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Java Concurrency and Parallelism

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

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781805129264
Length 496 pages
Edition 1st Edition
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Author (1):
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Jay Wang Jay Wang
Author Profile Icon Jay Wang
Jay Wang
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Table of Contents (20) Chapters Close

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 17. Other Books You May Enjoy Appendix A: Setting up a Cloud-Native Java Environment 1. Appendix B: Resources and Further Reading

Java’s concurrency models – alignment with scaling strategies

Java’s concurrency models offer powerful tools that align with auto-scaling strategies, enabling applications to dynamically adjust resource allocation based on real-time demand. Let’s explore how Java’s concurrency utilities support auto-scaling.

ExecutorService efficiently manages thread pools, allowing dynamic adjustment of active threads to match the workload. CompletableFuture enables asynchronous programming, facilitating non-blocking operations that scale with demand. Parallel streams harness the power of multiple CPU cores to process data streams in parallel, enhancing performance.

To demonstrate the practical application of these concurrency tools in auto-scaling, let’s walk through a simple example. We will implement an auto-scaling solution that dynamically adjusts the number of worker threads based on the load:

  1. Set up ExecutorService:
    ExecutorService executorService...
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