Achieving Enhanced Space Efficiency and Crash Resilience in Cloud-based Garbage Collection Systems for Optimized Resource Management

Abstract

For cloud-based apps to remain scalable and performant, effective resource management is essential. High storage costs, resource contention, and system resilience are some of the particular difficulties that garbage collection, a fundamental tool for managing underutilized resources, encounters in cloud systems. In order to maximize resource use in cloud-based systems, this study proposes an enhanced trash collection architecture that improves space efficiency and crash resilience. In order to minimize system downtime and lower memory and storage needs, our method incorporates adaptive garbage collection techniques such object compaction, data deduplication, and incremental cleaning. We implement features like as fault-tolerant replication, transaction logging, and periodic checkpoints to address crash resilience, guaranteeing quick recovery and data integrity in the event of failures. After thorough testing and analysis, our suggested architecture shows notable gains in resilience and space efficiency, resulting in lower memory and storage consumption and faster crash recovery. According to the study, our method offers a solid means to efficiently manage resources in large-scale, multi-tenant cloud applications, opening the door for more durable and reasonably priced cloud infrastructure.