Using gravitational search algorithm enhanced by fuzzy for resource allocation in cloud computing environments

Abstract

The aim of this paper is to allocate resources to tasks and scheduling tasks on existing virtual machines (VMs) in cloud environments, so that the time to finish the last work and average of all tasks execution time are minimized, and loads are distributed balanced on virtual machines. Since task scheduling in the cloud environment is a continuous process, so scheduling improvements, although slight, play an important role in cloud efficiency. On the other hand the resource allocation problem in cloud computing and user tasks scheduling on existing virtual machines is a NP-hard problem, and traditional algorithms requires exponential time to examine search space of this problem in sequence and finding the best answer, therefore we used Gravitational Search Algorithm (GSA) that has a high efficiency in solving nonlinear problems, for solving this problem. To do this, we create masses by combining sequences of tasks assigned to all machines. Each mass position is a solution of the problem. Then we find the best possible assignment using the gravitational search algorithm. We used fuzzy logic to determine the number of masses that affect one another during the implementation of the GSA. To calculate the cost, we use a combination of Make_ span (Time to finish the last task) and Mean_ Flow_Time (Average of all tasks execution time) and Load_ imbalance. The results show that the proposed method achieves more optimal response than genetic algorithm and GSA without fuzzy for resource allocation. It means that proposed algorithm allocated resources to tasks with less make span and mean_ flow time and more load balancing than other two algorithms.