A task allocation method based on particle swarm optimization and simulated annealing optimization in mobile cloud
A technology of simulated annealing and task allocation, which is applied in the field of mobile cloud computing, can solve problems such as system load imbalance and lower system efficiency, and achieve the effect of both computing energy consumption and communication energy consumption, and effective computing resource sharing
Active Publication Date: 2021-04-27
SOUTHEAST UNIV
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However, the focus of existing algorithms is mainly on shortening the task completion time, which can easily cause the load imbalance of the whole system and reduce the system efficiency.
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[0056] The implementation method of the present invention will be further described below in conjunction with the accompanying drawings.
[0057] Initiate task unloading:
[0058] Such as figure 1 As shown, there is a master node M in the Wifi area 0 and N slave nodes S 1 -S n , master node M 0 Connected to each slave node through a wireless link, the master node M 0 There is a complex computational task Q total , and the master node does not have enough computing resources to perform Q total processing, so the master node moves to all slave nodes S of Duoyun 1 -S n Send a task offload request. When the slave node receives the task offloading request from the master node, the slave node collects the remaining computing resource information on the node and sends it back to the master node that initiated the task offloading request.
[0059] Create a cost function:
[0060] We use M to represent the total number of tasks and N to represent the total number of slave no...
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The invention provides a task allocation method based on particle swarm and simulated annealing optimization in a mobile cloud. This allocation method involves a plurality of mobile nodes. Mobile nodes form an ad hoc network wirelessly and share computing resources without basic network facilities. The task allocation method includes four stages: (1) Initiate a task unloading request. When there is a complex computing task on the master node, but the master node does not have enough computing resources to process the task, the master node sends a task offloading request to the slave node. (2) Establish a cost function. The master node generates a task offloading cost function based on the remaining computing resource information of the slave node and related information that needs to process complex computing tasks. (3) Solve the optimization problem. Execute the task allocation algorithm based on particle swarm optimization and simulated annealing to obtain the task allocation results. (4) Distributing tasks. The master node distributes computing tasks to each slave node according to the optimization results.
Description
technical field [0001] The invention relates to a task allocation method based on particle swarm optimization and simulated annealing optimization in a mobile cloud, and belongs to the technical field of mobile cloud computing. Background technique [0002] Mobile devices such as smartphones and tablets have gained tremendous popularity in the past few years, however, due to limitations in CPU performance, battery capacity, storage capacity, etc., mobile devices perform poorly in processing computationally intensive tasks. Such as slow computing speed, rapid power failure, etc. In order to solve these problems, researchers began to consider establishing a system called Mobile Cloud Computing (MCC, Mobile Cloud Computing). The main idea is to offload the computationally intensive tasks on the mobile client to the target agent to perform , not only can greatly reduce the processing time of the task but also can minimize the energy consumption of the mobile device. [0003] A...
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IPC IPC(8): G06F9/48G06F9/50G06N3/00
CPCG06F9/4881G06F9/505G06F2209/509G06N3/006
Inventor 夏玮玮黄博南张静章跃跃邹倩燕锋沈连丰
Owner SOUTHEAST UNIV