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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 (2)
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

Advanced topics

This section will explore advanced techniques such as predictive auto-scaling using ML algorithms and integration with cloud-native tools and services, which provide more efficient and intelligent scaling solutions for optimal performance and cost efficiency.

Predictive auto-scaling using ML algorithms

Predictive auto-scaling, a more proactive approach than traditional reactive methods, harnesses ML algorithms to forecast future demand based on historical data and relevant metrics. This allows for optimized resource allocation and improved application performance.

To implement predictive auto-scaling, follow these steps:

  1. Collect and preprocess data: Gather historical metrics such as CPU usage, memory usage, network traffic, and request rates using monitoring tools (e.g., AWS CloudWatch, Google Cloud Monitoring, or Azure Monitor). Cleanse and preprocess this data to handle any missing values, and outliers and ensure consistency.
  2. Train ML models:...
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