The Future of the Enterprise Cloud Is Multi-Architecture Infrastructure
After years of steady declines, the costs of running a cloud data center are now soaring, due to various factors such as aging infrastructure, rising energy costs and supply chain issues.
A survey by Uptime Institute showed that enterprise data-center owners are most concerned about rising energy costs and IT hardware costs, respectively. In a recent example, Google announced increases in some of its cloud storage and network egress prices, some of which had previously been free to users.
In short, “cloud-flation” is a new reality, and developers are paying a price by having to do more with less. Organizations need to continuously understand and measure the impact of their compute to balance performance, efficiency, and design flexibility in line with budget and business goals.
What are some steps developers can take to reduce costs?
There are a few pieces of low-hanging fruit, including:
- Optimize the allocation of cloud resources by analyzing usage patterns and adjusting the size of instances, storage, and databases to match the workload’s requirements.
- Implement auto-scaling mechanisms that dynamically adjust the number of instances based on demand. This ensures resources are provisioned as needed, preventing over-provisioning during low-traffic periods and reducing costs.
- Evaluate your data storage and database needs and choose the most cost-effective options. Use tiered storage options to move infrequently accessed data to lower-cost storage tiers.
Many companies have adopted cost monitoring and reporting, creating alerts to notify their teams of sudden spikes or anomalies. One significant movement in this direction is around FinOps, a concatenation of “finance” and “DevOps.” This emerging cloud financial management discipline enables organizations to help teams manage their cloud costs. Another example is Amazon Web Services, with its cost-optimization guide pointing to Arm-based Graviton as a way to improve price performance.
Embrace the New Paradigm
Perhaps the most important piece of fruit to be harvested, however, is to think about computing differently. And this is easier said than done.
Data centers and the cloud grew up on a single, monolithic approach to computing — one size fits all. That worked in the days when workloads were relatively few and very straightforward. But as cloud adoption exploded, so too have the number and types of workloads that users require. A one-size-fits-all environment just isn’t flexible enough for users to be able to run the types of workloads they want in the most effective and cost-efficient manner.
Today, technologies have emerged to overthrow the old paradigm and give developers and cloud providers what they need: flexibility and choice. One of its manifestations is multi-architecture, the ability of a cloud platform or service to support more than one legacy architecture and offer developers the flexibility to choose.
The Liberty of Flexibility and Choice
Flexibility to run workloads on the architecture of your choice is important for organizations for two reasons: better price performance and — a reason that is far downstream from data centers but nevertheless important — laptops and mobile devices.
Price performance often arises when organizations realize that running workloads, like web servers or databases, on Arm could be cost-effective, either for themselves or in response to customer demand. Remember those statistics I shared earlier? They’re a big motivation in this context. And this is why the flexibility to choose the right compute for the right workload is critical.
Arm has a legacy of delivering cost-effective, power-efficient computing solutions for mobile technologies for over 30 years. During those years, other sectors, including infrastructure, have embraced these benefits. Today every major public cloud provider runs Arm in some form for various workloads. For example, 48 of the top 50 AWS customers run on Arm Neoverse-based AWS Graviton. The cost benefits of deploying an Arm-based server compared to a traditional one are significant.
The second motivation relates to laptops, which are increasingly being run by power-efficient Arm processors. Developers using these machines began wanting to develop on Arm all the way from their laptop into the cloud. They’re embracing Arm64 for their production and development environments because it makes it easier to troubleshoot and reproduce bugs locally earlier in the development process. And with Arm-based processors now available in every major cloud, Arm-native developers need a multi-arch aware toolchain to safely deploy their code.
Doing the Work
We see three main steps to adopting a multi-arch infrastructure: inform, optimize and operate:
- Inform involves taking an inventory of your entire software stack, including finding the operating systems, images, libraries, frameworks, deployment and testing tools, monitoring solutions, security measures and other components you rely on. Make a comprehensive list and check each item for Arm support. Additionally, identify the most resource-intensive components in terms of compute, as these will be your hotspots for optimization.
- Optimize allows you to provision a test Arm environment easily. You can spin it up on a public cloud and proceed to make necessary upgrades, changes and conditional statements to ensure compatibility with different architectures. It is crucial to determine the key metrics you care about and conduct performance testing accordingly. Simultaneously, consider upgrading your CI/CD processes to accommodate more than one architecture. Keep in mind that this stage may require many iterations and that you can start migrating workloads before completing all infrastructure upgrades.
- Operate within your chosen environment. For instance, in Kubernetes, decide how you will build your cluster. Consider whether to prioritize migrating control nodes or worker nodes first, or opting for a mixture of both. This decision will depend on your software stack, availability and initial workload choices. Modify your cluster creation scripts accordingly.
With the infrastructure in place, you can proceed to deploy your workloads.
Conclusion
A few years ago, migrating to Arm meant hunting for specific versions of software and libraries that support Arm. This support was limited and uneven. But over time, the ecosystem matured quickly, making the migration easier and ensuring software on Arm “just works.” Some notable vendors, such as Redis, MongoDB and Nginx, have come out with Arm support in recent years. This improved ecosystem support contributes to the ease of migration.
The high-performance, energy-efficient benefits of transitioning away from running only on legacy architectures are such that big companies like Airbnb are undergoing this migration, even though they know it’s a multi-year journey for them. Airbnb knows they have a complex infrastructure that assumes everything is x86, so they need to adjust and test all their systems to ensure compatibility with both x86 and Arm architectures. In Airbnb’s case, the longtime benefits are worth the up-front costs and time investment.
Melanie Cebula, a software engineer at Airbnb, put it this way at a 2022 KubeCon-CloudNativeCon presentation: “So Arm64 is in the cloud. It’s in all the clouds, and it’s cheaper, and that’s why we’re doing the work.”
Further solidifying these points, Forrester’s Total Economic Impact study, including interviews and survey data, revealed a cloud infrastructure cost savings of up to 80%, with 30% to 60% lower upfront infrastructure costs.
The key to accelerating and unlocking even more innovation in the world today is for workloads to run on the best hardware for the user’s price-performance needs. Arm technology continues to push the boundaries of the performance-efficiency balance without the developers having to worry about whether their software is compatible.
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