Multi-objective Scheduling of Tasks in Cloud Computing Using Fire Fly and Bee Colony Algorithms

Abstract

The problem of allocating hardware and processing resources in cloud computing is considered in this research. Here, a virtual bee is assigned to any task that dynamically seeks the most appropriate computing resources. Each available computing source also has a virtual fire fly that its brightness is proportional to the amount of processing unit and the amount of computing residue that examines the computing source. The problem of distribution of computing resources among existing tasks will be investigated using the greedy technique of calculating the location of each task in general. The current method has many advantages compared to the random distribution method, but to complement the other topics, researchers can offer other tactics of distribution of computing resources simultaneously with the method proposed in this study and compare their performance with the implementation of both simulations. The simulation results show that the performance criteria of the cloud computing system such as time and computing resources have been greatly improved.