Cloud Task Scheduling Based on Swarm Intelligence and Machine Learning

• Published in: 2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud) pp. 272 - 279
• Main Authors: Rjoub, Gaith, Bentahar, Jamal
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-08-2017
• Subjects: Ant Colony Optimization; Cloud computing; CloudSim; Honey Bee Optimization; Load management; Machine Learning; Optimization; Particle swarm optimization; Processor scheduling; Scheduling; Swarm Intelligence
• DOI: 10.1109/FiCloud.2017.52

Cloud computing is the expansion of parallel computing, distributed computing. The technology of cloud computing becomes more and more widely used, and one of the fundamental issues in this cloud environment is related to task scheduling. However, scheduling in Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques, especially those inspired by Swarm Intelligence (SI) have been proposed. This paper proposes a machine learning algorithm to guide the cloud choose the scheduling technique by using multi criteria decision to optimize the performance. The main contribution of our work is to minimize the makespan of a given task set. The new strategy is simulated using the CloudSim toolkit package where the impact of the algorithm is checked with different numbers of VMs varying from 2 to 50, and different task sizes between 30 bytes and 2700 bytes. Experiment results show that the proposed algorithm minimizes the execution time and the makespan between 7% and 75%, and improves the performance of the load balancing scheduling.