Abstract
Energy efficiency optimization of mobile edge computing e-commerce clients and reasonable management of server computing resources are worth further study. The participant of the algorithm game model proposed in this paper is mobile e-commerce customer management. The decision space is a two-dimensional space composed of unloading decision and power control, and the benefit function is the energy efficiency function and delay function. The existence and uniqueness of the multidimensional game model are proved theoretically. The simulation results show that the proposed multidimensional game based energy efficiency optimization algorithm of mobile edge computing can reduce the energy consumption and delay of mobile terminals and improve the energy efficiency of unloading calculation under the same task compared with the game scheme without considering power consumption control when the number of e-commerce customer management is larger. This paper deduces the optimal load migration decision of mobile e-commerce customer management and the optimal pricing strategy of mobile edge cloud service providers and proves that the optimal decision and optimal pricing constitute the Starkberg equilibrium. The semidistributed and decentralized task transfer decision-making mechanisms are designed, respectively, and the management decision-making behaviors of mobile e-commerce customers in the mobile edge cloud energy trading market are studied by numerical analysis, as well as the time efficiency of the two mechanisms.
Highlights
Mobile edge is defined as the technology by the European Telecommunications Standards Institute (ETSI), providing service environments, within wireless access networks and in the vicinity of mobile users [1]
The commonly used assumptions in the above algorithms for energy efficiency optimization of e-commerce Customer Relationship Management (CRM) systems based on MEC mobile edge computing have certain limitations: the computing resources of MEC servers are limited, and unloading a large number of tasks will bring considerable queuing delay
The user collects energy efficiency information: other e-commerce customers need to uninstall the MEC server computing the number of users, their choice of channel, and according to the information transmission rate calculation, each channel interference, and the total number of users, in MEC server to determine whether the user needs to line up
Summary
Received 22 November 2021; Revised 8 December 2021; Accepted 16 December 2021; Published 3 January 2022. Energy efficiency optimization of mobile edge computing e-commerce clients and reasonable management of server computing resources are worth further study. E participant of the algorithm game model proposed in this paper is mobile e-commerce customer management. E simulation results show that the proposed multidimensional game based energy efficiency optimization algorithm of mobile edge computing can reduce the energy consumption and delay of mobile terminals and improve the energy efficiency of unloading calculation under the same task compared with the game scheme without considering power consumption control when the number of e-commerce customer management is larger. E semidistributed and decentralized task transfer decision-making mechanisms are designed, respectively, and the management decision-making behaviors of mobile e-commerce customers in the mobile edge cloud energy trading market are studied by numerical analysis, as well as the time efficiency of the two mechanisms Energy efficiency optimization of mobile edge computing e-commerce clients and reasonable management of server computing resources are worth further study. e participant of the algorithm game model proposed in this paper is mobile e-commerce customer management. e decision space is a two-dimensional space composed of unloading decision and power control, and the benefit function is the energy efficiency function and delay function. e existence and uniqueness of the multidimensional game model are proved theoretically. e simulation results show that the proposed multidimensional game based energy efficiency optimization algorithm of mobile edge computing can reduce the energy consumption and delay of mobile terminals and improve the energy efficiency of unloading calculation under the same task compared with the game scheme without considering power consumption control when the number of e-commerce customer management is larger. is paper deduces the optimal load migration decision of mobile e-commerce customer management and the optimal pricing strategy of mobile edge cloud service providers and proves that the optimal decision and optimal pricing constitute the Starkberg equilibrium. e semidistributed and decentralized task transfer decision-making mechanisms are designed, respectively, and the management decision-making behaviors of mobile e-commerce customers in the mobile edge cloud energy trading market are studied by numerical analysis, as well as the time efficiency of the two mechanisms
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