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[Software] Automating telecom software deployment with GitOps


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A declarative, GitOps-based approach to software delivery and deployment enables communication service providers to increase automation and speed up the introduction of new features and updates including security fixes into their networks.


A declarative, GitOps-based approach to software delivery and deployment enables communication service providers to increase automation and speed up the introduction of new features and updates including security fixes into their networks.

An always-up-to-date software (SW) base will be a huge advantage for communication service providers (CSPs) by making it possible to rapidly introduce new features and updates that open up new business opportunities, improve efficiency and proactively address security threats. To make it happen, greater automation of SW delivery and operational processes will be essential.

Current telecom SW delivery and operation processes in which even the smallest updates must undergo the same semi-frequent, complex processes as large packages that deliver new functionality are simply too complicated, manual and time-consuming to support an always-up-to-date SW base. To ensure efficient delivery and deployment of network and operations support systems/business support systems (OSS/BSS) functions in the future, CSPs need to evolve their processes and tools to fit the new application architectures and incorporate the use of new technologies.

At Ericsson, we believe that the most effective approach to achieving the necessary automation of telecom SW delivery and operation processes is by adopting continuous integration/continuous deployment (CI/CD) best practices for SW delivery pipelines. Further, we recommend a microservice-based architecture of componentized cloud-native network functions (CNFs), which enables more targeted SW modifications, while also making it possible to maintain a larger number of SW artifacts. Evolving the application architecture and the underlying platform in this way also opens up the opportunity to use cloud-native best practices for life-cycle management (LCM).

With a DevOps mindset as our starting point, the automation efforts we present in this article focus on the implementation of a continuous SW flow and the creation of feedback loops between organizations that develop SW and those that operate it. Our concept separates LCM on the functional and realization domains and introduces new automation on top of the declarative deployment functionality, which makes it possible to separate the concerns between the network function (NF) operations and their realization. This approach is already widely adopted in various IT domains and ecosystems and therefore provides a solid baseline for adoption in the telecom industry. 

As an emerging best practice, DevOps is starting to replace existing processes. During the past decade, there has been a technology evolution among CSPs from physical NFs toward virtual NFs, CNFs and cloud-native applications (CNAs). This has brought with it new LCM characteristics that have enabled and driven a coevolution from existing ways of working to new practices. Most notably, development teams are now able to observe SW performance in live systems and quickly update CNAs in the case of unwanted behavior.

When CNAs are developed according to the 12-factor application principles [3], microservice in-service SW updates and the LCM of the underlying Kubernetes cluster and infrastructure do not impact the service provided by the running NFs or applications. This is because, in the cloud-native paradigm, a product is composed of microservices that each have their own independent life cycles. As a logical consequence of this, the life cycle of the CNA becomes decoupled from the life cycle of the service it provides. The service will continue while the underlying deployed SW can evolve and change.

Each microservice in a CNA is represented by a number of accompanying artifacts such as the container image, helm charts, Flux manifests and a deployment configuration in the form of values.yaml. These artifacts make it possible to deploy a microservice initially as part of a CNF. The same mechanism can be applied to post-deployment configuration, which is commonly referred to as Day-1/n configuration. The relationships and customizations of these artifacts can be formulated in a declarative description that explains what should be deployed in a Kubernetes cluster.


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