A MASTER NODE CONSENSUS APPROACH FOR ENHANCING LATENCY PERFORMANCE IN DISTRIBUTED CLOUD COMPUTING ENVIRONMENT

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Efficient master node selection is critical for achieving low-latency performance in distributed cloud computing environments. Traditional election mechanisms often overlook node resource variability, leading to bottlenecks, increased execution times, and SLA violations. This paper introduces the Master Node Consensus Approach (MNCA), which integrates a Genetic Algorithm-based resource-profiling model with a fault-tolerant master-candidate failover mechanism to enhance latency performance in distributed cloud infrastructures. MNCA evaluates node fitness using CPU capacity, memory availability, bandwidth, and throughput, ensuring that only high-capacity nodes are selected as masters. The model was implemented in CloudSim Plus and benchmarked against the Heterogeneous Earliest Finish Time (HEFT) and Balanced Load Allocation (BLA) algorithms. Under node scaling from 20 to 100 hosts, MNCA reduced execution time from 5900 ms to 1800 ms, outperforming HEFT (11000–3800 ms) and BLA (7000–2800 ms). MNCA achieved the lowest SLA violation rates, decreasing from 200 to 130 violations, compared to HEFT (250–150) and BLA (300–180). Memory usage consistently dropped from 54 MB to 33 MB, surpassing HEFT (60–42 MB) and BLA (55–35 MB). Under workload scaling from 200 to 2000 tasks, MNCA maintained the fastest execution times (1500–4100 ms) versus HEFT (1700–11000 ms) and BLA (1600–8000 ms). These results confirm that MNCA enhances latency performance, reduces SLA violations, improves memory efficiency, and provides robust failover behavior in distributed cloud environments. The proposed approach offers a scalable, intelligent solution for optimizing coordination services in modern distributed systems.

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Keywords

Master Node Selection, Genetic Algorithm (GA), Distributed Cloud Computing, Latency Optimization, Consensus Mechanism, Fault Tolerance.