Abstract
Mobile edge computing is a new computing paradigm that can extend cloud computing capabilities to the edge network, supporting computation-intensive applications such as face recognition, natural language processing, and augmented reality. Notably, computation offloading is a key technology of mobile edge computing to improve mobile devices’ performance and users’ experience by offloading local tasks to edge servers. In this paper, the problem of computation offloading under multiuser, multiserver, and multichannel scenarios is researched, and a computation offloading framework is proposed that considering the quality of service (QoS) of users, server resources, and channel interference. This framework consists of three levels. (1) In the offloading decision stage, the offloading decision is made based on the beneficial degree of computation offloading, which is measured by the total cost of the local computing of mobile devices in comparison with the edge-side server. (2) In the edge server selection stage, the candidate is comprehensively evaluated and selected by a multiobjective decision based on the Analytic Hierarchy Process based on Covariance (Cov-AHP) for computation offloading. (3) In the channel selection stage, a multiuser and multichannel distributed computation offloading strategy based on the potential game is proposed by considering the influence of channel interference on the user’s overall overhead. The corresponding multiuser and multichannel task scheduling algorithm is designed to maximize the overall benefit by finding the Nash equilibrium point of the potential game. Amounts of experimental results show that the proposed framework can greatly increase the number of beneficial computation offloading users and effectively reduce the energy consumption and time delay.
Highlights
With the development of artificial intelligence and Internet of ings (IoT) technology, a large number of computationintensive and time-sensitive mobile applications have appeared on mobile terminals, such as face recognition, natural language processing, and augmented reality
(1) In the offloading decision stage, the offloading decision is made based on the beneficial degree of computation offloading, which is measured by the total cost of the local computing of mobile devices in comparison with the edge-side server
(3) In the channel selection stage, a multiuser and multichannel distributed computation offloading strategy based on the potential game is proposed by considering the influence of channel interference on the user’s overall overhead. e corresponding multiuser and multichannel task scheduling algorithm is designed to maximize the overall benefit by finding the Nash equilibrium point of the potential game
Summary
With the development of artificial intelligence and Internet of ings (IoT) technology, a large number of computationintensive and time-sensitive mobile applications have appeared on mobile terminals, such as face recognition, natural language processing, and augmented reality. In the MEC scheme based on the public cloud [3,4,5], accessing the mobile cloud service through the wireless channel results in a large channel blocking rate and delay. Erefore, the key technology of the MEC solution based on the edge server is computing offloading. When many users offload computing tasks to the same edge server through the same channel at the same time, it will cause congestion and greater delay. In order to minimize the energy consumption and service delay of mobile users, the main contributions of this paper are listed as follows:. E overall cost of the mobile edge computing system is defined as the weighted sum of energy consumption, the corresponding transmission delay of computation offloading, and the task processing of all edge nodes. E experiment and result analysis are discussed in Section 7. e final section summarizes the paper and discusses future work
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