Over the last few years, there has been an increase in Kubernetes users as the open-source container orchestration system offers portability, flexibility, multi-cloud capability, and proven ability to increase productivity. Interestingly, Gartner predicts that Kubernetes’ current adoption will surpass 75% in 2022. One reason organizations adopt Kubernetes is to improve efficiency, even while scaling up and down to accommodate changing workloads.
However, it is reported that 94% of all Kubernetes users are also challenged with new and unexpected complexities. According to a recent survey conducted by the Cloud Native Computing Foundation (CNCF), engineers have witnessed a consistent increase in their cloud and Kubernetes-related bills to manage their mission-critical workload. On the other hand, there is an increasing concern about implementing optimization strategies and best practices for their Kubernetes. While developers enjoy the flexibility that Kubernetes offers, it also makes it challenging to optimize the platform effectively. Is Machine Learning the answer to these Kubernetes discrepancies?
Pitfalls in Kubernetes optimization
Over the past decade, businesses have started to deploy cloud-native applications to conduct their everyday business operations. While the pandemic fueled the need for remote business, this phenomenal departure from legacy infrastructure can be attributed to the need for real-time data analysis. Facilitating this growth is the open-source platform Kubernetes which is currently under the radar for its deficiencies in optimization. Research shows that 89% of organizations use Kubernetes as a container orchestration tool in their production or pre-production environments. While Kubernetes offers many advantages when used for large-scale systems, it also has resulted in higher cloud costs and delays in deployment due to its complexity and transparency. This is particularly true for organizations that are in the process of expansion.
Broadly, there are three major challenges — more workload entering Kubernetes results in slower deployments, expanding gap in utilizing data collected into actionable insights, and a lack of cloud-native skilled resources.
To manage Kubernetes environments at scale and prevent the company from falling prey to these upcoming challenges, developers must focus on optimizing applications running on Kubernetes clusters. Optimizing applications for Kubernetes allows developers to efficiently use the codes for CPU and memory to improve performance while minimizing cost and effort. While optimization is possible for clusters, the complexity increases as the number of containers increases. For an organization running their applications at scale on Kubernetes, optimization through manual labor could go beyond the scope as they lack the resources.
This is where artificial intelligence and machine learning come into play. Machine learning can bridge the automation gap and provide insights that can help optimize the Kubernetes environment at an individual level.
Advantages of using AI and ML for Kubernetes Optimization
Machine learning has the potential to answer the challenges in optimizing the Kubernetes platform and offer powerful insights at every level. Developers can take two different ML-based approaches to optimize their Kubernetes platform. The first is a science-based approach where the optimization is conducted based on a series of experimentations to emulate various production scenarios. These experiments are conducted in a non-product environment. With each experiment, the ML engine understands better.
Another type of ML optimization is observations based. In this method, the ML engine is configured to analyze observability data and makes recommendations at intervals. These recommendations can be deployed manually or automatically based on user preference. It is comparatively a simpler model that provides values quicker with minimal effort.
While there is no correct answer to which approach a company should adopt, it is recommended that an experiment-based approach works best for complex applications that need a much deeper analysis. Irrespective of the type of optimization approach, there are several advantages a business will enjoy by using AI and ML. Some of them are –
- Optimizing the Kubernetes environment helps maintain optimal resource utilization at scale, improves resource efficiency, frees up resources for strategic work, and reduces cloud costs.
- By using ML for application behavior analysis, developers can better visualize application behavior across a range of scenarios. This deep application insight can be leveraged to identify cloud-native architecture improvements.
- Kubernetes clusters become more productive by making better use of observability. Machine Learning allows the organization to move from observability to actionability. ML can analyze observability data and find opportunities to minimize cloud resource usage and cost while reducing the risk of CPU throttling or out-of-memory errors.
- Using ML for optimization also creates value in certain business areas, such as scenario planning like a sale or product launch.
StormForge for Kubernetes
As a company committed to building real artificial intelligence that is connected to solving real business problems, StormForge introduced a solution that uses ML to optimize using data – observations with observability data and experimentation with performance-testing data. StormForge has created a patent-pending machine learning (ML) platform model that is purpose-built for Kubernetes that helps organizations analyze existing observability data to recommend real-time configuration changes. The platform lays the foundation to optimize the entire Kubernetes stack, including application, pod, and container. StormForge offers Optimize Live as part of the StormForge platform. Optimize Live leverage the observability data companies are currently collecting to create intelligent optimization within production environments. It draws performance insights on all data collected to inform and optimize cloud-native environments. This solution bridges the gap between preproduction and production optimization, which in turn helps organizations maximize their Kubernetes ROI. According to reports, the platform users have witnessed 40% to 60% cost savings and 30% to 50% performance improvement.
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