Meta-heuristic firefly approach to multi-servers load balancing with independent and dependent server availability consideration

摘要

Load balancing is the foremost confront in a cloud environment. Load balancing is assisted to disseminate dynamic workload across many nodes to guarantee that not a single node gets overloaded. In the existing work Iterative Proximal Algorithm is introduced for the load balancing. This current work concentrates on request migration criteria between multiple servers for load balancing. It varies from common load balancing crisis, assume that it is under a disseminated, competitive environment, and is non-cooperative. For every server, its projected response time is taken to be a dis-utility function and its value is reduced. But load balancing with dependent and independent servers is a confronting errand. In order to resolve this challenge, Meta-heuristic scheme is carried out based on firefly algorithm to balance load on multiple servers. The main contribution of this research work is to perform the load balancing to improve the computational efficiency of the task submitted by the users. The anticipated multi-server load balancing is carried out based on dependent and independent tasks. The jobs comprise of various interdependent errands in which independent tasks might be processed in multiple cores of the VM or multiple VMs. The errands come through the server's run-time in arbitrary time intervals for different loads. This crisis is resolved by using variation inequality (VI) theory and confirming that there prevails Nash equilibrium resolution set for the devised game. After this, a Nash equilibrium resolution for multi-server load balancing is calculated by using an Iterative Proximal algorithm (IP) is anticipated with Meta heuristic Firefly Optimization Algorithm. Convergence of IPA algorithm is analyzed so that it gets converged to Nash equilibrium. At last, many numerical computations are performed to confirm theoretical analysis.