Performance evaluation of heuristic algorithms for resource allocation in distributed cloud computing
Abstract
Cloud computing is a term that refers to computer resources and systems that are available upon request over the network and that can provide a number of integrated computer services without being restricted by local resources, with the aim of making it easier for the user. It has been proven that the process of optimally allocating resources in the cloud computing environment is NP-complete, hence the need to use heuristic methods.
A number of resource allocation algorithms have been proposed and used to address this problem, but no one has been shown to be absolutely superior to the other. Therefore, five resource allocation algorithms were applied in order to evaluate their performance in terms of overall execution time for a set of tasks (Makespan), Throughput, Response Time, Memory Utilization, CPU Utilization, and BW Utilization.
The evaluated algorithms in this paper in terms of resource allocation performance in cloud computing are MaxMin, MinMin, Honey Bee algorithm, Ant Colony Optimization, and the Particle Swarm Optimization algorithm. The comparisons studied in this article indicate that the Honey Bee algorithm is the most suitable choice for allocating resources in cloud computing if the goal of the computing system is to achieve the highest Throughput with lowest Response Time.
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