AI-Enhanced Predictive Systems for Thread Deadlock Resolution: Early Detection and Prevention in Cloud Applications

EasyChair Preprint 14955

Abstract

In the realm of cloud applications, thread deadlocks pose a significant challenge, impacting system performance and reliability. Traditional methods for detecting and resolving deadlocks often fall short in dynamic and scalable cloud environments. This article presents an advanced framework for AI-enhanced predictive systems aimed at early detection and prevention of thread deadlocks. By leveraging machine learning algorithms and real-time data analytics, the proposed system predicts potential deadlock scenarios before they escalate into critical issues. The framework integrates with cloud-based applications to monitor thread interactions, identify patterns indicative of impending deadlocks, and recommend preemptive actions. Through extensive simulations and real-world case studies, we demonstrate the effectiveness of our approach in reducing the incidence of deadlocks and improving overall application stability. This research contributes to the development of more resilient cloud systems by offering a proactive solution to one of the most challenging aspects of concurrent computing.

Keyphrases: Cloud, Computing., Resilient, concurrent, development, research, systems

@booklet{EasyChair:14955,
  author    = {Joseph Oluwaseyi},
  title     = {AI-Enhanced Predictive Systems for Thread Deadlock Resolution: Early Detection and Prevention in Cloud Applications},
  howpublished = {EasyChair Preprint 14955},
  year      = {EasyChair, 2024}}