A new hybrid multi-objective optimization algorithm for task scheduling in cloud systems

Nowadays, cloud computing is widely used in various fields and is booming day by day with different services offered to users according to their needs and contracts. However, this has brought many challenges and constraints that organizations must to be aware of and address to fully harness its power. In practice, the most important issue that has gained significant influence in improving system performances is task scheduling. Unfortunately, it is commonly known that this problem is NP-hard and the use of both heuristics and metaheuristics is required to obtain near optimal solutions but in a reasonable amount of computation time. Despite the fact that several studies have been published in the literature, there are still interesting and relevant questions to be addressed. For instance, when it comes to the stagnation phenomenon of local solutions and the premature convergence of the search process, it is crucial to execute the exploration and exploitation stages carefully as improperly performed stages may result in inefficient task mapping solutions. Consequently, to overcome the limitations of existing techniques in terms of local optimality trap and immature convergence, a novel hybrid optimization algorithm is proposed to deal with multi-objective task scheduling in heterogeneous IaaS cloud environments. It is based on the combination of the pollination behavior of flowers with the search exploration capability of the grey wolf optimizer strategy. In addition, it makes use of the evolutionary algorithms crossover operators to strike a good balance between exploring new solutions and exploiting the already discovered ones. Based on the CloudSim framework, different test-bed scenarios and both synthetic and standard workload traces were considered to assess the performance of the proposed algorithm by evaluating its objective function in terms of four optimization criteria, namely time makespan, resource utilization, degree of imbalance and throughput. Our proposal was compared to the well-known optimization-based scheduling techniques in the literature, like TSMGWO, GGWO, LPGWO and FPA approach. The obtained results corroborate the merits of the new designed hybrid algorithm.