Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm

TLDR: The experimental results show that the proposed algorithm reduces makespan, enhances resource utilization, and improves load balancing, compared to MOHEFT and MCP, the well-known workflow scheduling algorithms of the literature.

Abstract: Cloud computing is one of the most popular distributed environments, in which, multiple powerful and heterogeneous resources are used by different user applications Task scheduling and resource provisioning are two important challenges of cloud environment, called cloud resource management Resource management is a major problem especially for scientific workflows due to their heavy calculations and dependency between their operations Several algorithms and methods have been developed to manage cloud resources In this paper, the combination of state-action-reward-state-action learning and genetic algorithm is used to manage cloud resources At the first step, the intelligent agents schedule the tasks during the learning process by exploring the workflow Then, in the resource provisioning step, each resource is assigned to an agent, and its utilization is attempted to be maximized in the learning process of its corresponding agent This is conducted by selecting the most appropriate set of the tasks that maximizes the utilization of the resource Genetic algorithm is utilized for convergence of the agents of the proposed method, and to achieve global optimization The fitness function that has been exploited by this genetic algorithm seeks to achieve more efficient resource utilization and better load balancing by observing the deadlines of the tasks The experimental results show that the proposed algorithm reduces makespan, enhances resource utilization, and improves load balancing, compared to MOHEFT and MCP, the well-known workflow scheduling algorithms of the literature