Advanced Performance Analysis of Serverless Java: Best Practices for Full-Stack AI Cloud Applications

EasyChair Preprint 14950

Abstract

As serverless computing continues to evolve, its application in full-stack AI cloud environments demands an advanced performance analysis to optimize both efficiency and scalability. This article delves into the nuanced performance challenges and best practices specific to deploying Java-based serverless solutions for AI-driven cloud applications. By examining the impact of serverless architectures on Java performance, the article provides a comprehensive guide to leveraging advanced monitoring tools, optimizing cold start times, managing memory and execution constraints, and effectively scaling resources. Through empirical analysis and case studies, we highlight strategies for mitigating common performance bottlenecks and achieving cost-effective scalability. The insights presented are aimed at developers, architects, and data scientists seeking to enhance the performance and reliability of serverless Java applications within complex AI cloud ecosystems.

Keyphrases: Applications, Cloud, Ecosystems, Java, constraints, effectively, empirical, resources

@booklet{EasyChair:14950,
  author    = {Wayzman Kolawole},
  title     = {Advanced Performance Analysis of Serverless Java: Best Practices for Full-Stack AI Cloud Applications},
  howpublished = {EasyChair Preprint 14950},
  year      = {EasyChair, 2024}}