A Fault-Tolerant Framework for Improving Reliability in Distributed Cloud Systems
Keywords:
Checkpointing, Cloud computing, Distributed cloud systems, Failure detection, Fault tolerance, Lightweight framework, Recovery mechanism, Reliability, Replication, Task schedulingAbstract
Distributed cloud systems, which offer scalable and on-demand services to consumers globally, have emerged as the foundation of contemporary computer infrastructures. However, the availability and dependability of services can be severely impacted by these systems’ high susceptibility to hardware malfunctions, network outages, and node breakdowns. Although they increase system resilience, traditional fault-tolerance techniques like replication and checkpointing frequently result in higher computing overhead, storage costs, and system complexity. Hence, a lightweight fault-tolerant framework is presented in this paper to reduce resource overhead and enhance dependability in distributed cloud settings. To ensure service continuity in the event of node failures, the suggested model integrates effective recovery mechanisms, adaptive task reallocation, and failure detection. To assess system performance in the event of a failure, a simulation-based model was created. In comparison to non-fault-tolerant methods, experimental results show increased task completion rate, decreased recovery time, and improved system availability. The study emphasises how crucial it is to create fault-tolerance plans for next-generation distributed cloud systems that are both economical and effective.
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