Enterprise spending on public cloud shows no signs of slowing with some types of cloud spending growing by 92% per year. From a sustainability perspective, that might seem like a good thing — after all, cloud services tend to be more energy efficient than traditional data centers. But as enterprises embrace cloud to support user growth and revenue generation, few stop to consider the sustainability implications beyond choosing a cloud provider. Thankfully there are some simple ‘green cloud optimizations’ your IT organization can do, which are both good for the planet and your business.
To appreciate the need for greener cloud computing, it helps to consider some statistics. The amount of energy needed to power data centers roughly doubles every four years, and even in a best case scenario, the ICT industry is likely to account for 8% of total electricity demand by 2030 — a 15-fold increase from 2010. And when it comes to global greenhouse gas emissions (GHG or CO2e, hereafter referred to as carbon emissions), the ICT industry has contributed 2–6% consistently since 2007 — on par with the aviation industry.
The good news here is that despite the exponential growth of ICT and cloud adoption, the relative energy use and carbon emissions have remained roughly the same; due to significant efficiency gains from hyperscale data centers used by major public cloud providers. But that isn’t the full story. Cloud and data centers are increasingly becoming a bigger piece of the ICT industry’s carbon emissions pie. To meet the goals of the Paris Climate Agreement, the industry will need to reduce carbon emissions by 45% in the next 10 years. So even with considerable efficiency gains in the cloud, there is still much more to be done.
Why would a company embrace green cloud optimization? Etsy is a good example. For the first 11 years of its history, Etsy maintained its own servers in a co-located data center. As it grew, purchasing, installing and provisioning servers became time-consuming and hard to plan. Etsy found an astronomical difference between cloud and data center provisioning speed: cloud was an astonishing two million percent faster. Why? Etsy estimated it could take months to provision 150 servers in its data center, but only four minutes to do the same in the cloud.
With the cloud, organizations can scale capacity up and down in response to fluctuating demand. For example, you can run workloads to rotate around the globe, so they fit with local time zones, rather than running 24/7. Shifting towards serverless computing, such as GCP Cloud Functions or AWS Lambda, lets development teams run code without even provisioning or managing servers. In that way, you only pay for the compute time you consume.
Moving to a flexible cloud-based infrastructure enabled Etsy to reduce major idle time and associated energy consumption. As Etsy was transitioning from co-located data centers to the cloud, its combined energy consumption decreased by an estimated 13% (from 7330 MWh in 2018 to 6376 MWh in 2019) at the same time as their business grew.
There are algorithmic efficiency opportunities too. Machine learning, such as that used by Etsy, are targets for algorithmic efficiency, since these algorithms are particularly computationally intensive. While these opportunities exist within on-premise data centers too, cloud has given rise to easily accessible AI and ML services and proliferated their mainstream adoption. Researchers and practitioners are aware of the problems and costs associated with inefficient algorithms. They recommend comparing different models to perform a cost-benefit-accuracy analysis. Changes to these energy-demanding algorithms, can then produce measurably more efficient models and influence hardware choices.
One study of natural language processing models illustrates the extent of the problem. Researchers showed that training one NLP model produced carbon emissions equivalent to 125 roundtrip flights from New York to Beijing.
With the amount of computing power used for deep learning doubling roughly every quarter, when it comes to your AI/ML applications, efficiency needs to be as important a consideration as accuracy. We’re beginning to see some tech teams consider notifying developers about carbon emissions associated with compute intensive ML workloads in order to reduce costs and switch those workloads to run in geographic regions with more renewable energy on the grid.
When confronted with this energy challenge, organizations should firstly aim to shift to hyperscale data centers and optimize their cloud usage to reduce overall energy use. And secondly, they can green their remaining energy usage by switching to regions with more direct local renewables and running workloads at a time of day when renewables are available.
With traditional on-premise data centers, organizations know how much energy they are using from their energy bill. In the cloud, that information isn’t readily available. Although hyperscale data centers are more efficient than on premise, their on-demand model for rapidly scaling up and their readily available compute intensive services can mean that it’s easy for organizations to unwittingly consume more resources.
On premise infrastructure also means organizations need excess capacity to handle peak loads. In the cloud, any resources that you don’t need can easily be shed. But not every organization is seizing that opportunity. A lift and shift strategy could result in inefficient software and rack up higher costs. And given the ease with which new servers can be added, many organizations have lost control of their cloud estate and are seeing costs balloon, especially if they don’t pay attention or precisely forecast their computing needs. With the storage and transfer of data increasing exponentially (which we have seen for the last three decades) — and the next five years expected to see a doubling in cloud demand and increase in computationally and energy intensive technologies like AI, blockchain — it’s up to all of us to ensure our use of technology becomes more sustainable.
Unfortunately, many organizations think that the efficiencies of moving to the cloud are “enough”; optimizing and reducing their cloud costs and footprint even further is a blind spot. Or, they’re so focused on shipping products and on the customer-facing parts of their business, that they don’t have enough resources to dedicate towards the numerous internal efficiency and optimization opportunities.
But today’s stakeholders, from customers to employees to investors, expect more when it comes to organization’s sustainability commitments -- as we will see below -- and the good news is that there is a strong business case for reducing your cloud carbon footprint.
The good news is that reducing your cloud carbon footprint is win-win for all stakeholders.
1. Save money by reducing costs. The more efficient your cloud usage, the less you’ll spend. In 2018, Gartner estimated that organizations that didn't optimize their public cloud usage would spend 40% more by 2020. We’ve seen this first hand. At an independent news company, Thoughtworks optimized the infrastructure to reduce the number of IBM cloud virtual machines needed to support web applications and allow for better caching. We also increased performance by implementing responsive and dynamic interfaces that refreshed and loaded UI components only as needed, rather than the whole page. Our work reduced the compute resources needed by 50%, which resulted in 25–30% overall infrastructure cost savings.
2. Meet sustainability goals by reducing your carbon footprint. Reduce your number of servers and you’ll save electricity and cut carbon emissions; switch to cloud providers and services that use renewable energy and you’ll cut your carbon footprint even further. Companies like Spotify and SiteGround have switched to the carbon neutral Google Cloud Platform (GCP) in recent years to reduce their carbon footprint. Global retailer Forever New includes its use of containers and virtualization as part of its cloud strategy to reduce overall power consumption and carbon footprint. And MapBox configured its use of AWS regions to those that are covered by renewable energy, in order to achieve its goal of being carbon neutral.
3. Improve customer experiences with faster, more responsive applications. Optimizing application performance — with caching, image size, data transferred and so on — speeds up page load times and improves the overall customer experience of your digital channels. Similarly for internal applications, this can improve your employees’ efficiency by reducing the time it takes to complete tasks. By reducing the amount of data transferred and distance it travels, this reduces your cloud spend, energy usage and carbon footprint. The paradigm of edge computing which moves computing from central servers closer to the end user further reduces energy and emissions, as well as delivering faster services. Developers can improve algorithm and query performance using services like AWS CodeGuru that let developers know which lines of code need the most resources — and therefore cost the most. The developers can then explore ways of improving that code.
Thoughtworks modernized e-commerce systems at a major retailer, improving both site design and functionality, as well as moving them to the cloud. This involved migrating legacy features to a newer software platform and migrating its infrastructure to the cloud. The resulting site is four and a half times faster, delivers 20% higher revenue and increased conversation rates over the old site by 50%.
And of course communicating your efforts to customers has additional value: 81% of global consumers feel strongly that companies should help improve the environment. Akamai, one of the world’s largest cloud content delivery networks reported a 30–40% increase in requests for information about sustainability between 2018-2019. And a 2019 study found that 50% of growth in consumer packaged goods in the last five years came from sustainable products, whose sales grew almost six times faster than non-sustainable products. So whether companies are consumer-facing or B2B, customers are increasingly considering environmental performance as part of their decision making criteria.
4. Speed up development times by improving developer efficiency. Optimizing build pipelines and code so that it is faster and more efficient — such as by reducing unnecessary dependencies that consume extra storage or compute resources — helps to reduce costs, reduce energy usage and make your developers’ lives easier so that they can focus on delivering value.
At one online travel booking company we worked with, development teams had routinely left code from A/B experiments in their codebase after running tests and simply switched off the test or feature. After thousands of experiments over years, this added up to a significant cost and performance lag. The team identified that removing this excess code would result in notable speed and cost savings, not to mention reduce their overall energy use and carbon footprint.
Where possible, tasks should be automated because it allows them to be continuously improved and optimized. One simple example we’ve seen when working with US organizations to introduce standardized AWS CloudFormation templates that automate infrastructure setup: teams can ensure their default AWS region is set to US-West (Oregon), rather than US-East (Virginia). This would mean all new infrastructure by default uses a greater proportion of direct renewable energy and reduces an organization’s cloud carbon footprint, without anyone having to think twice about it.
5. Attract and retain talent with environmental initiatives. Around 70% of employees said they were more likely to work at a company with a strong environmental agenda, and more likely to stay there long term. In recent years, especially within the tech sector, many employees have been actively demanding greater action from their companies on climate change. Thousands of companies showed their support for the 2019 Global Climate Strikes and almost one fifth of Fortune 500 companies had committed to the Paris Climate Agreement emissions reductions targets in 2018.
6. Satisfy investors by disclosing carbon footprint information. Over the past 10 years, sustainable investment funds of companies with good environmental, social and corporate governance (ESG) practices have been shown to have superior long term stock performance to conventional funds. In 2020 investment firm Blackrock, the world’s largest asset manager, announced in its annual letter to CEOs that climate risk is financial investment risk and they will look at sustainability or ESG criteria for every investment. Thus more sustainable companies have greater access to capital when needed. Public companies are also facing increasing regulatory and compliance requirements.
So how can IT organizations ensure a greener cloud approach? One common misconception is that it is solely a matter of choosing the best cloud provider. While this is partially the case, it’s not the whole story. There is a lot that tech or IT organizations are responsible for during the development lifecycle too. Your digital actions have an impact: code + data = energy = carbon emissions.
Green cloud computing at its best is using a datacenter fed by local renewable energy that runs software on its infrastructure that is designed and optimized to minimize energy consumption (and costs).