Task allocation method based on particle swarm and simulated annealing optimization in mobile cloud

A particle swarm optimization and simulated annealing technology, applied in the field of mobile cloud computing, can solve problems such as system load imbalance and reducing system efficiency

Active Publication Date: 2018-03-06
SOUTHEAST UNIV
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

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.

Method used

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  • Task allocation method based on particle swarm and simulated annealing optimization in mobile cloud
  • Task allocation method based on particle swarm and simulated annealing optimization in mobile cloud
  • Task allocation method based on particle swarm and simulated annealing optimization in mobile cloud

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Experimental program
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Embodiment Construction

[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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Abstract

The invention provides a task allocation method based on a particle swarm and simulated annealing optimization in mobile cloud. Multiple mobile nodes are involved in the allocation method. The mobilenodes form a self-organized network in a wireless mode and share computing resources under the condition that no basic network facilities are available. The task allocation method comprises the four stages that (1) a task uninstalling request is initiated, wherein when a complicated computing task exists on a main node and the main node does not have sufficient computing resources for processing the task, the main node sends the task uninstalling request to slave nodes; (2) a cost function is established, wherein the main node generates a task uninstalling cost function according to remainingcomputing resource information of the slave nodes and relevant information needed for processing the complicated computing task; (3) an optimization problem is solved, wherein a task allocation algorithm based on the particle swarm and simulated annealing optimization is executed, and a task allocation result is obtained; and (4) the task is allocated, wherein the main node allocates the computingtask to all the slave nodes according to the optimization result.

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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
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
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