Abstract
Job scheduling is one of the most important research problems in distributed systems, particularly cloud environments/computing. The dynamic and heterogeneous nature of resources in such distributed systems makes optimum job scheduling a non-trivial task. Maximal resource utilization in cloud computing demands/necessitates an algorithm that allocates resources to jobs with optimal execution time and cost. The critical issue for job scheduling is assigning jobs to the most suitable resources, considering user preferences and requirements. In this paper, we present a hybrid approach called FUGE that is based on fuzzy theory and a genetic algorithm (GA) that aims to perform optimal load balancing considering execution time and cost. We modify the standard genetic algorithm (SGA) and use fuzzy theory to devise a fuzzy-based steady-state GA in order to improve SGA performance in term of makespan. In details, the FUGE algorithm assigns jobs to resources by considering virtual machine (VM) processing speed, VM memory, VM bandwidth, and the job lengths. We mathematically prove our optimization problem which is convex with well-known analytical conditions (specifically, Karush-Kuhn-Tucker conditions). We compare the performance of our approach to several other cloud scheduling models. The results of the experiments show the efficiency of the FUGE approach in terms of execution time, execution cost, and average degree of imbalance.