The idea that you will reduce costs during an Azure migration is not a given. In fact, when an organization is new to the cloud is when they are prone to missing details or falling in step with habits from their on-prem days. Unfortunately, up to $26.6 billion will be wasted this year due to idle and overprovisioned cloud resources. All of that could be prevented.
When the team at Providence Health took on a migration to Microsoft Azure in early 2020, they planned ahead for these challenges – and built cost optimization into their migration plans, so they could land with an optimized infrastructure and truly take advantage of the elasticity of public cloud.
Here’s how they did it.
First, Overcome the Cultural Challenges of Azure Migration
Migration can be scary for users who have never used the public cloud before. Often, those who are less familiar with public clouds have misconceptions about security, reliability, or cost. When Bryan de Boer joined the Providence Health team as an Executive Director of IT in 2020, their Microsoft Azure migration project was just beginning. Bryan recalled, “The very ideas of cloud elasticity, flexibility, and optimization were completely foreign to this enterprise… Servers were fixed resources that you bought; they were physical resources that just sat there, and it was a one-time upfront purchase. There was a conceptual paradigm shift that needed to occur.”
Luckily, by getting in front of these misconceptions with education, you can address them and get your teams on board and even excited for the move to the public cloud. Bryan told us the process of educating key stakeholders was fun, and this is not to be ignored – by treating your colleagues kindly and with good humor, you can build the rapport and shared-dream teamwork that makes digital transformation possible throughout the organization.
Optimization is not a one-time activity. In fact, we estimate cloud resource drift of about 10-15% month over month, which could mean a complete environment turnover in less than one year. Thus, continuous optimization is key. One straightforward way to bake this optimization into your processes is by setting on/off schedules on non-production resources, such as development, testing, staging, and QA, so that you’re only paying for on-demand resources when you need them. This is also a great way to give some ownership over cloud resources and costs to the end-users within your organization, to make the transition from an overprovisioning-happy on-premises model to the on-demand consumption charging model of the public cloud.
Speed to Vaccination Supported by Cloud Adoption
Once you internalize the concepts of cloud elasticity, you can start to grow and innovate faster than ever before. For the Providence Health team, this took the trailblazing form of building a new COVID-19 vaccination tracking and scheduling application that they were able to launch in a matter of days – a process that would have taken months pre-migration. With this rapid test case, the Admin Tech application team proved that they could solve significant problems in a very short period with the agility of the cloud. This became a key proof point in driving the internal cultural change to show the value of cloud infrastructure.
Join Us Live for More
Want to hear Providence’s story, live? Want to see how they were able to launch that vaccine tracking application so quickly? Join us.
Bryan de Boer will be speaking at our upcoming Apps ON Cloud Summit, happening online May 11th through 13th. Learn from customers like Providence alongside the industry’s best, like Kelsey Hightower and prince of snark Corey Quinn. Plus, we’re doing a giveaway for a two-night getaway to one lucky attendee – details on the registration page. Hope to see you there!
There are three advantages of using public cloud: cloud elasticity, agility, and cost reduction. However – most public cloud users are not (yet!) achieving all three of these. While you may be aware if your costs are out of control, what you may not consider is how directly that’s tied to a misuse of the idea of “elasticity”.
The True Meaning of Cloud Elasticity
There’s a reason “elastic” is in the name of EC2 and EBS. Users can provision what they want, when they want it, to easily scale up to meet demand. This ability is what has fueled the explosion of agility and enabled experimentation and innovation.
But the other ingredient in elasticity is shrinking or turning off resources dynamically to meet demand, which is not nearly as easily achieved… and is often neglected. The problem with only being “half” elastic is fundamental to the purpose of the cloud.
When you let resources in one direction only – up – in the name of “elasticity”, you neglect the promise of the cloud: only pay for what you need. You’ll be slapped in the face with rising costs. And yes – we’ve seen this happen time and time again. Organizations find that those easily-provisioned resources stick around, clogging their environments with waste.
Knowledge is Power, but Not Always Action
A recent Turbonomic survey found that optimizing existing cloud resources for performance and cost was the #1 goal for cloud users this year. The fact that this came in at #1 shows us that there’s certainly awareness among cloud customers that this is a problem they need to solve.
This is not news to you. You’ve perhaps seen the bill. You may have dashboards slicing and dicing your cloud bill. But too often, those dashboards are simply something to pull up for pretty data (or ugly, as the case may be) to share in a team meeting. The knowledge doesn’t
We get it. We’re all busy. Our priorities in the workplace are always shifting, and honestly, it can be hard to make ✨achieving the promise of cloud elasticity✨ one of them. It’s definitely hard to make “reducing costs” one of them.
But here’s the thing: knowledge – like the visibility you gain from cost dashboards – is power.
In our experience, most people do not act on that power.
When Elasticity Goes Two Ways, You Can Optimize
What would it look like to take that knowledge and turn it into action?
You would truly only use the resources you need, when you need them. You would take advantage of public cloud’s usage-based pricing model, and achieve a cost-optimized environment.
And ideally, this would be automated – so the action just happens and you can spend your time on those more exciting benefits of cloud infrastructure: growing and innovating. When you eliminate wasted spend from your cloud environment, you open up your budget to achieve the business goals that really matter.
Here at ParkMyCloud, we help you with ensuring elasticity with easy-to-implement actions based on your utilization data. By “parking” non-production resources when not needed, such as nights and weekends, you can reduce the cost of those resources by 65% – an easy win for both elasticity and cost optimization.
Topping the list of cloud computing trends 2021 is…drumroll please…cloud optimization!
This is according to data collected by the annual State of Multicloud Survey run by Turbonomic (that’s our parent brand). The survey, using data collected from more than 800 global IT professionals, examines the state of multicloud adoption, its drivers, and the technologies enabling it, including public cloud, containers, and edge computing. Here are a few highlights from the report.
Optimization Remains Top Priority for Public Cloud
As mentioned above, when asked their most important initiative for 2021, survey respondents ranked “optimize existing cloud resources for performance and cost” as their highest priority. We see this need playing out every day as we talk to enterprises, both newly migrated and with years of experience in the cloud. Those who are paying attention are realizing their infrastructure is overprovisioned, running when not needed, or otherwise wasted – to the tune of $26.6 billion wasted this year in all. By making optimization a priority, tech leaders can reallocate wasted spend and achieve more with their budgets.
Advancing a Multicloud Strategy Rated #2
After optimization, the next-highest ranked initiative was “advancing a multicloud strategy”(multiple public clouds with or without private clouds in the mix). Multicloud existence is the reality for most organizations – and 30% of survey respondents are using three or more public clouds today. Research by IDG finds that the most common reported reason for using multiple public clouds is to get the best platform and service options available.
Relevance of PaaS is Growing
Of organizations surveyed, 62% reported that public cloud PaaS will play a strategic role for their business within 18 months. This is supported by research from Gartner, which estimates the PaaS market will grow by 26% this year, driven at least in part by the need for remote workers to have access to scalable infrastructure in the form of modernized and cloud-native applications.
Container Use is Advancing – But Difficult to Scale
For 61% of organizations surveyed, containerization will play a strategic role within 18 months. It is already strategic for nearly 20% today. Of those already implementing containers, 56% are using them in production. For the remainder, operations are difficult to scale due to the complexity introduced and the difficulty to optimize.
The Full Report on Cloud Computing Trends 2021
Check out the report for the full data for each of these highlights, as well as results about AWS vs. Azure use, container deployment trends, edge computing, and how leaders compare to laggards in cloud services and strategy adoption. Read the 2021 State of Multicloud Report now.
If optimization is a priority for your cloud organization, check out the free trial of ParkMyCloud to start reducing costs through parking and rightsizing.
Google Cloud Platform offers a range of machine types optimized to meet various needs. Machine types provide virtual hardware resources available to a virtual machine that vary by virtual CPU (vCPU), disk capability, and memory size, giving you a breadth of options. Within every machine family there’s a set of machine types that offer a combination of memory and processor configuration. With so much to choose from, finding the right Google Cloud machine type for your workload can get complicated.
Since we’ve gone over EC2 instance types and Azure VMs, we’re doing an overview of each GCP machine type. The image below shows the basics of what we will cover, but remember that you’ll want to investigate further to find the right machine type for your particular needs.
A version of this article was published in 2018. It has been completely revised and updated for 2021.
General-Purpose Machine Type Family
General-purpose machine types are resources managed by Google Compute Engine. Each machine type in the general-purpose machine type family is curated for specific workload types. You’ll find that these machine types are suitable for a variety of common workloads. Some examples of these workloads include: development and testing environments, databases, mobile gaming and web applications. These machines are known for offering the best balance of performance and price. Within the General-purpose family, you can choose from four general-purpose machine types: E2, N2, N2D, and N1.
E2 Machine Types
E2 VMs provide a variety of compute resources for the lowest on-demand pricing across all general-purpose machine types. E2 machine types also utilize dynamic resource management, which offers numerous benefits for workloads that prioritize cost savings. These machine types offer the lowest cost of ownership on Google Cloud – you’ll see up to 31% of savings compared to N1. Additionally, pricing for E2 VMs already includes sustained use discounts and are also eligible for committed use discounts – which increases potential savings of up to 55%.
Best fit: Workloads such as small-to-medium databases, web serving, and application development and testing environments that don’t require large instance sizes, GPUs or local SSD would all be good fits for E2.
N2 Machine Types
N2 machine types are the second generation general-purpose machine types that support up to 80 vCPUs and 640 GB of memory. N2 VMs offer you the ability to get about 30% higher performance from your VMs, and shorten many of your computing processes. These machine types offer higher memory-to-core ratios for VMs created with the extended memory feature.
Best fit: General purpose workloads including web and application servers, enterprise applications, gaming servers, content and collaboration systems, and most databases.
N2D Machine Types
N2D machine types are the largest general-purpose machine type with up to 224 vCPUs and 896 GB of memory. These VMs are designed to provide you with the same features as N2 VMs.
Best fit: Web applications, databases, workloads, and video streaming.
N1 Machine Types
N1 VMs are first-generation general-purpose machine types that support up to 96 vCPUs and 624GB of memory. Though most would recommend using one of the second-generation general-purpose machine types, N1 VMs do offer a larger sustained use discount than N2 machine types. Additionally, they have support for Tensor Processing Units (TPUs) in select areas.
Compute-Optimized Machine Type Family
Compute-optimized machine types are ideal for compute-intensive workloads. These machine types offer the highest performance per core and most consistent performance on Compute Engine. Compute-optimized machine types are suitable for workloads such as game servers, latency-sensitive API serving and high-performance computing (HPC). These machines have 40% greater performance than previous generation N1.
C2 Machine Types
C2 machine types provide full transparency into the architecture of the underlying server platforms which will allow you to fine-tune the performance. C2 machine types run on a newer platform, offer more computing power, and are typically more powerful for compute-intensive workloads compared to the N1 high-CPU machines.
C2 VMs also offer up to 20% sustained use discounts. Additionally, they are eligible for committed use discounts, which would bring potential savings up to 60%.
Memory-Optimized Machine Type Family
Memory-optimized machine types are fit for tasks that require intensive use of memory with higher memory-to-vCPU ratios due to the fact that they offer the highest memory configurations across our VM families with up to 12 TB for a single instance. It is important to note that these machine types do not support GPUs. Memory-optimized machine types offer up to 30% sustained use discounts. Additionally, they are eligible for committed use discounts, bringing additional savings up to greater than 60%.
These machine types are best fit for in-memory databases and in-memory analytics.
M2 Machine Types
M2 VMs support the most demanding and business critical database applications with up to 12TB of memory. These machine types offer the lowest cost per GB of memory on Compute Engine, which makes them a perfect choice for workloads that utilize higher memory configurations and have low compute resources requirements.
M1 Machine Types
M1 machine types are the first generation memory-optimized machine types that offer 4TB of memory. Similar to M2 machine types, M1 machine types offer the lowest cost per GB of memory on Compute Engine.
Accelerator-optimized Machine Type Family
Accelerator-optimized VMs were added in July 2020. This machine type family is optimized for demanding compute workloads – this would be workloads such as high-performance computing and CUDA-enabled machine learning.
A2 Machine Types
Every A2 VM has a set amount of A100 GPUs that offer 20x improvements in computing speed in comparison to previous generation NVIDIA V100 GPUs. These machine types are currently available through Google’s alpha program.
Shared-Core Machine Types
Share-core machine types are a cost-effective option that works well with small or batch workloads that only need to run for a short time. They use partial vCPUs that run on one hyper-thread of the host CPU running your instance. These machine types use context-switching to share a physical core between vCPUs so they can multitask.
The GCP shared-core family provides bursts of physical CPU for brief periods of time in moments of need. They’re like spikes in compute power that can only happen in the event that your workload requires more CPU than you had allocated. These bursts are only possible periodically and are not permanent.
Custom Machine Types
Predefined machine types vary to meet needs based on high memory, high vCPU, a balance of both, or both high memory and high vCPU. If none of the machine types meet your needs, Google has one more option for you – custom machine types. With custom machine types, you can define exactly how many vCPUs you need and what amount of system memory for the instance. They allow you to independently configure CPU and memory to find the right balance for your applications, so you’re only paying for what you need. They’re a great fit if your workloads don’t quite match up with any of the available predefined types, or if you need more compute power or more memory, but don’t want to get bogged down by upgrades you don’t need that come with predefined types.
About GPUs and machine types
On top of your GCP instance types, Google also offers graphics processing units (GPUs) that can be used to boost workloads for processes like machine learning and data processing. You can only attach GPUs to predefined or custom machine types. In general, the higher number of GPUs attached to your instances, the higher number of vCPUs and system memory available to you.
What Google Cloud Machine Type should you use?
Between the predefined options and the ability to create custom Google Cloud machine types, Google offers enough variety for almost any application. Cost matters, but with the resource-based pricing structure, the actual machine you chose matters less when it comes to pricing.
With good insight into your workload and usage trends you have the resources available to find the machine type that fits your business needs.
Overprovisioning and leaving cloud resources on are two enormous sources of wasted spend.
Wasted spend drags down IT budgets – of particular importance as we enter 2021. The Flexera 2021 State of Tech Spend report found that the biggest change in key IT initiatives from 2020 to 2021 was in cost savings, with the percent of respondents ranking cost savings as a top initiative tripling year-over-year.
It’s important that this is being recognized. Based on data collected by Gartner, we estimate that wasted spend will exceed $26.6 billion this year.
Where the Wasted Cloud Spend is Coming From
Gartner estimates a total market spend of $304 billion on public cloud services end-user spending in 2021, as broken out in the table below. Their estimate for the proportion of that spent on Infrastructure as a Service (IaaS) is $65.3 billion. While wasted spend can be found in any area of cloud spend, customers tend to see the largest amount in these two areas, as well as finding it easiest to identify.
Cloud resources can be considered “idle” when they are running while not being used. For example, when development servers are left running overnight and on weekends when they’re not needed. Since compute resources are paid for by the minute or second, that’s a large portion of the week they’re being paid for but not used (and yes, this applies even if you have reservations.)
Our data shows that about 44% of compute spend is on non-production resources. If we estimate that non-production resources are only needed during a 40-hour work week, the other 128 hours (76%), the resources are sitting idle.
Applying that to the Gartner IaaS number, we estimate that up to $14.5 billion will be wasted on idle resources this year.
Overprovisioning occurs when a larger resource size is selected than is actually needed. There is a mindset of safety behind this, as of course, no one wants their applications to be under-resourced.
But the overprovisioning occurring is far beyond what is necessary, given the elasticity of the cloud. About 40% of instances are sized at least one size larger than needed for their workloads. The cost can be cut in half by reducing an instance by one size, while downsizing by two sizes saves 75%.
Many of our customers show a large percentage of their resources are oversized, but bringing this to a conservative estimate of 40% of resources oversized by one size, giving us a savings per resource of 50%, we estimate that up to $8.7 billion is wasted due to overprovisioning.
Orphaned Volumes and Snapshots
Another significant source of waste is orphaned volumes and snapshots. These are resources that have been detached from the infrastructure they were created to support, such as a volume detached from an instance or a snapshot with no volume attachment.
Our customers spend approximately 15% of their bills on storage, and we found that about 35% of that spend is on unattached volumes and snapshots. Applying that to the Gartner spending numbers, we estimate that up to $3.4 billion could be wasted this year on orphaned volumes and snapshots.
Reducing Wasted Spend
Altogether, this gives us an estimate of $26.6 billion to be wasted on unused cloud resources in 2021. This waste estimate is just based on the three prominent sources of cloud waste. It does not include wasted spend on Platform as a Service (PaaS), which makes up $55 billion in cloud spend according to Gartner’s estimates, nor from SaaS, unused reservation commitments, inefficient containerization, and other areas of the bill.
Attacking the three problem areas above is a great area to start for nearly all public cloud users. Here at ParkMyCloud, we’re on a mission to do just that. See how and try it out today, to do your part in reducing wasted cloud spend.
If you’re looking to break into the cloud computing space, or just continue growing your skills and knowledge, there are an abundance of resources out there to help you get started, 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.
With the combined knowledge from 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 are training resources for every experience level and learning type – get started learning now with these 7 free Google Cloud Platform training resources:
1. Google Cloud Free Program
For free, hands-on training there’s no better place to start than with Google Cloud Platform itself. Within the Google Cloud free program you’ll have two options – sign up for a free trial or free tier. As a new Google Cloud customer, you can get started with a 90-day free trial. If you’re already a Google Cloud customer and are looking for a free option, you can sign up for Google Cloud’s free tier. GCP’s free program 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 with your free trial.
$300 credit is yours to spend for the next 90-days, 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. Free tier customers can use select Google Cloud products free of charge, with specified monthly usage limits, making this a perfect option for learning purposes.
For help with navigating the platform as you use it, check out GCP’s documentation for a full overview, comparisons, tutorials, and more.
On the Google Cloud training page, you’ll find plenty of classes tailored to your interests or role so you can get technical skills and learn best practices for using the platform. As another free Google Cloud training option, Google has 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 including topics in Machine Learning, Architecting, Data Engineering, Developing Applications, and the list goes on.
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 and get started with 50+ hands-on labs from beginner to expert level. Here 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. Plural Sight
Pluralsight is a technology skills platform that offers a full breadth of Google Cloud courses, learning paths, and skills assessments. You’ll find several Google Cloud resources to help level up your skills. If you’re looking to dive deeper into Google Cloud, this is a great option – get started learning with a free trial and make sure to keep an eye out for training discounts offered by Google.
GitHub provides users a number of materials that can help further your Google Cloud training. The great thing about this platform is collaboration among the users, this community brings together people from all different backgrounds so they are able to provide knowledge about their own specialties and experiences. Here’s a great list of Google Cloud training resources that can help you.
You can never go wrong with YouTube. With an endless amount of free videos, YouTube offers an abundance of Google Cloud training options 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 Platform training include:
Google Cloud Platform (640k subscribers) – “helping you build what’s next with secure infrastructure, developer tools, APIs, data analytics and machine learning.”
Edureka (2.29M 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 help get you started.
7. Blogs & Forums
Blogs are a great way to keep your mind flowing with new insights, ideas, and the latest on all things cloud computing. Google Cloud and Qwiklabs have blogs of their own, perfect for supplemented reading with their trainings. For a more well-rounded blog with content on other service providers, check out Cloud Academy.
Take Advantage of These Free Google Cloud Training Resources
It is clear that cloud computing is here to stay and as cloud technology continues to grow and advance, free training resources only continue to emerge so it’s important to stay up to date on new resources. We picked the 7 above for their reliability, variety, quality, and range of information. With the current working remote culture, this is the perfect time to take advantage of free google cloud training online. 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.