An enhancement of task scheduling in cloud computing based on imperialist competitive algorithm and firefly algorithm

Cloud computing is an Internet-based approach in which all applications and files are hosted in a cloud consisting of thousands of computers that are linked in complex ways. The major challenge of cloud data centers is to show how the millions of requests of final users are correctly and effectively being investigated and serviced. Load-balancing techniques are needed to increase the flexibility and scalability of cloud data centers. Load-balancing technique is one of the most significant issues in the distributed computing system. Since there are large-scale resources and a lot of user demands in cloud computing load-balancing problem, it could be the main reason that many researchers considered and addressed that as an NP-hard problem. Therefore, some heuristics algorithms such as imperialist competitive algorithm (ICA) and firefly algorithm (FA) had been proposed by previous researchers to solve the mentioned problem. Although ICA and FA could get an approximate satisfying result in solving the cloud computing load-balancing problem, obtaining the better result means to make improvements in makespan, CPU time, load balancing, stability and planning speed. The motivation of this research is proposing an intelligent meta-heuristic algorithm based on the combination of ICA and FA to get the mentioned required result. Local search ability of FA can reinforce ICA algorithm. The obtained result of this research showed dramatic improvements in makespan, CPU time, load balancing, stability and planning speed.