Rapid Recover Map Reduce (RR-MR): Boosting failure recovery in Big Data applications

Abstract

The rapid growth of Big Data applications has brought forth unprecedented opportunities for insights and innovation, but it has also exposed the inherent vulnerabilities of data processing pipelines to failures. Hardware glitches, software anomalies, and network interruptions can disrupt the smooth execution of critical tasks, leading to extended downtimes, compromised reliability, and increased operational costs. In response to these challenges, we introduce Rapid Recover Map Reduce (RR-MR), an innovative framework designed to revolutionize failure recovery mechanisms within the context of Big Data applications.  RR-MR addresses the shortcomings of conventional Map Reduce frameworks by presenting a novel approach to failure recovery that focuses on expeditious restoration of processing tasks. By leveraging advancements in distributed systems, fault tolerance, and parallel processing techniques, RR-MR introduces a multi-faceted strategy that enhances both the efficiency and reliability of recovery processes.

URN:NBN:sciencein.jist.2024.v12.773