Explore OpenStack’s flexibility and control for designing and deploying robust and scalable cloud infrastructures, which makes it one of the largest open-source communities in the cloud market. This book delves deep into the OpenStack architecture, dissecting each component to guide you in architecting your cloud with precision. From essential components to cutting-edge services, this book offers a step-by-step approach, ensuring you grasp the fundamentals before exploring the latest advancements.
This updated edition guides you through the deployment process, integrating secure best practices inspired by the DevSecOps philosophy. You’ll also explore the Antelope release, covering new services such as container management and software-defined networking (SDN). The book outlines best practices for running and managing fault tolerant, secure, monitored, and high-performing setups and in the last part, navigates the convergence of public and private clouds, covering hybrid models through use cases of managing Kubernetes-based applications in OpenStack private and public clouds.
By the end of the book, you’ll be well-versed in the latest OpenStack advancements, ready to lead your organization on a successful cloud journey.

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Spis treści

Mastering OpenStack. Implement the latest techniques for designing and deploying an operational, production-ready private cloud - Third Edition eBook -- spis treści

• 1. Revisiting OpenStack – Design Considerations
• 2. Kicking Off the OpenStack Setup – The Right Way (DevSecOps)
• 3. OpenStack Control Plane - Shared Services
• 4. OpenStack Compute – Compute Capacity and Flavors
• 5. OpenStack Storage - Block, Object, and File Shares
• 6. OpenStack Networking - Connectivity and Managed Service Options
• 7. Running a Highly Available Cloud – Meeting the SLA
• 8. Monitoring and Logging – Remediating Proactively
• 9. Benchmarking the Infrastructure – Evaluating Resource Capacity and Optimization
• 10. OpenStack Hybrid Cloud - Design Patterns
• 11. A Hybrid Cloud Hyperscale Use Case - Scaling a Kubernetes Workload