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Java Concurrency and Parallelism

Java Concurrency and Parallelism

By : Jay Wang
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Java Concurrency and Parallelism

Java Concurrency and Parallelism

5 (1)
By: Jay Wang

Overview of this book

If you’re a software developer, architect, or systems engineer, exploring Java’s concurrency utilities and synchronization in the cloud, this book is an essential resource. Tech visionary Jay Wang, with over three decades of experience transforming industry giants, brings unparalleled expertise to guide you through Java’s concurrency and parallel processing in cloud computing. This comprehensive book starts by establishing the foundational concepts of concurrency and parallelism, vital for cloud-native development, and gives you a complete overview, highlighting challenges and best practices. Wang expertly demonstrates Java’s role in big data, machine learning, microservices, and serverless computing, shedding light on how Java’s tools are effectively utilized in these domains. Complete with practical examples and insights, this book bridges theory with real-world applications, ensuring a holistic understanding of Java in cloud-based scenarios. You’ll navigate advanced topics, such as synchronizing Java’s concurrency with cloud auto-scaling and GPU computing, and be equipped with the skills and foresight to tackle upcoming trends in cloud technology. This book serves as your roadmap to innovation and excellence in Java cloud applications, giving you in-depth knowledge and hands-on practice for mastering Java in the cloud era.
Table of Contents (20 chapters)
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Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing
7
Part 2: Java's Concurrency in Specialized Domains
12
Part 3: Mastering Concurrency in the Cloud – The Final Frontier

Summary

In this chapter, we explored the synchronization of Java’s concurrency models with cloud auto-scaling dynamics. We delved into the fundamentals of cloud auto-scaling, examining how Java’s concurrency tools can be leveraged to optimize applications for scalability. Key discussions included best practices for enhancing Java application performance, monitoring and managing Java processes during auto-scaling events, and real-world case studies from industry leaders such as Netflix and LinkedIn.

We also walked through a practical project that demonstrated the deployment and management of a scalable Java-based real-time analytics platform using AWS services and Docker. Advanced topics such as predictive auto-scaling using ML and the integration of Java applications with cloud-native tools such as Kubernetes, Istio, and AWS SAM were covered to provide a comprehensive understanding of modern scaling solutions.

The skills and knowledge gained from this chapter are...

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