Cost Aware Resource Allocation in Cloud Computing: A Comparison of Heuristic and Greedy Approaches
TL;DR
Experimental results demonstrate that the heuristic approach achieves better cost efficiency and improved resource utilization compared to the greedy method, highlighting that simple, rule-based allocation strategies can significantly enhance performance in cloud environments while maintaining low computational complexity.
Abstract
Cloud computing has become an essential platform for delivering scalable and on-demand computing resources. However, inefficient resource allocation often leads to increased operational costs and poor utilization of available resources. This paper focuses on addressing this issue by proposing a cost-aware resource allocation approach using a heuristic method and comparing its performance with a greedy allocation strategy. The heuristic approach assigns resources based on the actual requirements of tasks, aiming to minimize resource wastage and reduce overall cost. In contrast, the greedy method makes quick allocation decisions without considering future needs, which can result in over-provisioning. Experimental results demonstrate that the heuristic approach achieves better cost efficiency and improved resource utilization compared to the greedy method. The findings highlight that simple, rule-based allocation strategies can significantly enhance performance in cloud environments while maintaining low computational complexity. Keywords:Cloud Computing, Resource Allocation, Heuristic Method, Greedy Algorithm, Cost Optimization, Virtualization, Task Scheduling, Resource Utilization