A Load Balancing Algorithm in Cloud Computing Based on Modified Particle Swarm Optimization and Game Theory
Cloud computing is the internet based innovative technology, that offers many services and technologies over the internet, it is the fruit of increasing technological development in the grid, parallel, distributed and clustering systems as well as virtualization and data storage. Many load balancing algorithms and scheduling techniques are required for achieving the optimal utilization of resources and decreasing the time of shifting load, from the overload machines to the underload machines. In this paper, we present a new evolutionary algorithm called SASPSOLB to balance the workload in cloud computing based on game theory and a modified particle swarm optimization the load balancing problem has been modeled as a constrained optimization problem, to find the optimal or near optimal solution, using the Nash equilibrium concept to reduce the expected response time, we consider the game assumed as a noncooperative game among users. The executed algorithm presented in this paper is compared with another one based on genetic optimization. The simulation results show that our algorithm performs better in term of expected response time and the Makespan.
2019 4th World Conference on Complex Systems (WCCS)