Autoscaling enables cloud users to change compute requirements by dynamically adjusting resource utilization consumption as demand increases or decreases. Autoscaling is an important capability for users because it enables them to enjoy the extremely elastic and scalable nature of the cloud, reduce waste, and optimize their spend.
However, a study conducted at the company I work for, Pepperdata, found that, instead of reducing their operational spend, one-third of enterprises that moved to the cloud are overspending on their budgets by up to 40%.Why is this so?
Many organizations learn the hard way that, for all of the cloud’s great benefits, use that is not optimized typically leads to runaway costs for the unprepared. Cloud services providers offer autoscaling capabilities to help users reduce resource wastage and better manage costs, but many of these services have drawbacks.
The great shift from on-premises IT infrastructure to the cloud is driven by many factors, but cost savings is paramount. With the promise of lower costs in the cloud and a successful migration, many enterprises are shocked to see those first few cloud bills.
Statista ranks “cloud spend management” and “lack of resources” as two of the most important cloud migration challenges. Both problems stem from unexpected costs.
One of the main drivers for runaway costs in the cloud is the shift from a CapEx model to an OpEx model. Enterprises don't find it difficult to determine and assign budgets with a CapEx approach. But with OpEx, the constantly shifting requirements can make it extremely difficult to identify and manage spend.
Given the cloud's flexibility and scalability, the user's access to infinite resources, and the lack of a cloud-based spending governance framework, this is a recipe for overspending.
Autoscaling to the rescue?
Cloud service providers tell you that the best way to cut cloud spend is by autoscaling because it ensures adequate compute resources for big data stacks when traffic surges. Looking at the bigger picture, enterprises also benefit from autoscaling because it eliminates the need to add more compute resources manually when the need arises.
Many cloud infrastructures overprovision compute resources based on consumption and requirements at peak levels. Although this approach guarantees that compute resources are available once traffic increases, it also means that substantial amounts of unused compute are wasted when traffic is much less than predicted.
Cloud computing and big data
Cloud computing has an extensive list of benefits, especially as it pertains to big data. Businesses generate large volumes of data daily, and the overall volume is set to grow dramatically. Statista predicts big data volume to grow from 13.6 zettabytes in 2019 to over 79 zettabytes by 2025.
Enterprises migrated to a cloud-based infrastructure to help achieve scalability and speed up their modernization. With cloud computing, it’s possible to scale existing applications and processes and modernize workloads. Cloud computing enables enterprises to harness big data by enabling them to process large volumes of information and derive top-quality, actionable insights they need to perform tasks at peak levels, such as accelerating product development, achieving redundancy and resiliency, and, if done right, reduce costs.
As reliance on cloud infrastructure and big data grows, so too does the need for enterprises to find a better way to autoscale their cloud processes, cut down their costs, and maximize their cloud investments.
Observability and automation
Autoscaling is often implemented based on the vendor's default configurations, which don’t factor in SLAs, user performance requirements, or cost concerns. Without visibility into their big data stacks in the cloud, it’s impossible for enterprises to optimize their IT infrastructure, quickly troubleshoot and resolve problems, or reduce downtime.
This is where observability and automation are at their best.
Powered by automation, big data observability enables users to gather actionable data that provides not only the when of an error or issue but also the why. Observability helps DevOps and IT teams collect and analyze real-time information from different sources, including various performance monitoring solutions and logs. This results in a comprehensive, correlated view of their big data stacks and IT environments that allows them to immediately pinpoint issues and troubleshoot at the root level.
But what is really key is automation that allows you to optimize your workloads and eliminate or significantly reduce manual tuning efforts. Let’s face it — manually tuning scaling requirements to the most optimal configurations is beyond human capability. Automation makes it possible to analyze and make thousands of decisions per second to optimize CPU, memory, and I/O resources as well as how the workloads interact with those resources.
As you help your organization scale your cloud-based apps and processes, meet SLAs, and achieve your business goals, managing your cloud budget can seem a daunting task.
To get the maximum benefits of autoscaling and reduce your cloud spend, complement cloud autoscaling with observability and automation. This combination is the most effective solution to reducing waste and managing runaway costs in the cloud.