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

Strategic best practices – deploying and scaling microservices

When designing, deploying, and scaling microservices in a cloud environment, it’s essential to utilize cloud-native services and architectures to maximize performance, scalability, and reliability.

Here’s a straightforward guide on best practices, tailored for developers and architects:

Load balancing:
- Purpose: Distribute incoming traffic evenly across multiple microservice instances to enhance reliability and availability
- How to implement:
  - Use cloud-managed load balancers such as AWS Elastic Load Balancing (ELB), Azure Load Balancer, or Google Cloud Load Balancing, which can automatically adjust to traffic demands
  - Integrate service discovery tools (e.g., AWS Cloud Map, Azure Service Discovery, or Google Cloud Service Directory) to dynamically manage service instances
Caching solutions
- Purpose: Reduce database load and speed up response times by caching frequently accessed data
- How to implement...