Advanced Paradigms in Cloud Computing Task Scheduling: A Comparative Synthesis of Heuristic, Metaheuristic, and Hybrid Algorithmic Frameworks for Optimized Resource Allocation

Dr. Alistair Sterling

Authors

Dr. Alistair Sterling Department of Computer Science and Distributed Systems, University of British Columbia, Canada

Keywords:

Cloud Computing, Task Scheduling, Metaheuristic Algorithms, Resource Allocation

Abstract

The rapid proliferation of cloud computing as a fundamental utility for modern digital infrastructure has necessitated the development of highly sophisticated task scheduling and resource allocation mechanisms. As cloud environments transition toward increasingly heterogeneous and dynamic architectures, traditional scheduling methods frequently encounter scalability bottlenecks and efficiency degradation. This research provides an exhaustive analysis of the contemporary landscape of task scheduling in cloud computing, meticulously evaluating heuristic, metaheuristic, and hybrid algorithmic approaches. By synthesizing foundational research on Central Processing Unit (CPU) allocation using simulators such as CloudSim and examining novel frameworks like the Shortest Remaining Job First (SRJF), Shortest Job First (SJF), and multi-objective optimization models, this study delineates the critical trade-offs between makespan, throughput, cost-efficiency, and load balancing. Furthermore, the article explores the evolution toward bio-inspired metaheuristics, including Particle Swarm Optimization (PSO), Whale Optimization Algorithms (WOA), and Hybrid Grey Wolf Whale Optimization (GWW-WO). These paradigms are scrutinized for their efficacy in managing scientific workflows and disassembly tasks under stringent deadline constraints. The findings suggest that hybrid metaheuristic models offer superior performance in heterogeneous multi-cloud environments by effectively navigating the exploration-exploitation trade-off. This article concludes with a visionary outlook on the integration of sustainable computing practices and autonomous resource distribution in the next generation of cloud ecosystems.

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