scholarly journals Energy Efficiency Analysis of e-Commerce Customer Management System Based on Mobile Edge Computing

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Wenxing Chen ◽  
Bin Yang
Keyword(s):  
Decision Making ◽  
Energy Efficiency ◽  
Service Providers ◽  
Edge Computing ◽  
Customer Management ◽  
Migration Decision ◽  
Optimal Pricing ◽  
Game Model ◽  
Mobile Edge Computing ◽  
Efficiency Optimization

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.

scholarly journals Computation Offloading Strategy in Mobile Edge Computing

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 191 ◽  
Author(s):  
Jinfang Sheng ◽  
Jie Hu ◽  
Xiaoyu Teng ◽  
Bin Wang ◽  
Xiaoxia Pan
Keyword(s):  
Decision Making ◽  
User Experience ◽  
Task Scheduling ◽  
Mobile Internet ◽  
Edge Computing ◽  
Computation Offloading ◽  
Smart Devices ◽  
Mobile Edge Computing ◽  
Network Bandwidth ◽  
Intelligent Devices

Mobile phone applications have been rapidly growing and emerging with the Internet of Things (IoT) applications in augmented reality, virtual reality, and ultra-clear video due to the development of mobile Internet services in the last three decades. These applications demand intensive computing to support data analysis, real-time video processing, and decision-making for optimizing the user experience. Mobile smart devices play a significant role in our daily life, and such an upward trend is continuous. Nevertheless, these devices suffer from limited resources such as CPU, memory, and energy. Computation offloading is a promising technique that can promote the lifetime and performance of smart devices by offloading local computation tasks to edge servers. In light of this situation, the strategy of computation offloading has been adopted to solve this problem. In this paper, we propose a computation offloading strategy under a scenario of multi-user and multi-mobile edge servers that considers the performance of intelligent devices and server resources. The strategy contains three main stages. In the offloading decision-making stage, the basis of offloading decision-making is put forward by considering the factors of computing task size, computing requirement, computing capacity of server, and network bandwidth. In the server selection stage, the candidate servers are evaluated comprehensively by multi-objective decision-making, and the appropriate servers are selected for the computation offloading. In the task scheduling stage, a task scheduling model based on the improved auction algorithm has been proposed by considering the time requirement of the computing tasks and the computing performance of the mobile edge computing server. Extensive simulations have demonstrated that the proposed computation offloading strategy could effectively reduce service delay and the energy consumption of intelligent devices, and improve user experience.

scholarly journals Task Offloading Strategy and Simulation Platform Construction in Multi-User Edge Computing Scenario

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3038
Author(s):  
Guilu Wu ◽  
Zhongliang Li
Keyword(s):  
Decision Making ◽  
System Performance ◽  
Network Performance ◽  
Vehicular Networks ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Vehicular Communication ◽  
System Overhead ◽  
Computing Method ◽  
Task Offloading

Various types of service applications increase the amount of computing in vehicular networks. The lack of computing resources of the vehicle itself will hinder the improvement of network performance. Mobile edge computing (MEC) technology is an effective computing method that is used to solve this problem at the edge of network for multiple mobile users. In this paper, we propose the multi-user task offloading strategy based on game theory to reduce the computational complexity and improve system performance. The task offloading decision making as a multi-user task offloading game is formulated to demonstrate how to achieve the Nash equilibrium (NE). Additionally, a task offloading algorithm is designed to achieve a NE, which represents an optimal or sub-optimal system overhead. In addition, the vehicular communication simulation frameworks Veins, SUMO model and OMNeT++ are adopted to run the proposed task offloading strategy. Numerical results show that the system overhead of the proposed task offloading strategy can degrade about 24.19% and 33.76%, respectively, in different scenarios.

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