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.
Today, we’ll take a look at the latest AWS vs Azure vs Google Cloud market share comparison, including the Q4 2020 earnings the ‘big three’ cloud providers have reported. Let’s take a look at all three providers side-by-side to see where they stand.
Note: several previous versions of this article have been published. It has been updated for February 2021.
AWS vs. Azure vs. Google Cloud Earnings
To level-set this comparison, first know that – unsurprisingly – the cloud market as a whole is bigger than ever. Gartner has predicted worldwide public cloud spend to grow 18% in 2021, with 70% of organizations using cloud to increase cloud spending in the wake of COVID-19.
So within that market, let’s take a look at the AWS vs Azure vs Google Cloud market share breakdown and what each cloud provider’s reports shared.
First, the big news, of course: Jeff Bezos is trading his CEO role for Executive Chair of the Amazon Board, while current CEO of AWS Andy Jassy will step up to the Amazon CEO role. No AWS CEO has yet been announced, but many bets are on Matt Garman, currently the Vice President of AWS Sales and Marketing, or else Peter DeSantis, AWS’s Vice President of Global Infrastructure.
Next, the bigger news: Amazon revenue.
Amazon reportedAmazon Web Services (AWS) revenue of $12.7 billion for Q4 2020, compared to $9.95 billion for Q4 2019. AWS revenue grew 28% in the quarter.
Amazon as a whole had their first quarter over the $100 billion mark, at $125.56 billion. That’s an increase of 44% year-over-year, and beating predictions of $119.7 billion. Earnings per share were $14.09, compared to a $7.23 forecast.
Amazon as a whole benefitted from an astronomical online holiday shopping season due to COVID-19, and also from Prime Day being held in the fourth quarter. And AWS? It made up 10% of Amazon’s sales for the quarter – and 52% of its operating income.AWS only continues to grow, and bolster the retail giant time after time.
One thing to keep in mind: you’ll see a couple of headlines pointing out that revenue growth is down and/or highlighting the fact that it’s flattening out, quoting that 28% number and comparing it to previous quarters’ growth rates, which peaked at 81% in 2015. However, that metric is of questionable value as AWS continues to increase revenue at this enormous scale (see Geekwire graph), and dominate the market (as we’ll see below). AWS added more revenue quarter-over-quarter and year-over-year than any quarter in its history. Dave Fildes, Director of Investor Relations, mentioned on the call that “If you account for this COVID anomaly this year of [AWS re:Invent] being virtual and free, AWS year-over-year revenue growth, if you look at it, actually accelerated adjusting for that from the third quarter to the fourth quarter,” an interesting tidbit both from the perspective of gaining a glimpse into what re:Invent actually does for the company, and that AWS revenue is accelerating.
While Amazon specifies AWS revenue, Microsoft only reports on Azure’s growth rate. That number is 50% revenue growth over the previous quarter. This time last year, growth was reported at 62%. As mentioned above, comparing growth rates to growth rates is interesting, but not necessarily as useful a metric as actual revenue numbers – which we don’t have for Azure alone.
Here are the revenue numbers Microsoft does report. Azure is under the “Intelligent Cloud” business, which grew 23% to $14.6 billion. The operating group also includes server products and cloud services (26% growth).
The lack of specificity around Azure frustrates many pundits as it simply can’t be compared directly to AWS, and inevitably raises eyebrows about how Azure is really doing. Of course, it also assumes that IaaS is the only piece of “cloud” that’s important, but then, that’s how AWS has grown to dominate the market.
Nonetheless, Microsoft’s cloud business is clearly generating success for the company. Intelligent Cloud delivered the highest operating income of all segments this quarter at $6.4 billion, which is 36% of total consolidated operating income.
In more exciting news for public cloud followers, Alphabet has broken out Google Cloud revenue for the first time. Thus we learned that while Google Cloud revenue has increased over the last three years, so too have their operating losses. CFO Ruth Porat notes that these operating losses “reflect that we have meaningfully built out our organization, ahead of revenue.”
This quarter, Google Cloud reported revenue of $3.83 billion, an increase of 47% year-over-year. Operating losses were $1.24 billion compared to losses of $1.19 billion one year previously. For the full fiscal year 2020, Google Cloud’s revenue was $13 billion, with $5.6 billion operating losses.
Note that the Google Cloud unit includes not only Google Cloud Platform but also Google Workspace (formerly G Suite).
One highlight was that deals over $250 million tripled during 2020, and several billion-dollar deals were closed during the year.
We’ll add Alibaba Cloud to this list for the first time as the cloud computing division is profitable as of this quarter. The cloud computing arm of the Chinese retail giant earned $2.47 billion this quarter, an increase of 50% year-over-year.
Cloud Computing Market Share Breakdown – AWS vs. Azure vs. Google Cloud
When we originally published this blog in 2018, we included a market share breakdown from analyst Canalys, which reported AWS in the lead owning about a third of the market, Microsoft in second with about 15 percent, and Google sitting around 5 percent.
In 2019, they reported an overall growth in the cloud infrastructure market of 42%. By provider, AWS had the biggest sales gain with a $2.3 billion YOY increase, but Canalys reported Azure and Google Cloud with bigger percentage increases.
As of February 2021, Canalys reports that the worldwide cloud market grew 32% this quarter to $39.9 billion. For the full year of 2020, cloud infrastructure spending grew 33% to $142 billion. AWS has 31% of the market, followed by Azure at 20%, Google at 7%, Alibaba Cloud close behind.
Bezos has said, “AWS had the unusual advantage of a seven-year head start before facing like-minded competition. As a result, the AWS services are by far the most evolved and most functionality-rich.”
Our anecdotal experience talking to cloud customers often finds that true. It seems clear that in the case of AWS vs Azure vs Google Cloud market share – AWS still has a substantial lead, and their market share remains steady. With that said, all players are pushing growth and innovation and driving public cloud adoption across the board.
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.
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.
Every year, an exorbitant amount of money is wasted on idle cloud resources. That is – resources that are provisioned, and being paid for, but not actually being used. This is a huge problem that clogs up cloud environments and drains budgets.
Note: a version of this blog was originally published in 2018. It has been completely updated and rewritten for 2020.
Even the Cloud Providers are Talking About It
The issue of idle resources is something that is recognized even by the cloud providers themselves. This may sound counterintuitive. Doesn’t AWS just want as much money from you as it can get? Well, maybe, yes: but the best way for them to do this is by providing you with a positive experience and the most value for your money.
Case in point: at the AWS re:Invent keynote this week, Andy Jassy spoke about a few core guidelines for organizations to follow to ensure organizations are on the path for successful technology financial management. “Start early and start small…The key is to start experimenting with what matters the most to your organization” Jassy said. He shared that a great place to start is by deleting or stopping idle resources in your cloud environment. Small changes like this can have huge impacts and benefits can increase as time goes on. Idle resources are eating at your cloud budget causing you to spend money on resources that aren’t even being used.
AWS’s cloud financial management framework mentions this among the myriad ways your organization can improve practices to reduce usage waste and optimize costs.
The Cost of Idle Resources
The typical “idle resources” that come to mind are instances purchased On Demand that are being used for non-production purposes like development, testing, QA, staging, etc. These resources can be “parked” when they’re not being used, such as on nights and weekends, saving 65% or more per resource each month. In order to fully understand the problem of idle cloud resources, we have to expand this scope beyond just your typical virtual machine.
Most non-production resources can be parked about 65% of the time, that is, parked 12 hours per day and all day on weekends (this is confirmed by looking at the resources parked in ParkMyCloud – they’re scheduled to be off just under 65% of the time.) We see that our customers are paying their cloud providers an average list price of $220 per month for their instances. If you’re currently paying $220 per month for an instance and leaving it running all the time, that means you’re wasting $143 per instance per month.
Maybe that doesn’t sound like much. But if that’s the case for 10 instances, you’re wasting $1,430 per month. One hundred instances? You’re up to a bill of $14,300 for time you’re not using. And that’s just a simple micro example. At a macro level that’s literally billions of dollars in wasted cloud spend.
4 Types of Idle Cloud Resources
So what kinds of resources are typically left idle, consuming your budget? Let’s dig into that, looking at the big three cloud providers — Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
On Demand Instances/VMs – this is the core of the conversation, and what we’ve addressed above. On demand resources – and their associated scale groups – are frequently left running when they’re not being used, especially those used for non-production purposes.
Databases – there’s no doubt that databases are frequently left running when not needed as well, in similar circumstances to the On Demand resources, particularly non-production. The problem is whether you can park them to cut back on wasted spend. AWS allows you to park certain types of its RDS services like Neptune and Redshift databases, RDS instances and Google Cloud SQL. Make sure you review your database infrastructure regularly and terminate anything unnecessary – or change to a smaller size if possible.
Load Balancers – AWS Elastic Load Balancers (ELB) cannot be stopped (or parked), so to avoid getting billed for the time you need to remove it. The same can be said for Azure Load Balancer and GCP Load Balancers. Alerts can be set up in Cloudwatch/Azure Metrics/Google Stackdriver when you have a load balancer with no instances, so be sure to make use of those alerts.
Containers – optimizing container use is a project of its own, but there’s no doubt that container services can be a source of waste. It’s important that you regularly review the usage of your containers and the utilization of the infrastructure, especially in non-production environments. In the last few months, ParkMyCloud has released support for Amazon EKS, Azure AKS and Google Cloud GKE so customers can make sure their idle resources are parked.
Cloud waste is a billion-dollar problem facing businesses today. Make sure you’re turning off idle cloud resources in your environment, by parking those that can be stopped and eliminating those that can’t, to do your part in optimizing cloud spend.