Optimized Hyper Heuristic Scheduling using Adaptive Load Balancing Algorithm in Cloud Computing


Md Oqail Ahmad, Rafiqul Zaman Khan
Department of Computer Science, Aligarh Muslim University, Aligarh, India.


Effective scheduling is important for the inclusive performance of cloud computing system. The main problem of scheduling is how to distribute the entire tasks to an appropriate virtual machine (VM). The scheduling of the tasks to suitable VM for computing of the requests in cloud computing is originate to be an NP-complete issue. An optimized algorithm is essential to schedule the tasks on the VMs to decrease the makespan time, degree of imbalance and total processing cost. In this paper, we proposed a new optimized Hyper-Heuristics Scheduling using Adaptive Load Balancing Algorithm named as HHS-ALBA, for lookup optimized scheduling solution for cloud computing. The proposed HHS-ALBA algorithm uses the idea of load balancing by the solution identified with the hyper heuristic algorithm too ptimize the solution. The results show that the proposed HHS-ALBA algorithm could outperform in term of makespan, degree of imbalance factor, total processing cost and total processing time, and compared with existing HHSA and traditional algorithms. Moreover, the proposed HHS-ALBA algorithm is comparatively and statically more effective than the heuristics algorithms like ACO, PSO GA and traditional algorithms FCFS and SJF. Performance evaluation was done with CloudSim Simulator.