Adaptive Resource Scheduling Algorithms for Improving Cloud Computing Performance
Keywords:
Adaptive Resource Scheduling, Cloud Computing, Resource Allocation, Task Scheduling, Load Balancing, Virtual Machines, Resource Utilization, Genetic Algorithm, Ant Colony Optimization, Cloud PerformanceAbstract
Cloud computing has emerged as an important computing paradigm that provides scalable, flexible, and on-demand access to computational resources through the Internet. However, the increasing demand for cloud services creates significant challenges in resource allocation, task scheduling, workload distribution, and system performance. Traditional resource scheduling algorithms often struggle to respond effectively to dynamic workloads, heterogeneous virtual machines, and changing resource requirements. This study examines adaptive resource scheduling algorithms for improving cloud computing performance through dynamic resource allocation, workload prediction, and intelligent load balancing. The research focuses on evaluating scheduling approaches such as First Come First Serve, Round Robin, Min-Min, Max-Min, Genetic Algorithms, and Ant Colony Optimization. An adaptive scheduling framework is proposed to monitor resource utilization, estimate task requirements, select appropriate virtual machines, and dynamically distribute workloads. The research methodology emphasizes simulation-based evaluation using performance indicators, including execution time, response time, resource utilization, throughput, makespan, and energy consumption. The proposed approach aims to overcome the limitations of conventional scheduling methods by improving resource utilization and reducing computational delays. The study provides a foundation for developing scalable, efficient, and cost-effective cloud resource management systems capable of responding to changing application demands
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