A memory-based task scheduling algorithm for grid computing based on heterogeneous platform and homogeneous tasks

Grid computing is a new computing mode in recent years, which focuses on parallel infrastructure and its comprehensive application ability to network computers and distributed processors. Grid computing has been fully applied in the field of modern information technology and computer. Task scheduling is the core of grid computing. The quality of task scheduling algorithm directly affects the response time of the whole computing system. For heterogeneous tasks on heterogeneous platforms, this paper proposes a task scheduling algorithm with memory function, and introduces the distributed particle swarm optimisation algorithm into this algorithm, which realises the combination of resource processing tasks in grid computing and the behaviour characteristics of intelligent groups, so as to better realise the dynamic and scalable scheduling of heterogeneous tasks on heterogeneous platforms to adapt to grid environment sex. Finally, the grid simulation software GridSim is used to simulate the algorithm proposed in this paper. At the same time, it is compared with the state stochastic scheduling algorithm. Experimental results show that the proposed algorithm has obvious advantages in scheduling quality in grid environment.