A Novel Reinforcement-Learning-Based Approach to Workflow Scheduling Upon Infrastructure-as-a-Service Clouds

Recently, the cloud computing paradigm has become increasingly popular in large-scale and complex workflow applications. The workflow scheduling problem, which refers to finding the most suitable resource for each task of the workflow to meet user defined quality of service, attracts considerable research attention. Multi-objective optimization algorithms in workflow scheduling have many limitations (e.g., the encoding schemes in most existing heuristic-based scheduling algorithms require prior experts' knowledge), and thus, they can be ineffective when scheduling workflows upon dynamic cloud infrastructures with real time. A novel reinforcement-learning-based algorithm to multi-workflow scheduling over IaaS is proposed. It aims at optimizing make-span and dwell time and is to achieve a unique set of correlated equilibrium solution. The proposed algorithm is evaluated for famous workflow templates and real-world industrial IaaS by simulation and compared to the current state-of-the-art heuristic algorithms. The result shows that the algorithm outperforms compared algorithm.