Joint optimization method for task unloading and resource allocation in multi-cell scene

Abstract

The invention relates to a joint optimization method for task unloading and resource allocation in a multi-cell scene, and belongs to the field of mobile edge computing. The method comprises the following steps: firstly, establishing an MEC task unloading model in a multi-cell scene, and designing a system total overhead function; then, optimizing an unloading decision of the user by adopting a chaotic variation binary particle swarm algorithm; under the condition that an unloading decision of a user is obtained, decomposing an original problem into two sub-problems of MEC computing resource allocation and uplink sub-channel allocation; adopting a Lagrange multiplier method to carry out MEC calculation resource allocation on the unloading users, and adopting an improved Kuhn-Munkres algorithm to carry out uplink sub-channel allocation on the unloading users under the constraint conditions of meeting the minimum rate and the maximum tolerable interference of the users. According to theinvention, the total system overhead for a user to execute tasks can be reduced, and the system performance is effectively improved.

Description

Technical field [0001] The invention belongs to the field of mobile edge computing, and relates to a joint optimization method for task offloading and resource allocation in a multi-cell scenario. Background technique [0002] In recent years, with the development of mobile Internet and cloud computing, more and more new applications appear in people’s daily lives, such as augmented / virtual reality, face recognition and interactive games, etc. However, these new applications are common It is a computationally intensive and delay-sensitive application that makes it difficult for mobile terminals to execute effectively. Although Mobile Cloud Computing (MCC) can meet users’ performance requirements for these applications to a certain extent, it allows mobile devices to partially or completely offload local complex and a large number of computing tasks to the cloud data center located in the core network for execution. This solves the problem of resource shortage of mobile devices t...

AI Technical Summary

Problems solved by technology

However, since MCC's cloud data center is located in the core network, the transmission distance between the cloud and end users is long, which will generate additional delay overhead, which cannot meet the requirements of low delay and high reliability in the future.

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