查看文章

Most studies on task scheduling in the cloud consider at most two or three objectives and hence the main motivation of this paper is to design the task scheduling problem by considering conflicting objectives (i.e., makespan, energy consumption, resources utilization, and security). The proposed algorithm consists of two stages (i.e., meta-scheduler and local-scheduler). In the meta-scheduler stage, the tasks are assigned to hosts based on their priorities, deadlines, and the power of hosts. In the local-scheduler stage, the optimal mapping between tasks and virtual machines is obtained with the proposed Parallel Reinforcement Learning Caledonian Crow (PRLCC). The proposed PRLCC is a combination of the New Caledonian Crow Learning Algorithm (NCCLA), Reinforcement Learning (RL), and parallel strategy. The RE is utilized to guide the agent's activity and provide a balance between intensification and …