Book Image

Multi-Cloud Architecture and Governance

By : Jeroen Mulder
Book Image

Multi-Cloud Architecture and Governance

By: Jeroen Mulder

Overview of this book

Multi-cloud has emerged as one of the top cloud computing trends, with businesses wanting to reduce their reliance on only one vendor. But when organizations shift to multiple cloud services without a clear strategy, they may face certain difficulties, in terms of how to stay in control, how to keep all the different components secure, and how to execute the cross-cloud development of applications. This book combines best practices from different cloud adoption frameworks to help you find solutions to these problems. With step-by-step explanations of essential concepts and practical examples, you’ll begin by planning the foundation, creating the architecture, designing the governance model, and implementing tools, processes, and technologies to manage multi-cloud environments. You’ll then discover how to design workload environments using different cloud propositions, understand how to optimize the use of these cloud technologies, and automate and monitor the environments. As you advance, you’ll delve into multi-cloud governance, defining clear demarcation models and management processes. Finally, you’ll learn about managing identities in multi-cloud: who’s doing what, why, when, and where. By the end of this book, you’ll be able to create, implement, and manage multi-cloud architectures with confidence
Table of Contents (28 chapters)
1
Section 1 – Introduction to Architecture and Governance for Multi-Cloud Environments
7
Section 2 – Getting the Basics Right with BaseOps
12
Section 3 – Cost Control in Multi-Cloud with FinOps
17
Section 4 – Security Control in Multi-Cloud with SecOps
22
Section 5 – Structured Development on Multi-Cloud Environments with DevOps

Optimizing cloud environments using AIOps

The two major benefits of AIOps are first, the speed and accuracy in detecting anomalies and responding to them without human intervention. Second, AIOps can be used for capacity optimization. Most cloud providers offer some form of scale-out/-up mechanism driven by metrics, already available natively within the platform. AIOps can optimize this scaling since it knows what thresholds are required to do this, whereas the cloud provider requires engineers to define and hardcode it. Since the system is learning, it can help in predicting when and what resources are needed. The following diagram shows the evaluation of operations, from descriptive to prescriptive. Most monitoring tools are descriptive, whereas AIOps is predictive:

Figure 19.1 – Evolution of monitoring to AIOps

Monitoring simply registers what's happening. With log analytics, companies can set a diagnosis of events and take remediation actions...