Monitoring And Scaling Kubernetes Workloads

Adopting the cloud-native approach can be expensive when you manually scale up and down your resources. Users may also face frequent service failures due to a lack of resources to handle the load.

Monitoring Kubernetes workloads and utilizing an autoscaling option can help solve these challenges.

Why Monitoring And Autoscaling Are Important For Efficient Scaling

Efficient scaling is the ability to handle increased but also decreased workload or demand of the applications of our system. For instance, ecommerce websites witness increased traffic, sales, and order processing during festival seasons compared to regular days.

During these times, a dedicated person must manually allocate and adjust the necessary computing resources for the company to ensure seamless order placements. In such cases, your apps begin to break down as you need to provision more resources to handle the workload or pay excess for resources that have yet to be scaled down when not required.

On the flip side, businesses can monitor their computing resources, understand their workload capacity, and automate the scaling process accordingly.

What Is Kubernetes Monitoring?

Kubernetes monitoring involves reporting mechanisms that enable proactive cluster management. During Kubernetes Monitoring, developers can oversee the utilization of resources such as memory, storage, and CPU, thereby streamlining the management of containerized infrastructure.

As a result, businesses can track performance metrics, get real-time data, and help them take the necessary corrective actions to ensure the maximum uptime of applications. Timely monitoring of Kubernetes resources helps optimize nodes, detect faulty pods, and make scaling decisions.

Why Should You Monitor Kubernetes Workloads?

Simply put, monitoring Kubernetes workloads ensures the performance, security, and availability of apps running on Kubernetes clusters. Issues affecting the cluster and its applications are difficult to spot or identify without monitoring.

Along with cost management, monitoring Kubernetes workloads has other benefits, including scalability, fault detection and troubleshooting, and performance and resource optimization.

What Are The Metrics To Be Monitored?

While two essential resources—nodes and pods—must be monitored, subsets within them must also be considered.

• Cluster nodes
  • There are two types of nodes: control plane and worker nodes. The worker node hosts your application, while the master node controls the operations of worker nodes. These nodes execute their tasks by running multiple resources, such as pods, services, deployments, replicasets, etc.
• Resource utilization
  • This monitors CPU usage, memory usage, disk utilization, network bandwidth, and more.

• Average Load
  • It shows the number of programs running or in the queue to be run on a CPU. Troubleshooting becomes easier when you monitor the load average concerning I/O wait and sys CPU usage.

• Scaling and availability
  • The number of pods decides the number of nodes required to run an application. Therefore, understanding Kubernetes' autoscaling capabilities aids in configuring auto scalars for clusters.

• Application metrics
  • This allows businesses to track metrics specific to applications and their business logic. For example, you can collect data concerning the app's conversion, user experience, latency and throughput, status and response time, and traffic.

What Is Kubernetes Autoscaling?

Autoscaling is one of Kubernetes' core value propositions that helps businesses modify the number of pods in a deployment based on the metrics discussed above. Now, businesses can optimize resources, improve app performance and availability, and maintain cost-efficiency by automatically adjusting the compute resources based on usage patterns and workload demand.

Why Should You Use Kubernetes Autoscaling?

Kubernetes Autoscaling allows apps to adjust their resources per the rising or lowering demands, thereby helping businesses avoid the problem of overprovisioning and underprovisioning computing resources. As a result, businesses ensure the optimal running of apps and resource costs while encountering varying demands.

Types Of Kubernetes Autoscaling

The process of autoscaling begins with the tool Metrics Server. It gathers the pod metrics and exposes those data points using REST APIs. Now, the autoscaling mechanisms fetch the necessary parameters and decide whether to scale up or down the computing resources.

Types of Autoscalers in Kubernetes:

Horizontal Pod Autoscaler (HPA)
Vertical Pod Autoscaler (VPA)