Book Image

Migrating Linux to Microsoft Azure

By : Rithin Skaria, Toni Willberg
Book Image

Migrating Linux to Microsoft Azure

By: Rithin Skaria, Toni Willberg

Overview of this book

With cloud adoption at the core of digital transformation for organizations, there has been a significant demand for deploying and hosting enterprise business workloads in the cloud. Migrating Linux to Microsoft Azure offers a wealth of actionable insights into deploying Linux workload to Azure. You'll begin by learning about the history of IT, operating systems, Unix, Linux, and Windows before moving on to look at the cloud and what things were like before virtualization. This will help anyone new to Linux become familiar with the terms used throughout the book. You'll then explore popular Linux distributions, including RHEL 7, RHEL 8, SLES, Ubuntu Pro, CentOS 7, and more. As you progress, you'll cover the technical details of Linux workloads such as LAMP, Java, and SAP, and understand how to assess your current environment and prepare for your migration to Azure through cloud governance and operations planning. Finally, you'll go through the execution of a real-world migration project and learn how to analyze and debug some common problems that Linux on Azure users may encounter. By the end of this Linux book, you'll be proficient at performing an effective migration of Linux workloads to Azure for your organization.
Table of Contents (8 chapters)

Challenges in on-premises infrastructure

Hosting an infrastructure on-premises is quite challenging because of the requirement for qualified personnel and complex networking. The traditional approach has persisted for a long time. With the introduction of cloud computing, organizations started to recognize how the challenges they faced for decades could be resolved by cloud computing. Before we take a look at the benefits of cloud computing, let's understand the root cause of these on-premises challenges:

  • Scaling: This is one of the primary challenges. It is really hard to implement a solution that can scale in and out based on varying traffic. You can add more servers (physical or virtual) whenever there is a need for more resources and terminate them when they are no longer needed. However, resource utilization in this scenario is not optimized. With the introduction of the cloud, scaling is very easy; you just have to specify the scaling conditions (CPU %, memory %...