Introduction to Kubernetes


Kubernetes, also known as K8s or Kube, is an open-source system that helps organizations manage cloud-based containerized applications. The system helps with deploying, scaling, and managing containers that are part of a vast distributed ecosystem of cloud applications and storage.

Another advantage of Kubernetes is the ability to automate several operations and processes involved in the container life cycle.

Significant features of Kubernetes include:

  •     Assigning and designating storage as local or cloud-based
  •     Managing workloads through batch execution
  •     Scaling the system as and when it is required
  •     Managing and updating secrets and application configurations

Besides the main functionality, additional robust features help provide a full end-to-end solution for the management of distributed systems. As a readily available open-source tool, Kubernetes can be found in many different computing environments from academia to small startup companies and even large multinational corporations.  

Let’s take a deeper dive into Kubernetes specifics such as clusters, the benefits of K8s, the challenges companies face monitoring and managing their configurations, and how AI monitoring solutions can help solve these issues.

What are Kubernetes Clusters?


Kubernetes clusters are used for managing containers. These clusters help develop an app, its dependencies, and the resources required in a single container. This way, applications can be more easily developed, managed, and scaled. They also allow the containers to run across different environments such as on-premise, cloud, and virtual environments and ensure interoperability across disparate operating systems.

Kubernetes clusters are actually a set of nodes categorized into master nodes and worker nodes. Every cluster needs to have at least one of each. While the master node is meant to control the state of a cluster, the worker nodes perform the operations and tasks that comprise the application.

Essentially, the master node schedules the operations, monitors operational status, and updates that status once the operations are completed. It is according to these instructions that the worker nodes perform those operations as scheduled by the master node.

Components of a Kubernetes Cluster


A Kubernetes cluster consists of six major components:

API Server is the front end and acts as the interface between various Kubernetes resources.

The Scheduler is used for assigning operations, defining the resources required by each node and finding nodes for unassigned Pods.

The Controller Manager is responsible for maintaining the states of operation, managing endpoints, and the node controllers.

Kubelet ensures that containers located on Pods are fully functional through an interface with the Docker Engine responsible for container creation.

The Kube Proxy is used to manage network connectivity and ensures that all the nodes are following the network rules at any given time.

Finally, Etcd is the component that stores all the cluster data.

How Kubernetes Clusters Work


First, the operational state is determined, by specifying the application type and the number of replicas required. With the help of JASON or YAML files, these specs are stored and conveyed to the clusters using an API or command-line interface.

The master node then conveys this information to the worker nodes, and with the help of the control plane, the clusters automatically align themselves to achieve the desired operational state. Overall, these operations are managed by the control plane.

Many operations can be automated in Kubernetes clusters, with the help of a Pod Lifecycle Event Generator or PLEG. These operations include starting containers, adjusting their replicas, validating container images, launching and managing containers, and implementing updates/rollbacks.

Benefits of K8s


Scaling your distributed systems becomes exponentially simplified when using Kubernetes. This is mainly due to the automation of major tasks to the extent that only a few instructions are required to scale as required with complete monitoring of the entire process. 


Kubernetes is interoperable with any runtime container operating in multiple computing environments, this is what makes it so flexible, enabling quick and easy deployments even across large organizations.


Another significant advantage is the portability of Kubernetes, allowing organizations to shift from on-premises to cloud or vice versa at any time. All this with high efficiency and no downtime. 


Employees can be highly productive as K8s automatically takes care of container operations, so network managers can focus on gaining greater efficiency through Kubernetes’ automation resulting in significant savings of time, energy, and resources.


Challenges of Monitoring Kubernetes

Vast and Intricate Distributed Ecosystem

The Kubernetes network is highly complex with numerous containers that are dynamic in nature. This makes it quite difficult to monitor and ensure operational efficiency for all containers at the same time.

Identification of Glitches, Behavioral Patterns and Anomalies

Since the network is vast, identification of anomalies takes time from their identification until they are reported to the central system. This is because each individual container must be checked to determine where the problem originated. Unfortunately, by the time it is detected, the problem may have evolved into something worse or disappeared entirely.

Besides initial detection, understanding the behavioral patterns of the containers and identifying the anomalies can also be extremely challenging and time-consuming. This is especially true when considering that it is extremely labor-intensive, requiring skilled teams to analyze large amounts of data, from various operational perspectives, and then come to the proper conclusions of how to solve the problem.

Delayed Responses to Initiation of Recovery Measures

When identification of the glitch is not immediate and analysis of the problem takes time, then inevitably recovery measures would consequently also be delayed. Even if the issue was identified early, to get the recovery measures in action, a significant amount of time would still be required to have the affected containers reach the desired state of operation, allocate them the proper resources and perform the required tasks. 

24×7 Monitoring Requirements

One way to meet this challenge is through providing enough network management manpower to constantly monitor the Kubernetes system. This solution is highly inefficient both the overheads required to manage and coordinate such a large team and of cost the direct and indirect costs required by such an operation.

How AI Monitoring Can Solve K8s Challenges


Due to the dynamic and complex nature of Kubernetes, AI-based solutions are the best choice for comprehensive monitoring of Kubernetes by providing the following benefits:

Automated Monitoring

AI can leverage unsupervised machine learning to monitor 100% of Kubernetes data. This means the complete process is automated and doesn’t require constant human intervention with the system.

Real-Time Glitch Detection

Since AI solutions can monitor the network 24×7, anomalies can essentially be detected in real-time and proactively solutions can be suggested that potentially solve a problem shortly after it is detected. In this manner, negative effects are minimized and in most cases, a full recovery can be achieved almost immediately.

Calculating the Cost of Resources

Kubernetes workloads are dynamic, which poses a challenge when it comes to calculating the cost of resources utilized by your applications correctly. AI Monitoring optimizes cloud cost management by providing real-time visibility and contextual understanding of Kubernetes spend. 

Understanding of Seasonality Patterns

Another advantage of a machine learning algorithm is its ability to analyze the behavioral pattern of containers over time. As a result, false positives and false negatives can be significantly reduced by taking into account seasonal fluctuations, including different geographies at different hours of the day.

Root Cause Analysis and Identification of Problems

The Kubernetes system also correlates the anomalies, various event occurrences, and metrics at a particular time to understand the factors that caused a problem in the system. It does so by understanding the initial metrics and correlating them with other factors simultaneously to give the company a holistic picture of the incident.

Now that you have a basic understanding of Kubernetes, its advantages and challenges, it is clear that leveraging the latest AI technologies is the best way to monitor your Kubernetes operations.

Anodot for Cloud Cost gives organizations granular visibility into their Kubernetes costs, down to the node and pod level. Anodot combines Kubernetes costs with non-containerized costs and business metrics for an accurate view of how much it costs to run a microservice, feature, or application. 

Written by Anodot

Anodot leads in Autonomous Business Monitoring, offering real-time incident detection and innovative cloud cost management solutions with a primary focus on partnerships and MSP collaboration. Our machine learning platform not only identifies business incidents promptly but also optimizes cloud resources, reducing waste. By reducing alert noise by up to 95 percent and slashing time to detection by as much as 80 percent, Anodot has helped customers recover millions in time and revenue.

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