Apr 8, 2021. By S V Aditya
Docker adoption is on the rise as organizations are increasingly moving their applications to cloud-native environments. According to the 2019 Cloud Native Computing Foundation Survey, over 84% of respondents are using containers in production. And Kubernetes has emerged as the clear winner in technology for managing Dockers with over 78% usage for managing Dockers. However, there are challenges and caveats in this Kubernetes success story. In this blog, we discuss how AIOps can help organizations turn these challenges into opportunities and accelerate their transformation.
Challenges in Kubernetes Adoption
Kubernetes has a long list of challenges - and security is at the top of them. In the Diaminti 2019 Container Survey, 32% of respondents (highest) cite security in runtime as their biggest pain point. Hackers have exploited vulnerabilities in Container images and used them for deploying malware or cryptocurrency mining. As the majority of containers are hosted on-premise, this exposes enterprise networks to attackers if process security and RBAC are not handled correctly. This brings us to our second point - building strong security elements and configurations is challenging. It requires deep knowledge of Kubernetes configurations as well as understanding the impact of dependencies between software stacks. Maintaining them and fixing issues during runtime is a struggle even for experts.
Moreover, such experts are rare. Among enterprises investing more than $50,000 in Kubernetes (i.e, most medium to large-scale enterprises), 65% indicate that skill shortage is preventing smooth adoption. Lacking skilled resources, enterprises face infrastructure integration and deployment challenges. When working with legacy apps containing the majority of workloads, this problem is exacerbated and requires enterprises to build skills internally. The delays caused by such activities have restricted most enterprises from scaling up Kubernetes deployments. Most Kubernetes deployments are therefore either on-premise or on a single public cloud. Scaling up Kubernetes into multiple public clouds or onto edge nodes means managing configurations and workloads across all different platforms - another major challenge that is dependent on a skilled talent pool. Finally, there is the issue of monitoring. While it is possible and easy to monitor technical metrics (CPU, memory, the works) in Kubernetes distributions, it does not correlate well to business metrics. Middle managers spend manual work correlating the impact of container workloads and utilization with business results like revenues and customer satisfaction. As you go up the executive chain to a CIO, this information becomes increasingly abstract. They get only a fraction of the visibility to make effective business-guided decisions. These challenges do not even consider cultural changes needed for widespread adoption or bureaucratic difficulties(e.g., internal conflicts in choosing distributions). To accelerate the adoption of containerized technology, organizations need a transformation in their approach to Kubernetes.
AIOps for Kubernetes and Docker
So how can AIOps bring this transformation to the enterprise? AIOps platforms solve several of the cultural and technical challenges in Kubernetes adoption. Take the security issues for a start. As organizational workflows are split across containers, VMs, and bare steel infrastructure, it can get difficult to isolate vulnerabilities and irregularities. AI-based incident recognition and anomaly detection can correlate and process information across all deployments to find the root cause of anomalous behavior. This cuts down on MTTRs when working with Kubernetes in real-time scenarios. As for deployment, AIOps-enabled automation orchestration can handle application configuration and manage Kubernetes Operators. By controlling Operators to manage applications running on Kubernetes, AIOps tools can deliver automated lifecycle management of applications on Kubernetes and deliver secure deployments.
ROI of AIOps on Kubernetes Implementations
AIOps delivers greater observability and governance capability as well. By necessity, AIOps solutions collect metrics and logs across all systems - whether they are containers, VMs, or bare-metal servers. This gives you the observability for better decision-making by consolidating the monitoring stack for IT infrastructure. A governance dashboard built with drill-down capability on top of this data can enable a single pane of glass view of your entire IT infrastructure. Comparing this view with business metrics gives you actionable insight into the RoI of Kubernetes adoption.
Finally, reinforcement learning holds great potential in the space of automation in Kubernetes systems management. AI models trained on Kubernetes audit logs can learn to mimic experts' actions and learn to auto-remediate all routine issues and flag unknown issues for human supervision. This frees up their time to work on more complicated solutions like deploying on multi-cloud environments and edge nodes. By adopting AIOps, enterprises can then generate cost savings in employee hours with automation.
Algomox® AIOps solutions are built keeping the challenges of modern IT environments in mind. The Cognitive Observe Manager and the Cognitive Automation Engine enable greater insight into systems and auto-remediation of incidents. The Cognitive Enterprise Dashboard allows deep-dive analysis of IT infrastructure with the ability for capacity planning. To learn more about Algomox® AIOps, please visit our website.