Task unloading method based on approximate optimization and reinforcement learning in MEC

A reinforcement learning and optimization technology, applied to electrical components, transmission systems, etc., can solve problems such as application unresponsiveness and task retention

Active Publication Date: 2020-04-07
DALIAN UNIV OF TECH
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Problems solved by technology

If a certain strategy is not adopted, directly assigning a large number of users to the same network access point will cause everyone to be unable to submit tasks and receive calculation results at a normal rate
Similarly, if the computing tasks of a large number of users are a

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  • Task unloading method based on approximate optimization and reinforcement learning in MEC
  • Task unloading method based on approximate optimization and reinforcement learning in MEC
  • Task unloading method based on approximate optimization and reinforcement learning in MEC

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Embodiment Construction

[0084] The specific implementation manners of the present invention will be further described below in conjunction with the accompanying drawings and technical solutions.

[0085] Approximate optimization and task offloading methods based on reinforcement learning in MEC are composed of two parts: one part is an approximate optimization method based on integer linear programming, through the method of relaxation-filtering-rounding, an approximate optimal unloading strategy and Resource allocation strategy; the other part is based on reinforcement learning theory, using linear regression method to predict and give unloading strategy, and then further give the corresponding optimal resource allocation strategy through deep neural network;

[0086] (1) The specific establishment process of the mobile edge computing offloading model is as follows:

[0087] (1.1) Consider an edge computing network consisting of multiple edge cloud servers in Each represents an edge cloud serv...

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Abstract

The invention belongs to the technical field of mobile edge computing unloading, and provides a task unloading method based on approximate optimization and reinforcement learning in MEC. A mathematical model is established for the problem, and a to-be-solved problem is converted into an integer linear programming problem. In order to solve the problem. an offline algorithm provided by the invention performs relaxation operation on integer constraint conditions in the problem, and then performs filtering and rounding operation on a solving result in sequence to obtain a final solution. The invention also provides an online algorithm for solving the problem, a linear regression method is used for predicting and providing an unloading strategy based on a reinforcement learning theory, and then a corresponding optimal resource allocation strategy is further provided by combining a deep neural network on the basis. A reasonable task unloading and resource allocation strategy can be formulated for a user under a limited resource condition, so that the application program execution delay and equipment energy consumption of user equipment are effectively reduced, and the utilization rateof the whole network is improved while the service quality is improved.

Description

technical field [0001] The invention relates to a method for providing an efficient offloading strategy and a resource allocation strategy for task offloading in the framework of Mobile Edge Computing (MEC for short), and belongs to the technical field of mobile edge computing offloading. This method can reasonably formulate task offloading strategies and allocate edge cloud computing resources for mobile device users under the condition of limited computing resources and network resources, which can effectively reduce the application task execution delay of user devices and the energy consumption of user devices, and improve user services. Improve the utilization rate of the entire edge network while improving the quality. Background technique [0002] Mobile Edge Computing (MEC for short) is a newly proposed network model in recent years. Different from the traditional cloud computing network model, the mobile edge network does not concentrate the computing resources in t...

Claims

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Application Information

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IPC IPC(8): H04L29/08
CPCH04L67/10H04L67/60
Inventor 夏秋粉娄铮徐子川
Owner DALIAN UNIV OF TECH
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