You're reading from Java Concurrency and Parallelism Master advanced Java techniques for cloud-based applications through concurrency and parallelism

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781805129264

Length 496 pages

Edition 1st Edition

Languages

Java

Tools

Boost

Concepts

Application Development

Author (1):

Jay Wang

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Table of Contents (20) Chapters

Preface

1. Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing

2. Chapter 1: Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape FREE CHAPTER

3. Chapter 2: Introduction to Java’s Concurrency Foundations: Threads, Processes, and Beyond

4. Chapter 3: Mastering Parallelism in Java

5. Chapter 4: Java Concurrency Utilities and Testing in the Cloud Era

6. Chapter 5: Mastering Concurrency Patterns in Cloud Computing

7. Part 2: Java's Concurrency in Specialized Domains

8. Chapter 6: Java and Big Data – a Collaborative Odyssey

9. Chapter 7: Concurrency in Java for Machine Learning

10. Chapter 8: Microservices in the Cloud and Java’s Concurrency

11. Chapter 9: Serverless Computing and Java’s Concurrent Capabilities

12. Part 3: Mastering Concurrency in the Cloud – The Final Frontier

13. Chapter 10: Synchronizing Java’s Concurrency with Cloud Auto-Scaling Dynamics

14. Chapter 11: Advanced Java Concurrency Practices in Cloud Computing

15. Chapter 12: The Horizon Ahead

16. Index

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Appendix A: Setting up a Cloud-Native Java Environment

1. Appendix B: Resources and Further Reading

Fundamentals of cloud auto-scaling – mechanisms and motivations

In the ever-evolving landscape of cloud computing, auto-scaling has emerged as a pivotal feature, enabling applications to dynamically adjust their resources to meet varying demands. This section delves into the core concepts and advantages of cloud auto-scaling, providing a comprehensive understanding of how it enhances scalability, cost-effectiveness, and resource utilization.

Definition and core concepts

Cloud auto-scaling automatically adjusts the amount of computational resources in a server farm based on CPU, memory, and network usage, ensuring optimal performance and cost efficiency. Dynamic resource allocation is a key concept where resources are added or removed based on real-time demand. Scaling can be done vertically (scaling up/down) by adjusting the capacity of existing instances, or horizontally (scaling out/in) by adding or removing instances to handle changes in workload.

Auto-scaling relies...