Multi-objective Security Driven Job Scheduling for Computational Cloud Systems

This paper proposes a multi-objective parallel job scheduling algorithm for a Computational Cloud environment. We present a fault-tolerant, scalable and efficient solution for optimizing scheduling of N independent jobs on M parallel machines that minimizes two objectives simultaneously, namely the failure probability and the total completion time of all the jobs. Obtaining an optimal solution for this type of complex, large-sized problem in a reasonable computational time using traditional approaches or optimization tools is extremely difficult. As this problem is NP-hard in the strong sense, a meta-heuristic method which is the second version of the non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve this problem. This approach is based on the Pareto dominance relationship, providing no single optimal solution, but a set of solutions which are not dominated by each other. The performance of the presented model and the applied GA is verified by a number of numerical experiments. The related results show the effectiveness of the proposed model and GA for small and medium-sized problems.