Can Custom Machine Types Improve Your Life?

Can Custom Machine Types Improve Your Life?

Today we’re going to look at an interesting trend we are seeing toward the use of custom machine types in Google Cloud Platform. One of the interesting byproducts of managing the ParkMyCloud platform is that we get to see changes and trends in cloud usage in real time. Since we’re directly at the customer level, we can often see these changes before they are spotted by official cloud industry commentators. They start off as small signals in the noise, but practice has allowed us to see when something is shifting and a trend is emerging – as is the case with these custom machine types.

Over the last year, the shift to greater use of Custom Machine Types (launched in 2016 on Google Compute Engine) and to a lesser extent Optimized EC2 instances (launched in 2018 on AWS) are just such a signal that we have observed growing in strength. Interestingly, Microsoft has yet to offer their equivalent version on Azure.

What do GCE custom machine types let you do?

Custom machine types let you build a bespoke instance to match the specific needs of your workload. Many workloads can be matched to off-the-shelf instance types, but there are many workloads for which it is now possible to build a better matching machine which delivers a more cost effective price. The growth in adoption of this particular instance type supports the case and likely benefits of their availability.

So what are the benefits of these new customized machines? First, they provide a granular level of control to match the needs of your specific application workloads. In practice, this leads to compromise as you select the closest instance type to your optimal configuration. Such compromises typically lead to over-provisioning, a situation we see across the board among our customer base. We analyzed usage of the instances in our platform this summer, and found that across all the instances in our platform, the average vCPU utilization was less than 10%!

Secondly, they allow you to finely tune your machine to maximize the cost effectiveness of your infrastructure. Google claims savings of up to 50% when utilizing their customized options compared to traditional predefined instances, which we believe to be a reasonable assessment as we see the standard instance types are often massively overprovisioned.

On GCE, the variables that you can configure include:

  • Quantity and type of vCPU’s;
  • Quantity and type of GPU;
  • Memory size (albeit there are some limits on the maximum per vCPU).

Sustained Use Discounts and Preemptible VM Discounts are also available for these customized instances on GCE which also make this an attractive option.

On AWS, customized options are currently more limited and include only the number and type of vCPU’s, and the options are focused on per-core licensed software problems, rather than cost optimization. It will be interesting if they follow Google and open up cost-based customization options in the coming months, and allow the effective unbundling of fixed off-the-shelf instance types.

Should you use custom machine types?

So just because customization is an option, is this something you should actually pursue? In fact, you will pay a small premium compared to the size of standard instances/VMs, albeit you can optimize for specific workloads, which oftentimes will mean an overall lower cost. To make such an assessment will require that you examine your applications resource use and performance requirements. Such determinations require that you carefully analyze your infrastructure utilization data. This quickly gets complex, although there are a number of platforms which can support thorough analytics and data visualization. Ideally such analytics would be combined with the ability to recommend specific cost-effective customized instance configurations as well as automate their provisioning.

Watch this space for more news on custom machine types!

How to Automate and Secure Your Environment with AWS Server Fleet Management

How to Automate and Secure Your Environment with AWS Server Fleet Management

AWS recently announced a combination of AWS Systems Manager and Amazon Inspector into a new offering called AWS Server Fleet Management. The goal of this service is to provide a way to secure, automate, and configure a large array of servers through multiple AWS services all working together. Some enterprises already have a config management tool in place, but might be looking for a more AWS-centric way to manage their numerous EC2 servers. Let’s look at how Server Fleet Management works, how it stacks up against other config management tools, and some of the pros and cons of using this solution.

How It Works

AWS Server Fleet Management utilizes quite a few AWS services under the hood.  The good news is that you don’t have to deploy these services manually, as there’s a Cloudformation template available that will build the entire stack for you. The services include:

  • Amazon Cloudwatch – for kicking off events to trigger other services
  • Amazon Inspector – manages the assessment rules for configuration and security
  • Amazon SNS – message queue for tracking instance IDs and email addresses
  • Amazon Lambda – various tasks, including querying Inspector and updating Systems Manager
  • AWS Systems Manager – tracks inventory and configuration for EC2 instances and manages OS patches
  • Amazon S3 – secure storage of artifacts

Before deploying the Cloudformation stack, you’ll need to enter a few configuration details. The main configuration detail is the “Managed Instances Tag Value”, which is the tag on your EC2 servers that you’ll place if you want them managed via Server Fleet Management. This can work in conjunction with the “Patch Group” tag in AWS Systems Manager if you want the instance to be automatically patched. Once you specify the tag, an email address, and whether you want a sample fleet to be deployed, you’re ready to create the stack!

Comparison to other tools

In the config management world, there are a few major players, including Chef, Puppet, Ansible, and SaltStack. From a purely configuration perspective, Server Fleet Management doesn’t offer anything new. However, if you’re fully bought-in to running everything within AWS, the flexibility of using Lambda functions in addition to other AWS services can be a huge advantage. On the flip side of that, enterprises that are multi-cloud may want to keep using a cloud-agnostic tool.

Pros and Cons

Along with the possible benefit of being purely within the AWS ecosystem, another major pro of AWS Server Fleet Management is the combination of security enforcement and patch management. Solving both of those problems often requires multiple tools, so this can trim down your list of applications. This solution also has lots of opportunities to tie into other existing AWS solutions or to be customized to fit your use cases.

The expandability can also be considered a con, as the built-in uses are fairly specific and require more customization for larger fleets. Some things that aren’t included are topics like cost management (we’ve got you covered), non-EC2 services that need security audits, application grouping, and cross-account access. There also aren’t any built-in hooks to existing config management tools that are likely already in use.

Automated Security and Patching

All in all, AWS Server Fleet Management is worth looking into if you’ve got a large EC2 deployment. Even if you don’t use the pre-made stack, it might give you some ideas on how to use the underlying AWS services to help secure and manage your fleet. With the included sample fleet, it’s easy to get it set up and try it out!

5 Free Google Cloud Training Resources

5 Free Google Cloud Training Resources

If you’re looking to break into the cloud computing space, there’s an abundance of resources out there, including free Google Cloud training. If you know where to look, open source learning is a great way to get familiar with different cloud service providers. Combined with our previous blog posts on free training resources for AWS and Azure, you’ll be well on your way to expanding your cloud expertise and finding your own niche. No matter where you are in the learning process, there’s a training resource for every experience level and learning type – get started now with our list of 5 free Google Cloud training resources:  

1. Google Cloud Free Tier

For free, hands-on training there’s no better place to start than with Google Cloud Platform itself. GCP’s free tier option is a no-brainer thanks to its offerings:

  • Access to all GCP products. You’ll have everything you need to experiment with building and running apps, sites, and services. Firebase and the Google Maps API are included.
  • $300 credit is yours to spend for the next 12 months, an expansion from their previous 60-day period and a sizable offer in comparison to Azure’s $200 for 30 days, so take advantage.
  • No autocharges after the trial period ends – a rarity for most free trials, and a guarantee that this training resource is 100% free.
  • An always-free option. GCP’s free tier takes the cake with this an always-free tier that gives you enough power to run a small app despite limitations on product and usage, a perfect option for learning purposes.

And for help with navigating the platform as you use it, check out GCP’s documentation for a full overview, comparisons, tutorials, and more.

2. Coursera

On the Google Cloud training page, you’ll find plenty of classes to get technical skills and learn best practices for using the platform. Among those options, they have also teamed up with Coursera, an online learning platform founded by Stanford professors, to offer courses online so you can “skill up from anywhere.”

Coursera includes a number of free courses, and until 1/1/19, you can sign up and get your first month free on any select Google Cloud Specialization. Courses include topics in Machine Learning, Architecting, Data Engineering, Developing Applications, and the list goes on.  

3. Qwiklabs

In conjunction with Coursera, Google Cloud offers hands-on training with specialized labs available via Qwiklabs, a learning lab environment for developers. Choose a “quest” from their catalog to get started with 50+ hands-on labs from beginner to expert level, where you’ll learn new skills in a GCP environment and earn cloud badges along the way. Get started with GCP Essentials and work your way into more advanced, niche topics like Managing Cloud Infrastructure with Terraform, Machine Learning APIs, IoT in Google Cloud, and so on.

4. YouTube

You can’t go wrong with YouTube. An endless amount of free videos offers an abundance of Google Cloud training for those of you who prefer to watch the movie instead of reading the book (you know who you are). Some of the most popular YouTube channels for free Google Cloud training include:

  • Google Cloud Platform (243k subscribers) – “helping you build what’s next with secure infrastructure, developer tools, APIs, data analytics and machine learning.”
  • Simplilearn (164k subscribers) – one of the world’s leading certification training providers, with online training that includes Machine Learning, AWS, DevOps, Big Data, and Google Cloud Platform, among others. The course on Introduction To Google Cloud Platform Fundamentals Certification is a popular one with upwards of 99k views.
  • Edureka (537k subscribers) is a full-service, online learning platform with curated content in Big Data and Hadoop, DevOps, Blockchain, AI, Data Science, AWS, Google Cloud, and more. Their YouTube channel is a “gateway to high-quality videos, webinars, sample classes and lectures from industry practitioners and influencers.” If you’re jumping into GCP with no prior knowledge or experience, the What is Google Cloud Platform tutorial will get you started.

5. Blogs & Forums

While other resources keep you learning with hands-on training, tutorials, and certification prep, blogs keep your mind flowing with new insights, ideas, and the latest on all things cloud computing. Google Cloud and Qwiklab have blogs of their own, perfect for supplemented reading with their trainings. But for a more well-rounded blog with content on other service providers, check out Cloud Academy. We also cover Google Cloud on the ParkMyCloud blog – check out this guide to Google Cloud machine types, an explanation of sustained use discounts, and introduction to resource-based pricing. And be sure to subscribe to relevant discussion forums such as r/googlecloud on Reddit and the GCP Slack.

Take Advantage of These Free Google Cloud Training Resources

As it becomes clear that cloud computing is here to stay, free training resources only continue to emerge. We picked the 5 above for their reliability, variety, quality, and range of information. Whether you’re new to Google Cloud or consider yourself an expert, these resources will expand your knowledge and keep you up to date with what’s latest in the platform.

More Free Training Resources:

The Speed of Cloud Management Acquisitions Tells Us 3 Things About the Cloud Market

The Speed of Cloud Management Acquisitions Tells Us 3 Things About the Cloud Market

There has been a rush of cloud management acquisitions lately, with VMware, Apptio, and Flexera making major acquisitions in the last three months alone (and more to follow). I thought it would be useful to compile a centralized list, so we can take a look at the trends in this market and why these acquisitions are accelerating.

The Multi-Faceted Cloud Management Industry

First, let’s be clear: the cloud management industry is broad and a bit ambiguous but as it matures industry analysts have begun to define specific categories. We found the below put together by Gartner in a recent blog:

ParkMyCloud fits into the “Cost Management and Resource Optimization” category, which in and of itself is broad, but in a nutshell these vendors help enterprises monitor, manage, govern and control cloud spend in a variety of ways. The other category we find intriguing is “Provisioning and Orchestration”. That’s where we feel a lot of the DevOps tools fit, and that is the go-to-market model we like to fashion ourselves after — technical user/buyer, self-service trials, SaaS, and freemium model.

Cloud Management Acquisitions, 2013-2018

So it should be no surprise that we have collected the following data points listed below – we would welcome your feedback on others we should add to this list.

 

 

Company Founded Year Category Raised Acquirer Acquisition Price
FittedCloud 2015 2018 CMP Apptio
Rightscale 2006 2018 CMP $62.1MM Flexera
Cloud Ranger 2014 2018 CMP $1.1MM Druva
OpsGenie 2012 2018 DevOps $10MM Atlassian $295MM
CloudHeath Technologies 2012 2018 CMP $88MM VMware $500MM
Relus Cloud 2013 2018 MSP Cloud Reach
VictorOps 2012 2018 DevOps $34MM Splunk $120MM
Codeship 2011 2018 DevOps $11.4MM CloudBees tbd
N2WS 2012 2018 CMP ~$10MM Veeam $42.5MM
cmpute.io 2012 2017 CMP Cisco N/A
Botmetric 2014 2018 CMP $2MM Nutanix $50MM
Cloud Technology Partners 2009 2017 MSP $34MM HPE N/A
Cloudyn 2012 2017 CMP $20.5MM Microsoft ~$50MM-$70MM
CloudMgr 2011 2017 CMP $1.45MM Cloudability N/A
Cloudcruiser 2010 2017 CMP $19.8MM HPE N/A
Cloudamize 2012 2017 CMP $2MM Cloudreach N/A
Cliqr 2010 2016 CMP $38.4MM Cisco $260MM
ITapp 2012 2016 CMP ServiceNow N/A
Gravitant 2004 2015 CMP $40.3MM IBM N/A
ClusterK 2013 2015 CMP Seed Amazon $20MM-$50MM
Servicemesh 2008 2013 MSP $15MM CSC $295MM

In the last 45 days or so the cloud management platform (CMP) space has been hyperactive as VMware acquired CloudHealth, Apptio acquired FittedCloud, and Flexera acquired Rightscale. Good news for all but we are most excited for CloudHealth given we are a commercial and technology partner with them.

What These Cloud Management Acquisitions Tell Us about The State of Public Cloud

So what does this tell us about the cloud management space, and in particular the cost management and optimization space? We have some opinions:

  1. Multi-cloud is truly here. The benefit of these cloud management tools is that they are agnostic and can help enterprises manage and optimize AWS, Azure and Google services alike.
  2. Companies like Cisco, HPE and VMware understand the importance of being in the public cloud game, each basically failed at competing against AWS et. al. head on, so they are now ensuring they have tools that help enterprises manage public, private, hybrid and multi-cloud services.
  3. The cost management portion of cloud management is always a “top 3” concern of CIOs and CTOs according to any cloud survey published, so cloud cost optimization is front in center in enterprise IT and ISVs must be able to address this concern.

Clearly, cloud management acquisitions will continue, and new solutions and companies will evolve as this market grows and matures. The cloud providers are launching new services at a rapid pace, and like any large scale utility there needs to be tools to help manage, govern, secure, and optimize these existing and new services.

Microsoft Azure VM Types Comparison

Microsoft Azure VM Types Comparison

Microsoft Azure VM types come in a wide range optimized to meet various needs. Machine types are specialized, and vary by virtual CPU (vCPU), disk capability, and memory size, offering a number of options to match any workload.

With so many options available, finding the right machine type for your workload becomes confusing – which is why we’ve created this overview of Azure VM types (as we did before with EC2 instance types, and Google Cloud machine types). Note that while AWS EC2 instance types have names associated with their purpose, Azure instance type names are simply in a series from A to N.The chart below and written descriptions are a brief and easy reference, but remember that finding the right machine type for your workload will always depend on your needs.

General Purpose

General purpose VMs are suitable for balanced CPU and memory, making them a great option for testing and development, smaller to medium databases, and web servers with lower traffic:

DC-series

The latest family of virtual machines stand out for data protection and code confidentiality. SGX technology and a 3.7GHz Intel XEON E-2176G Processor back these machines, and in conjunction with Intel Turbo Boost Technology, they can go up to 4.7 GHz.

Av2 Series

A-series VMs have a CPU-to-memory ratio that works best for entry level workloads, like those for development and testing. Sizing is throttled for consistent processor performance to run the instance.

Dv2-series

Dv2 VMs boast powerful CPUs – roughly 35% faster than D-series VMs – and optimized memory, great for for production workloads. With the same memory and disk configurations as the D-series, based upon either a 2.4 GHz or 2.3 GHz processor and Intel Boost Technology, they can go to up to 3.1 GHz.

Dv3-series

With expanded memory and adjustments for disk and network limits, the Dv3 series Azure VM type offers the most value to general purpose workloads. Best for enterprise applications, relational databases, in-memory caching, and analytics.

B-series

Similar to the AWS t-series machine type family, B-series VMs are burstable and ideal for workloads that do not rely on full and continuous CPU performance. Customers can purchase a VM size that builds up credits when underutilized, and the accumulated credits can be used as bursts – spikes in compute power that allow for higher CPU performance when needed. Use cases for B-series VM types include development and testing, low-traffic web servers, small databases, micro services, and more.

Dsv3-series

With premium storage and a 2.4 or 2.3 GHz Intel Xeon processor that can achieve 3.5 GHz thanks to Intel Turbo Boost Technology 2.0, the Dsv3-series is best suited for most production workloads.  

Compute Optimized

Compute optimized Azure VM types offer a high CPU-to-memory ratio. They’re suitable for medium traffic web servers, network appliances, batch processing, and application servers.

Fsv2-series

With a base core frequency of 2.7 GHz and a maximum single-core turbo frequency of 3.7 GHz, Fsv2 series VM types offer up to twice the performance boost for vector processing workloads. Not only do they offer great speed for any workload, the Fsv2 also offers the best value for its price based on the ratio of Azure Compute Unit (ACU) per vCPU.

F-series

F-series Azure VM types are great for workloads that require speed thanks to the 2.4 GHz Intel Xeon processor, reaching speeds up to 3.1 GHz with the Intel Turbo Boost Technology 2.0. The F-series is your best bet for fast CPUs but not so much when it comes to memory or temporary storage per vCPU. Analytics, gaming servers, web servers, and batch processing would work well with the F-series.

Memory Optimized

Memory optimized VM types are higher in memory as opposed to CPU, and best suited for relational database services, analytics, and larger caches.

M-Series

Enterprise applications and large databases will benefit most from the M-series for having the most memory (up to 3.8 TiB) and the highest vCPU count (up to 128) of any VM in the cloud.

Dv2-series, G-series, and the DSv2/GS

For applications that require fast vCPUs, reliable temporary storage, and demand more memory, the Dv2, G, and DSv2/GS series all fit the bill for enterprise applications. The Dv2 series offers a speed and power with a CPU about 34% faster than that of the D-series. Based on the 2.3 and 2.4 GHz Intel Xeon® processors and with Intel Turbo Boost Technology 2.0, they can reach up to 3.1 GHz. The Dv2-series also has the same memory and disk configurations as the D-series.

Ev3-series

The Ev3 follows in the footsteps of the high memory VM sizes originating from the D/Dv2 families. This Azure VM types provides excellent value for general purpose workloads, boasting expanded memory (from 7 GiB/vCPU to 8 GiB/vCPU) with adjustments to disk and network limits per core basis in alignment with the move to hyperthreading.

Storage Optimized

For big data, SQL, and NoSQL databases, storage optimized VMs are the best type for their high disk throughput and IO.

Ls-series

VMs provide as much as 32 vCPUs with the Intel® Xeon® processor E5 v3 family. The Ls-series comes with the same CPU performance as the G/GS-Series and 8 GiB of memory per vCPU. This type works best applications requiring low latency, high throughput, and large local disk storage.

GPU

GPU VM types, specialized with single or multiple NVIDIA GPUs, work best for video editing and heavy graphics rendering – as in compute-intensive, graphics-intensive, and visualization workloads.

  • NC, NCv2, NCv3, and ND sizes are optimized for compute-intensive and network-intensive applications and algorithms.
  • NV and NVv2 sizes were made and optimized for remote visualization, streaming, gaming, encoding, and VDI scenarios.]

High Performance Compute

For the fastest and most powerful virtual machines, high performance compute is the best choice with optional high-throughput network interfaces (RDMA).

H-series

For the latest in high performance computing, the H-series Azure VM was built for handling batch workloads, analytics, molecular modeling, and fluid dynamics. These 8 and 16 vCPU VMs are built on the Intel Haswell E5-2667 V3 processor technology featuring DDR4 memory and SSD-based temporary storage.

And besides sizable CPU power, the H-series provides options for low latency RDMA networking with FDR InfiniBand and different memory configurations for supporting memory intensive compute requirements.

What Azure VM type is right for you?

With six virtual machine types belonging to multiple families and coming in a range of sizes, how do you determine the right Azure VM type for your workload? The good news is that with this many options, you’re bound to find the right type to meet your computing needs – as long as you know what those needs are. With good insight into your workload, usage trends, and business needs, you’ll be able to find the Azure VM type that’s right for you.

 

3 Ways To Use Google Cloud Cron for Automation

3 Ways To Use Google Cloud Cron for Automation

If you use DevOps processes, automation and orchestration are king — which is why the Google Cloud cron service can be a great tool for managing your Google Compute Engine instances via Google App Engine code. This kind of automation can often involve multiple Google Cloud services, which is great for learning about them or running scheduled tasks that might need to touch multiple instances.  Here are a few ideas on how to use the Google Cloud cron service:

1. Automated Snapshots

Since Google Compute Engine lets you take incremental snapshots of the attached disks, you can use the Google App Engine cron to take these snapshots on a daily or weekly basis. This lets you go back in time on any of your compute instances if you mess something up or have some systems fail. If you use Google’s Pub/Sub service, you can have the snapshots take place on all instances that are subscribed to that topic.

As a bonus, you can use a similar idea to manage old snapshots and deleting things you don’t need anymore. For example, schedule a Google Cloud cron to clean up snapshots three months after a server is decommissioned, or to migrate those snapshots to long-term storage.

2. Autoscaling a Kubernetes Cluster

With Google on the forefront of Kubernetes development, many GCP users make heavy use of GKE, the managed Kubernetes service. In order to save some money and make sure your containers aren’t running when they aren’t needed, you could set up a cron job to run at 5:00 p.m. each weekday to scale down your Kubernetes cluster to a size of 0. For maximum cost savings, you can just leave it off until you need it, then manually spin up the cluster, or you could use a second cron to spin you clusters up at 8:00 a.m. so it’s ready for the day.

(By the way — we’re working on functionality to let you do this automatically in ParkMyCloud, just like you can for VMs. Interested? Let us know & we’ll notify you on release.)

3. Send Weekly Reports

Is your boss hounding you for updates? Does your team need to know the status of the service? Is your finance group wondering how your GCP costs are trending for this week? Automate these reports using the Google Cloud cron service! You can gather the info needed and post these reports to a Pub/Sub topic, send them out directly, or display it on your internal dashboard or charting tool for mass consumption. These reports can be for various metrics or services, including Google Compute, Cloud SQL, or your billing information for your various projects.

Other Google Cloud Cron Ideas? Think Outside The Box!

Got any other ideas or existing uses to use the Google Cloud cron service to automate your Google Cloud environment? Let us know how you’re using it and why it helps you manage your cloud infrastructure.

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