A novel hybrid meta‐heuristic‐oriented latency sensitive cloud object storage system

Abstract

SummaryCloud providers must find out how to properly arrange data in a limited count of servers while ensuring latency assurances to reduce total storage expenses. Timeout is also important to consider because it has a substantial impact on response latency. The core aim of this task is to implement a new cloud object storage system strategy that handles challenges like “latency‐sensitive data allocation, latency‐sensitive data re‐allocation, and latency‐sensitive workload consolidation.” The main contribution here is that distributing the latency of the cloud object storage system allows for better data allocation, data reallocation, and workload consolidation. The primary aim is to use the fewest number of servers feasible to fulfill all requests while maintaining their latency requirements, lowering the overall data transmission cost. As a consequence, Whale Butterfly Optimization Method (WBOA) is a novel hybrid meta‐heuristic algorithm that solves NP‐hard problems by combining baseline advanced algorithms. The simulation outcomes reveal that the offered paradigm consistently provides the greatest outcomes regarding throughput utilization, lower latency, higher storage, and number of used nodes when compared to competing techniques.