Kubernetes Resource Management

Managing K8s Resources using AIOps

AIOps is the application of artificial intelligence to IT operations. With AIOps, Ops teams can leverage data that would otherwise be intractable. Container orchestration (e.g., Kubernetes) has become the heart of the DevOps environment. Kaiops is developing and applying innovative AIOps frameworks for Kubernetes to provide capacity management, event monitoring, and alerting/remediation services for container-based deployments. Our solution works to reduce system failures, optimize resource utilization, and ease the burdens placed upon DevOps and ITOps engineers. Our solution utilizes proprietary Machine Learning models and algorithms that are designed to efficiently learn from IT system data in a unified manner.


Kaiops helps reduce costs by efficient resource management

Machine learning can provide resource autoscaling for optimized cluster performance and capacity. Specific implementations include:

a. Vertical Scaling: Node instance type and node count scaling for decreased cluster resource cost.

b. Horizontal Scaling: Replica set scaling for improved service response.

c. Pod De-scheduling: Replica set placement for improved service resilience.


Helps reduce costs of IT operations

AIOps enables IT organizations to take cost out of their operations. By boosting efficiency, reducing escalations, slashing downtime, eliminating, or shortening bridge calls, flattening headcount, reducing SLA penalties, and consolidating tools, AIOps customers can reduce operating costs by up to 50%.


What’s driving the cost of IT operations up?

Rising IT complexity. Too many monitoring tools. Runaway headcount growth. Hundreds of critical apps and services. All of them rely on your IT Ops and NOC teams to keep them healthy, and your customers happy. But your team is held back by legacy IT Ops tools, and your enterprise is paying the price.


Automate workflows

Manual incident management is slow, expensive, and difficult to scale to meet the demands of modern IT environments. Consequently, IT executives are forced to grow headcount. AIOps can automate incident management steps so that IT Operations teams don’t waste time on time-consuming manual tasks, including adding business context in real-time, automating ticket creation and routing, automatically sharing ticket updates with chat and notification tools, and running automated custom workflows.


Manage Escalations

Your Level-1 (L1) first response teams can’t effectively handle the growing tide of incidents. So they escalate almost everything to your expensive Level-2 (L2) and Level-3 (L3) teams and DevOps teams, making your costs skyrocket. AIOps leverages ML to bubble up operational and business context and makes it easily available to L1 operators. This includes information such as root cause, severity, relevant runbooks, potential customer impact and more. This dramatically boosts L1 resolution rates and reduces the number of times L3s, DevOps and other teams get pulled into incident handling.


Reduce downtime

Many IT Operation teams can’t handle the growing tide of incidents in a timely manner, resulting in prolonged outages and downtime. Organizations have to endure SLA penalties, damage to brand equity and frequently, lost revenue. By using ML to correlate alerts, changes and topology data, AIOps detects incidents as they start to form and before they escalate into outages. This reduces the frequency and impact of outages that affect critical revenue-generating applications and services. When outages do occur, AIOps surfaces the root cause and routes them to the right teams, for rapid resolution.


Reduce MTTR

AIOps uses ML to surface the probable root cause of incidents, including changes that cause them as well as low-level hardware and network issues. AIOps puts probable root cause at the IT Ops teams’ fingertips. This eliminates the need for long, expensive bridge calls because teams can now rapidly resolve incidents and outages.


Keep headcount in check

Event Correlation and Automation features reduces the alert volume by more than 95% and automates repetitive operator tasks. This enables IT Ops teams to cope with the growing volume of data and handle a much larger volume of incidents without having to grow headcount.