11. Conclusion
The paper widely reviews the application of metaheuristic techniques in the area of scheduling in cloud and grid environments. Metaheuristic techniques are usually slower than deterministic algorithms and the generated solutions may not be optimal, thus most of the research done is toward improving the convergence speed and quality of the solution. These issues have been undertaken by modifying the transition operator, preprocessing the input population or taking hybrid approach in metaheuristic techniques.
Moreover different scheduling algorithms have focused on diverse optimization criteria. In the studied literature, most of the authors have focused on reduction of makespan and execution cost whereas others have given significance to response time, throughput, flowtime and average resource utilization. Comparative analysis of algorithms based on each metaheuristic technique mainly compares the technique used for improving metaheuristics, optimization criteria, nature of tasks and the environment in which the algorithm is implemented. The recent research efforts are done in the direction of energy-aware scheduling as data centers have become energy-hungry and a major source of CO2 emissions. The challenge is to reduce energy consumption of data centers without degrading performance and violating SLA constraints. Various open issues are also discussed in the paper which can be taken up for future research.
