Research On Task Scheduling With Energy Constraint Based On Mobile Cloud Computing

In intelligent manufacturing factories,production automation brings a lot of task processing requirements to various task processing terminals,and mobile cloud computing technology can ease the task processing pressure.However,there are some problems in the application of mobile cloud computing technology.For example,the time delay requirements of a large number of real-time tasks are difficult to meet,and the limited energy storage of the mobile end makes the working time of the mobile end a bottleneck in mobile cloud computing applications.This thesis considers the task scheduling problem in mobile cloud computing with task delay and mobile energy utilization as the optimization goals.The task scheduling problem in this thesis is essentially a task unloading problem,that is,how to optimize the time delay and the energy utilization rate of the mobile side,and better migrate part of the task from the mobile side to the cloud server.This thesis puts forward the basic scheme of the task scheduling problem of mobile cloud computing,the basic framework of the task scheduling problem,and establishes the basic model for the task scheduling problem.This model lays a solid mathematical foundation for the solution of the task scheduling problem.Then two algorithms are proposed to optimize the time delay and the energy efficiency of the mobile end based on the mathematical model.In this thesis,based on the optimization goal of mobile energy consumption and task delay,task delay model,energy consumption model.Two optimization algorithms based on heuristic algorithm principles are put forward to solve the task scheduling step strategy.The two heuristic algorithms are based on particle swarm algorithm and genetic algorithm,and the PGAAT algorithm and GACTEU algorithm are proposed.The first algorithm optimizes some of the parameters related to the task,and the algorithm obtains the task arrival rate parameter with the minimum delay.The second algorithm is based on the output results of the first algorithm,and the optimal scheduling decision result of the mobile task is obtained by optimizing the energy utilization efficiency of the mobile end.This thesis classifies tasks by some attributes of tasks,and does task scheduling on the basis of classification.It also effectively improves the performance of the algorithm.Experimental simulation results show that the proposed two-step optimization method is effective,and the proposed two algorithms can reasonably perform the task scheduling,which makes the time delay and the energy utilization ratio of the mobile end significantly improve compared with the previous ones.