud Dataproc, and App Engine VMs.  Moving from per-minute billing to per-second billing is not likely to change a GCP service bill by more than a fraction of a percent.

Let’s consider the example of an organization that has ten GCP n1-standard-8 Compute Engine machines in Oregon at a base cost of $0.3800 per hour as of the date of this blog. Under per-minute billing, the worst-case scenario would be to shut a system down one second into the next minute, for a cost difference of about $0.0063. Even if each of the ten systems were assigned to the QA or development organization, and they were shut down at the end of every work day, say 22 days out of the month, your worst-case scenario would be an extra charge of 22 days x 10 systems x $0.0063 = $1.3860. Under per-second billing, the worst case is to shut down at the beginning of a second, with a highest possible cost for these same machines (sparing you the math) being about $0.02. So, the best this example organization can hope to save over a month with these machine with per-second billing is $1.39.

Amazon Web Services

On the Amazon Web Services (AWS) side of the fence, the change is both bigger and smaller.  It is bigger in that they took the leap from per-hour to per-second billing for On-Demand, Reserved, and Spot EC2 instances and provisioned EBS, but smaller in that it is only for Linux-based instances; Windows instances are still at per-hour.

Still, if you are running a lot of Linux instances, this change can be significant enough to notice.  Looking at the same example as before, let’s run the same calculation with the roughly equivalent t2.2xlarge instance type, charged at $0.3712 per hour. Under per-hour billing, the worst-case scenario is to shut a system down even a second into the next higher hour. In this example, the cost would be an extra charge of 22 days x 10 systems x $0.3712 = $81.664. Under per-second billing, the worst case is the same $0.02 as with GCP (with fractions of cents difference lost in the noise). So, under AWS, one can hope to see significantly different numbers in the bill.

The scenario above is equally relevant to other situations where instances get turned on and off on a frequent basis, driving those fractions of an hour or a minute of “lost” time. Another common example would be auto-scaling groups that dynamically resize based on load, and see enough change over time to bring instances in and out of the group. (Auto-scale groups are frequently used as a high-availability mechanism, so their elastic growth capabilities are not always used, and so savings will not always be seen.) Finally, Spot instances are built on the premise of bringing them up and down frequently, and they will also enjoy the shift to per-second billing.

However, as you look at your cloud service bill, do keep in mind some of the nuances that still apply:

  • Windows: GCP applies per-second billing to Windows; AWS is still on one-hour billing for Windows.
  • Marketplace Linux: Some Linux instances in the AWS Marketplace that have a separate hourly charge are also still on hourly billing (perhaps due to contracts or licensing arrangements with the vendors?), so you may want to reconsider which flavor of Linux you want to use.
  • Reserved instances: AWS does strive to “use up” all of the pre-purchased time for reserved instances, spreading it across multiple machines with fractions of usage time, and per-second billing can really stretch the value of these instances.
  • Minimum of one-minute charge: Both GCP and AWS will charge for at least a minute from instance start before per-second billing comes into play.

Overall, per-second billing is a great improvement for consumers of cloud resources…and will probably drive us all more than ever to make each second count.

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AWS IAM Roles and Ways to Use them to Improve Security

What are AWS IAM Roles?

Within AWS Identity and Access Management system (IAM) there are a number of different identity mechanisms that can be configured to secure your AWS environment, such as Users, Groups, and AWS IAM Roles. Users are clearly the humans in the picture, and Groups are collections of Users, but Roles can be a bit more obscure. Roles are defined as a set of permissions that grant access to actions and resources in AWS. Unlike Users, which are tied to a specific Identity and a specific AWS account, an IAM Role can be used by or assumed by IAM User accounts or by services within AWS, and can give access to Users from another account altogether.

To better understand Roles, I like the metaphor of a hat.  When we say a Role is assumed by a user – it is like saying someone can assume certain rights or privileges because of what hat they are wearing.  In any company (especially startups), we sometimes say someone “wears a lot of hats” – meaning that person temporarily takes on a number of different Roles, depending on what is needed. Mail delivery person, phone operator, IT support, code developer, appliance repairman…all in the space of a couple hours.

IAM Roles are similar to wearing different hats this in that they temporarily let an IAM User or a service get permissions to do things they would not normally get to do.  These permissions are attached to the Role itself, and are conveyed to anyone or anything that assumes the role.  Like Users, Roles have credentials that can be used to authenticate the Role identity.

Here are a couple ways in which you can use IAM Roles to improve your security:

EC2 Instances

All too often, we see software products that rely on credentials (username/password) for services or accounts that are either hard-coded into an application or written into some file on disk. Frequently the developer had no choice, as the system had to be able to automatically restart and reconnect if the machine rebooted, without anyone to manually type in credentials during the rebootwhen the system rebooted. If the code is examined, or file system is compromised, then the credentials are exposed, potentially compromisingand can potentially used to compromise other systems and services. In addition, such credentials make it really difficult to periodically change the password. Even in AWS we sometimes see developers hard-code API Key IDs and Keys into apps in order to get access to some AWS service. This is a security accident waiting to happen, and can be avoided through the use of IAM Roles.

With AWS, we can assign a single IAM Role to an EC2 instance. This assignment is usually made when the instance is launched, but can also be done at runtime if needed. Applications running on the server retrieve the Role’s security credentials by pulling them out of the instance metadata through a simple web command. These credentials have an additional advantage over potentially long-lived, hard-coded credentials, in that they are changed or rotated frequently, so even if somehow compromised, they can only be used for a brief period.

Another key security advantage of Roles is that they can be limited to just the access/rights privileges needed to get a specific job done. Amazon’s documentation for roles gives the example of an application that only needs to be able to read files out of S3. In this case, one can assign a Role that contains read-only permissions for a specific S3 bucket, and the Role’s configuration can say that the role can only be used by EC2 instances. This is an example of the security principle of “least privilege,”, where the minimum privileges necessary are assigned, limiting the risk of damage if the credential is compromised. In the same sense that you would not give all of your users “Administrator” privileges, you should not create a single “Allow Everything” Role that you assign everywhere. Instead create a different Role specific to the needs of each system or group of systems.

Delegation

Sometimes one company needs to give access to their resources to another company. Before IAM Roles, (and before AWS) the common ways to do that were to share account logins (with the same issues identified earlier with hardcoded credentials) or to use complicated PKI/certificate based systems. If both companies using AWS, sharing access is much easier with Role-based Delegation. There are several ways to configure IAM Roles for delegation, but for now we will just focus on delegation between accounts from two different organizations.

At ParkMyCloud, our customers use Delegation to let us read the state of their EC2, RDS, and scaling group instances, and then start and stop them per the schedules they configure in our management console.

To configure Role Delegation, a customer first creates an account with the service provider, and is given the provider’s AWS Account ID and an External ID. The External ID is a unique number for each customer generated by the service provider.

The administrator of the customer environment creates an IAM Policy with a constrained set of access (principle of “least privilege” again), and then assigns that policy to a new Role (like “ParkMyCloudAccess”), specifically assigned to the provider’s Account ID and External ID.  When done, the resulting IAM Role is given a specific Amazon Resource Name (ARN), which is a unique string that identifies the role.  The customer then enters that role in the service provider’s management console, which is then able to assume the role.  Like the EC2 example, when the ParkMyCloud service needs to start a customer EC2 instance, it calls the AssumeRole API, which verifies our service is properly authenticated, and returns temporary security credentials needed to manage the customer environment.

Conclusions

AWS IAM Roles make some tasks a lot simpler by flexibly assigning roles to instances and other accounts. IAM Roles can help make your environment more secure by:

  • Using the principle of Least Privilege in IAM policies to isolate the systems and services to only those needed to do a specific job.
  • Prevent hard coding of credentials in code or files, minimizing danger from exposure, and removing the risk of long-unchanged passwords.
  • Minimizing common accounts and passwords by allowing controlled cross-account access.
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AWS Lambda + ParkMyCloud = Supercharged Automation

Among the variety of AWS services and functionality, AWS Lambda seems to be taking off with hackers and tinkerers. The idea of “serverless” architecture is quite a shift in the way we think about applications, tools, and services, but it’s a shift that is opening up some new ideas and approaches to problem solving.  

If you haven’t had a chance to check out Lambda, it’s a “function-as-a-service” platform that allows you to run scripts or code on demand, without having to set up servers with the proper packages and environments installed. Your lambda function can trigger from a variety of sources and events, such as HTTP requests, API calls, S3 bucket changes, and more. The function can scale up automatically, so more compute resources will be used if necessary without any human intervention. The code can be written in Node.js, Python, Java, and C#.

Some pretty cool ideas already exist for lambda functions to automate processes.  One example from AWS is to respond to a Github event to trigger an action, such as the next step in a build process.  There’s also a guide on how to use React and Lambda to make an interactive website that has no server.

For those of you who are already using ParkMyCloud to schedule resources, you may be looking to plug in to your CI/CD pipeline to achieve Continuous Cost Control.  I’ve come up with a few ideas of how to use Lambda along with ParkMyCloud to supercharge your AWS cloud savings.  Let’s take a look at a few options:

Make ParkMyCloud API calls from Lambda

With ParkMyCloud’s API available to control your schedules programmatically, you could make calls to ParkMyCloud from Lambda based on events that occur.  The API allows you to do things like list resources and schedules, assign schedules to resources, snooze schedules to temporarily override them, or cancel a snooze or schedule.

For instance, if a user logs in remotely to the VPN, it could trigger a Lambda call to snooze the schedules for that user’s instances.  Alternatively, a Lambda function could change the schedules of your Auto Scaling Group based on average requests to your website.  If you store data in S3 for batch processing, a trigger from an S3 bucket can tell Lambda to notify ParkMyCloud that the batch is ready and the processing servers need to come online.

Send notifications from ParkMyCloud to Lambda

With ParkMyCloud’s notification system, you can send events that occur in the ParkMyCloud system to a webhook or email.  The events can be actions taken by schedules that are applied to resources, user actions that are done in the UI, team and schedule assignments from policies, or errors that occur during parking.

By sending schedule events, you could use a Lambda function to tell your monitoring tool when servers are being shut down from schedules.  This could also be a method for letting your build server know that the build environment has fully started before the rest of your CI/CD tools take over.  You could also send user events to Lambda to feed into a log tool like Splunk or Logstash.  Policy events can be sent to Lambda to trigger an update to your CMDB with information on the team and schedule that’s applied to a new server.

Think outside the box!

Are you already using AWS Lambda to kick off functions and run scripts in your environment?  Try combining Lambda with ParkMyCloud and let us know what cool tricks you come up with for supercharging your automation and saving on your cloud bill! Stop by Booth 1402 at AWS re:Invent this year and tell us.

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5 Favorite AWS Training Resources

When it comes to AWS training resources, there’s no shortage of information out there. Considering the wide range of videos, tutorials, blogs, and more, it’s hard knowing where to look or how to begin. Finding the best resource depends on your learning style, your needs for AWS, and getting the most updated information available. With this in mind, we came up with our 5 favorite AWS training resources, sure to give you the tools you need to learn AWS:

1. AWS Self-Paced Labs

What better way to learn that at your own pace? AWS self-paced labs give you hands-on learning in a live AWS environment, with AWS services, and actual scenarios you would encounter in the cloud. Among the recommended labs you’ll find an Introduction to Amazon Elastic Compute Cloud (EC2), and for more advanced users, a lab on Creating Amazon EC2 Instances with Microsoft Windows. If you’re up for an adventure, enroll in a learning quest and immerse yourself in a collection of labs that will help you master any AWS scenario at your own pace. Once completed, you will earn a badge that you can boast on your resume, LinkedIn, website, etc.  

2. AWS Free Tier

Sometimes the best way to learn something is by jumping right in. With the AWS Free Tier, you can try AWS services for free. This is a great way to test out AWS for your business, or for the developers out there, to try services like AWS CodePipeLine, AWS Data Pipeline, and more. While you are still getting a hands-on opportunity to learn a number of AWS services, the only downside is that there are certain usage limits. You can track your usage with a billing alarm to avoid unwanted charges, or you can try ParkMyCloud and park your instances when they’re not in use to get the most out of your free tier experience. In fact, ParkMyCloud started its journey by using AWS’ free tier – we eat our own dog food!

3. AWS Documentation

AWS Documentation is like a virtual encyclopedia of tools, terms, training, and everything AWS. You’ll find white papers, case studies, tutorials, cloud computing basics, and so much more. This resource is a one-stop-shop for all of your AWS documentation needs, whether you’re a beginner or advanced user. No matter where you are in your AWS training journey, AWS documentation is always a useful reference and certainly deserves a spot in your bookmarks.

4. YouTube

So far, we’ve gone straight to the source for 3 out of 5 of our favorite AWS training resources. Amazon really does a great job of providing hands-on training, tutorials, and documentation for users with a range of experience. However, YouTube opens up a whole new world of video training that includes contributions from not only Amazon, but other great resources as well. Besides the obvious Amazon Web Services channel, there are also popular and highly rated videos by Edureka, Simplilearn, Eli the Computer Guy, and more.

5. Bloggers

As cloud technology usage continues to expand and evolve, blogs are a great way to stay up to speed with AWS and the world of cloud computing. Of course, in addition to labs, a free-trial, extensive documentation, and their own YouTube channel, AWS also has their own blog. Since AWS actually has a number of blogs that vary by region and technology, we recommend that you start by following Jeff Barr – Chief Evangelist at Amazon Web Services, and primary contributor. Edureka was mentioned in our recommended YouTube channels, they also have a blog that covers plenty of AWS topics. In addition, the CloudThat blog is an excellent resource for AWS and all things cloud, and was co-founded by Bhaves Goswami – a former member of the AWS product development team.

 


There’s plenty of information out there when it comes to AWS training resources. We picked our 5 favorite resources for their reliability, quality, and range of information. Whether you’re new to AWS or consider yourself an expert, these resources are sure to help you find what you’re looking for.

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3 Enterprise Cloud Management Challenges You Should Be Thinking About

Enterprise cloud management is a top priority. As the shift towards multi-cloud environments continues, so has the need to consider the potential challenges. Whether you already use the public cloud, or are considering making the switch, you probably want to know what the risks are. Here are three you should be thinking about.

1. Multi-Cloud Environments

As the ParkMyCloud platform supports AWS, Azure, and Google, we’ve noticed that multi-cloud strategies are becoming increasingly common among enterprises. There are a number of reasons why it would be beneficial to utilize more than one cloud provider. We have discussed risk mitigation as a common reason, along with price protection and workload optimization. As multi-cloud strategies become more popular, the advantages are clear. However, every strategy comes with its challenges, and it’s important for CIOs to be aware of the associated risks.

Without the use of cloud management tools, multi-cloud management is complex and sometimes difficult to navigate. Different cloud providers have different price models, product features, APIs, and terminology. Compliance requirements are also a factor that must be considered when dealing with multiple providers. Meeting and maintaining requirements for one cloud provider is complicated enough, let alone multiple. And don’t forget you need a single pane to view your multi-cloud infrastructure.

2. Cost Control

Cost control is a first priority among cloud computing trends. Enterprise Management Associates (EMA) conducted a research study and identified key reasons why there is a need for cloud cost control, among them were inefficient use of cloud resources, unpredictable billing, and contractual obligation or technological dependency.

Managing your cloud environment and controlling costs requires a great deal of time and strategy, taking away from the initiatives your enterprise really needs to be focusing on. The good news is that we offer a solution to cost control that will save 65% or more on your monthly cloud bills – just by simply parking your idle cloud resources. ParkMyCloud was one of the top three vendors recommended by EMA as a Rapid ROI Utility. If you’re interested in seeing why, we offer a 14-day free trial.

3. Security & Governance

In discussing a multi-cloud strategy and its challenges, the bigger picture also includes security and governance. As we have mentioned, a multi-cloud environment is complex, complicated, and requires native or 3rd party tools to maintain vigilance. Aside from legal compliance based on the industry your company is in, the cloud also comes with standard security issues and of course the possibility of cloud breaches. In this vein, as we talk to customers they often worry about too many users being granted console access to create and terminate cloud resources which can lead to waste. A key here is limiting user access based on roles or Role-based Access Controls (RBAC). At ParkMyCloud we recognize that visibility and control is important in today’s complex cloud world. That’s why in designing our platform, we provide the sysadmin the ability to delegate access based on a user’s role and the ability to authenticate leveraging SSO using SAML integration . This approach brings security benefits without losing the appeal of a multi-cloud strategy.

Our Solution

Enterprise cloud management is an inevitable priority as the shift towards a multi-cloud environment continues. Multiple cloud services add complexity to the challenges of IT and cloud management. Cost control is time consuming and needs to be automated and monitored constantly. Security and governance is a must and it’s necessary to ensure that users and resources are optimally governed. As the need for cloud management continues to grow, cloud automation tools like ParkMyCloud provide a means to effectively manage cloud resources, minimize challenges, and save you money.

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How to Get the Cheapest Cloud Computing

Are you looking for the cheapest cloud computing available? Depending on your current situation, there are a few ways you might find the least expensive cloud offering that fits your needs.

If you don’t currently use the public cloud, or if you’re willing to have infrastructure in multiple clouds, you’re probably looking for the cheapest cloud provider. If you have existing infrastructure, there are a few approaches you can take to minimize costs and ensure they don’t spiral out of control.

Find the Cloud Provider that Offers the Cheapest Cloud Computing

There are a variety of small cloud providers that attempt to compete by dropping their prices. If you work for a small business and prefer a no-frills experience, perhaps one of these is right for you.

However, there’s a reason that the “big three” cloud providers – Amazon Web Services (AWS), Microsoft Azure, and Google Cloud – dominate the market. They offer a wide range of product lines, and are continually innovating. They have a low frequency of outages, and their scale requires a straightforward onboarding process and plenty of documentation.

Whatever provider you decide on, ensure that you’ll have access to all the services you need – is there a computing product, storage, databases? How good is the customer support?

For more information about the three major providers’ pricing, please see this whitepaper on AWS vs. Google Cloud Pricing and this article comparing AWS vs. Azure pricing.

Locked In? How to Get the Cheapest Cloud Computing from Your Current Provider

Of course, if your organization is already locked into a cloud computing provider, comparing providers won’t do you much good. Here’s a short checklist of things you should do to ensure you’re getting the cheapest cloud computing possible from your current provider:

  • Use Reserved Instances for production – Reserved instances can save money – as long as you use them the right way. More here. (This article is about AWS RIs, but similar principles apply to Azure’s RIs and Google’s Committed Use discounts.)
  • Only pay for what you actually need – there are a few common ways that users inadvertently waste money, such as using larger instances than they need, and running development/testing instances 24/7 rather than only when they’re needed. (Here at ParkMyCloud, we’re all about reducing this waste – try it out.)
  • Ask – it never hurts to contact your provider and ask if there’s anything you could be doing to get a cheaper price. If you use Microsoft Azure, you may want to sign up for an Enterprise License Agreement. Or maybe you qualify for AWS startup credits.

Get Credit for Your Efforts

While finding the cheapest cloud computing is, of course, beneficial to your organization’s common good, there’s no need to let your work in spending reduction go unnoticed. Make sure that you track your organization’s spending and show your team where you are reducing spend.

We’ve recently made this task easier than ever for ParkMyCloud users. Now, you can not only create and customize reports of your cloud spending and savings, but you can also schedule these reports to be emailed out. Users are already putting this to work by having savings reports automatically emailed to their bosses and department heads, to ensure that leadership is aware of the cost savings gained… and so users can get credit for their efforts.

 

 

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Reduce RDS Costs with ParkMyCloud

Thanks to the ability to shut down instances with a start/stop scheduler, users of Amazon’s database service can finally save time and reduce RDS costs. Until June 2017, the only way to accomplish this feat was by copying and deleting instances, running the risk of losing transaction logs and automatic backups. While Amazon’s development of the start/stop scheduler is useful and provides a level of cost savings, it also comes with issues of its own.

For one, the start/stop scheduler is not foolproof. The process for stopping and starting non-production RDS instances is manual, relying on the user to create and consistently manage the schedule. Having to manually switch off when instances are not in use, and then restart when access is needed again, is a helpful advantage but also leaves room for human error. Complicating things further, RDS instances that have been shut down will automatically be restarted after seven days, again relying on the user to switch those instances back off if they’re not needed at the time.

Why Scripting is not the Best Answer

One way of minimizing potential for error is by automating the stop/start schedule yourself with writing your own scripts. While that could work, you would need to consider the number of non-production instances deployed on AWS RDS, and plan for a schedule that would allow developers to have access when needed, which could very well be at varying times throughout the day. All factors considered, the process of writing and maintaining scheduling scripts is one that takes extra time and costs money as well. Ultimately, setting up and maintaining your own schedule could increase your cloud spend more than it reduces RDS costs.

When you start thinking about the cost of paying developers, the amount of scripts that would have to be written, and the ongoing maintenance required, buying into an automated scheduling process is a no-brainer.

How ParkMyCloud Reduces RDS Costs

Automated Scheduling

ParkMyCloud saves you time and money by automating the scheduling process of stopping and starting AWS RDS instances (in addition to Microsoft Azure VMs and Google Cloud Compute instances, but that’s another post). At the same time, you get total visibility and full autonomy over your account.

The process is simple. With you as the account manager, ParkMyCloud conducts a discovery of all the company accounts, and determines which instances are most suitable for parking. From there, you have the option of implementing company-wide schedules for non-production instances, or giving each development team the ability to create schedules of their own.

Flexible Parking

ParkMyCloud takes saving on RDS costs to a whole new level with parking schedules. Different schedules can be applied to different instances, or they can be parked permanently and put on “snooze” when access is needed. Amazon’s seven-day automatic restart of switched off instances is a non-issue with our platform, and snoozed instances can be re-parked when access is no longer needed, so there’s no more relying on the user to do it manually.

For the most part, we find that companies will want to park their non-production instances outside the normal working hours of Monday to Friday, let’s say from 8:00am to 8:00pm. By parking your instances outside of those days and hours, ParkMyCloud can reduce your cloud spend by 65% – even more if you implement a parking schedule and use the snooze option.

Valuable Insight

Because you have total visibility over the account, you can reduce RDS costs even further by having a bird’s eye view of your company’s cloud use. You’ll be able to tell which of your instances are underused, terminate them, and possibly benefit further from selecting a cheaper plan (really soon). You’ll be able to see all RDS instances across all regions and AWS accounts in one simple view. You can also view the parking schedules for each instance and see how much each schedule is saving, potentially reducing costs even further. The viewability of your account and access to information provides a great resource for budgeting and planning.

Conclusion

The AWS start/stop scheduler is useful, but has to be done manually. Writing your own scripts sounds helpful, but it’s actually time consuming, and not fully cost-effective. ParkMyCloud automates the process while still putting you in control, reducing RDS costs and saving you time and money.

See the benefits of ParkMyCloud for yourself by taking advantage of our two-week free trial. Test our cloud cost control platform in your own environment, without any need for a credit card or signed contract, and see why our simple, cost-effective tool is the key to reducing RDS costs. We offer a variety of competitive pricing plans to choose from, or a limited-function version that you can continue to use for free after the trial ends.

To start your free trial today, sign up here.

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Cloud Optimization Tools = Cloud Cost Control (Part II)

A couple of weeks ago in Part 1 of this blog topic we discussed the need for cloud optimization tools to help enterprises with the problem of cloud cost control. Amazon Web Services (AWS) even goes as far as suggesting the following simple steps to control their costs (which can also be applied  to Microsoft Azure and Google Cloud Platform, but of course with slightly different terminology):

    1. Right-size your services to meet capacity needs at the lowest cost;
    2. Save money when you reserve;
    3. Use the spot market;
    4. Monitor and track service usage;
    5. Use Cost Explorer to optimize savings; and
    6. Turn off idle instances (we added this one).

A variety of third-party tools and services have popped up in the market over the past few years to help with cloud cost optimization – why? Because upwards of $23B was spent on public cloud infrastructure in 2016, and spending continues to grow at a rate of 40% per year. Furthermore, depending on who you talk to, roughly 25% of public cloud spend is wasted or not optimized — that’s a huge market! If left unchecked, this waste problem is supposed to triple to over $20B by 2020 – enter the vultures (full disclosure, we are also a vulture, but the nice kind). Most of these tools are lumped under the Cloud Management category, which includes subcategories like Cost Visibility and Governance, Cost Optimization, and Cost Control vendors – we are a cost control vendor to be sure.

Why do you, an enterprise, care? Because there are very unique and subtle differences between the tools that fit into these categories, so your use case should dictate where you go for what – and that’s what I am trying to help you with. So, why am I a credible source to write about this (and not just because ParkMyCloud is the best thing since sliced bread)?

Well, yesterday we had a demo with a FinTech company in California that was interested in Cost Control, or thought they were. It turns out that what they were actually interested in was Cost Visibility and Reporting; the folks we talked to were in Engineering Finance, so their concerns were primarily with billing metrics, business unit chargeback for cloud usage, RI management, and dials and widgets to view all stuff AWS and GCP billing related. Instead of trying to force a square peg into a round hole, we passed them on to a company in this space who’s better suited to solve their immediate needs. In response, the Finance folks are going to put us in touch with the FinTech Cloud Ops folks who care about automating their cloud cost control as part of their DevOps processes.

This type of situation happens more often than not. We have a lot of enterprise customers using ParkMyCloud along with CloudHealth, CloudChekr, Cloudability, and Cloudyn because in general, they provide Cost Visibility and Governance, and we provide actionable, automated Cost Control.

As this is our blog, and my view from the street – we have 200+ customers now using ParkMyCloud, and we demo to 5-10 enterprises per week. Based on a couple of generic customer uses cases where we have strong familiarity, here’s what you need to know to stay ahead of the game:

  • Cost Visibility and Governance: CloudHealth, CloudChekr, Cloudability and Cloudyn (now owned by Microsoft)
  • Reserved Instance (RI) management – all of the above
  • Spot Instance management – SpotInst
  • Monitor and Track Usage: CloudHealth, CloudChekr, Cloudability and Cloudyn
  • Turn off (park) Idle Resources – ParkMyCloud, Skeddly, Gorilla Stack, BotMetric
  • Automate Cost Control as part of your DevOps Process: ParkMyCloud
  • Govern User Access to Cloud Console for Start/Stop: ParkMyCloud
  • Integrate with Single Sign-On (SSO) for Federated User Access: ParkMyCloud

To summarize, cloud cost control is important, and there are many cloud optimization tools available to assist with visibility, governance, management, and control of your single or multi-cloud environments. However, there are very few tools which allow you to set up automated actions leveraging your existing enterprise tools like Ping, Okta, Atlassian, Jenkins, and Slack.  Make sure you are not only focusing on cost visibility and recommendations, but also on action-oriented platforms to really get the best bang for your buck.

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