How to Avoid Downtime in the Telecom Industry?

The telecommunications sector and communications service providers (CSPs) are undergoing massive operational transformations by leveraging Software Defined Networking (SDN) and Network Functions Virtualization (NFV) to increase network agility while lowering costs. As the next-generation 5G core will be cloud-native from the start, the evolution of NFV infrastructure to serve cloud-native apps is gaining popularity at all major Telecom firms.

Containers are not just being utilized in the core network or for easy software development; they are also deployed on customer premises and at the network’s edge, where low latency, robustness, and portability are critical.

However, considering the number of containers in a Telco application and the complex needs such as load balancing, monitoring, failover, and scalability, managing each container separately and ensuring minimal downtime is becoming impossible. As a result, container management is now handled by a Container Orchestration Engine such as Kubernetes (K8s). Massive infrastructure investments by current and “next-generation” telecom carriers are pushing the transition to Kubernetes, the central container-orchestration technology.

Kubernetes is a free and open-source container-management system that automates application deployment, scaling, and administration. Google created it, and it is now managed by the Cloud Native Computing Foundation (CNCF). It can manage containers on both bare-metal systems and virtual machines. Containers may now be utilized efficiently with private and public clouds such as AWS, GCP, Azure, etc.

The global telecommunications sector is addressing the usage of Kubernetes and cloud-native technologies to increase operational and development efficiency. Open Networking Automation Platform (ONAP), SDN Enabled Broadband Access, and other open-source projects (SEBA).

So, why use Kubernetes?

Kubernetes is an open-source framework for managing containerized workloads and portable and adaptable services. It maintains containerized applications by supplying functionality for robustly running distributed systems. Here are some of its features:

  • Self-healing: Kubernetes restarts failing containers, replaces containers, destroys containers that fail to react to user-defined health checks, and does not broadcast them to clients until they are ready for usage.
  • Resources management: Kubernetes allows you to determine how much CPU and memory (RAM) each container requires. Kubernetes can make better judgments about how to manage container resources.
  • Horizontal autoscaling: Kubernetes auto scalers automatically scale an application up or down based on resource utilization (within defined limits).
  • Service discovery and load balancing: Kubernetes may expose a container using its DNS name or IP address. If there is a lot of traffic to a container, Kubernetes may load balance and spread the network traffic to keep the deployment stable.
  • Storage orchestration: Kubernetes allows you to automatically mount your preferred storage systems, such as local storage, public cloud providers, and others.
  •  Rollouts and rollbacks: You may specify the ideal state for your deployed containers. It will transform the actual state into the desired shape at a regulated rate. 

How does Kubernetes work?

A Kubernetes cluster orchestration system comprises at least one cluster master and several worker computers known as nodes. When you engage with K8s, you are connecting with the Kubernetes master of your cluster. The K8s primary node keeps your cluster in the best possible shape, while the worker nodes in a cluster are the machines (VMs, physical servers, etc.) that run your apps and cloud operations.

The K8s Master node is made up of several parts, including:

  • Kube-apiserver: The Kubernetes API is exposed. It is the Kubernetes control plane’s front-end. All REST (Representational State Transfer) queries are received by the central management entity.
  • Etcd: A basic, distributed key-value storage system used to store Kubernetes cluster data, API objects, and service discovery information.
  • Kube-controller-manager: Runs multiple controller processes in the background to manage the cluster’s shared state and execute basic operations. When a change happens in a service configuration, the controller detects it and begins working toward the new target state.
  • Kube-scheduler: Watches freshly formed pods with no node assigned and chooses a node for them to run on depending on resource use. It reads the operational needs of the service and schedules it on the best-fitting node.

The K8s Workers are made up of three parts:

  • Kublet: An agent that runs on each worker node in the cluster to ensure that pods and containers are healthy and properly functioning.
  • Kube-proxy: A service that runs on each worker node to handle individual host subnetting and expose services to the outside world. It routes requests to the appropriate pods/containers across a cluster’s segregated networks.
  • Container runtime: To execute containerized programs, use a container engine such as Docker. Kubernetes has multiple abstractions that reflect your system’s state: deployed containerized apps and workloads, their related network and storage resources, and other information about what your cluster is doing. These abstractions are represented in the Kubernetes API through objects such as Pod, Service, Volume, Namespace, ReplicaSet, Deployment, etc.

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