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Gartner predicts that through 2020, 80 percent of organizations will overshoot their cloud IaaS budgets. Gartner attributes cloud cost management problems to three main causes:
- Companies are increasingly adapting complex multi-cloud environments, and billing can vary greatly across providers;
- DevOps teams lack the expertise to manage costs in the cloud;
- Leaders are struggling to align their organizational cloud strategies with the plethora of cloud expense management solutions on the market.
Our own research bears this out. In a recent cloud services survey we conducted, almost 70 percent of respondents reported overspending on their cloud budgets by 25 percent or more.
In short: Many organizations are spending too much on their cloud bills because optimizing cloud applications to bring down spend is very difficult.
Like any business, cloud hosting companies want to drive revenue. But customers who feel they’re being fleeced may well switch to other providers that will help optimize their cloud usage. Savvy cloud hosting companies are doing more than simply pocketing the spiraling bills of the service providers they host. They’re helping customers reduce costs by helping them optimize cloud applications. This act of customer service will improve customer relations, loyalty and pay dividends in the long run.
Today, most DevOps teams operate a Continuous Integration/Continuous Deployment (CI/CD) pipeline, where cloud applications are iterated and developed in rapid cycles on an ongoing basis. However, while application teams pay very close attention when they are spinning up an app, once it’s running, they typically perform only minimal optimization efforts. The post-release portion of the delivery pipeline is generally neglected.
Why? Because cloud app optimization is extremely complicated. Rather than tweaking the internal machinery of a cloud app so that it consumes only what it needs, teams leave the controls alone and stock up on fuel. That is, for peace of mind they “over-provision” AWS (or other) cloud resources than they need so the cloud app can’t possibly be caught short.
It’s hard to blame them. The complexity of cloud optimization is a very real problem. Even a simple five-container application can have more than 255-trillion resource and basic parameter permutations. As a result most optimization tools focus on code and the app layer (UI, database schema etc.), but don’t go much deeper.
However, software companies developing advanced technology that fully leverages machine learning and deep reinforcement learning can take optimization to the next level. They can embrace a full view of the entire infrastructure: compute, memory, cache, storage, network (bandwidth and latency), thread management, job placement, database config, application runtime, Java garbage collector and more. These tools can monitor parameters such as requests per second, or response time, while tweaking settings like VM instance type, CPU shares, thread count, garbage collection and memory pool sizes. They can ensure they select the right instance, the right number of instances and the right settings in each instance.
Traditional APM (application performance monitoring) tools act at the deployment level and are focused on utilization and cost. They can look at CPU and/or memory usage, but all they really do is utilization monitoring and cost-cutting. But the new wave of cloud optimization tools take in the entirety of application performance, not just the footprint of the elements. They can focus not on cost but on a sophisticated performance metric.
The results of next-gen cloud optimization can be striking. Example: Ancestry have spent two years migrating a database of over 20 million members from data centers into AWS. After implementing cloud optimization, Ancestry.com saw a 50-100 person increase in resource utilization and up to a 50 percent decrease in cost.
Ross Schibler is co-founder and CEO of Opsani, an AIOps and cloud optimization company.
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