Utilizing Probability Distribution for Selecting Optimal and Minimal Replicas to Achieving Fault Tolerance in a Distributed System

Abstract

This paper highlights the importance of efficient task distribution and robust fault tolerance in network systems. It emphasizes the limitations of relying on a fixed resource quantity and proposes task replication as a solution to improve data availability. The introduced algorithm dynamically determines the optimal number of replicas based on network history, response time, and joint probability of successful servers, aiming to minimize task failure rates. The algorithm's advancements in grid scheduling lie in optimal resource management, fault-aware job placement, adaptability to changing conditions, and efficient fault tolerance through redundancy planning. The algorithm outperforms three other algorithms, showcasing significant enhancements.