scholarly journals Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market

2019 ◽  
Vol 11 (5) ◽  
pp. 118 ◽  
Author(s):  
Giorgos Mitsis ◽  
Pavlos Athanasios Apostolopoulos ◽  
Eirini Eleni Tsiropoulou ◽  
Symeon Papavassiliou

Software Defined Networks (SDN) and Mobile Edge Computing (MEC), capable of dynamically managing and satisfying the end-users computing demands, have emerged as key enabling technologies of 5G networks. In this paper, the joint problem of MEC server selection by the end-users and their optimal data offloading, as well as the optimal price setting by the MEC servers is studied in a multiple MEC servers and multiple end-users environment. The flexibility and programmability offered by the SDN technology enables the realistic implementation of the proposed framework. Initially, an SDN controller executes a reinforcement learning framework based on the theory of stochastic learning automata towards enabling the end-users to select a MEC server to offload their data. The discount offered by the MEC server, its congestion and its penetration in terms of serving end-users’ computing tasks, and its announced pricing for its computing services are considered in the overall MEC selection process. To determine the end-users’ data offloading portion to the selected MEC server, a non-cooperative game among the end-users of each server is formulated and the existence and uniqueness of the corresponding Nash Equilibrium is shown. An optimization problem of maximizing the MEC servers’ profit is formulated and solved to determine the MEC servers’ optimal pricing with respect to their offered computing services and the received offloaded data. To realize the proposed framework, an iterative and low-complexity algorithm is introduced and designed. The performance of the proposed approach was evaluated through modeling and simulation under several scenarios, with both homogeneous and heterogeneous end-users.

Big Data ◽  
2021 ◽  
Author(s):  
Ibrahim A. Elgendy ◽  
Ammar Muthanna ◽  
Mohammad Hammoudeh ◽  
Hadil Shaiba ◽  
Devrim Unal ◽  
...  

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185938-185949
Author(s):  
T. Gopalakrishnan ◽  
D. Ruby ◽  
Fadi Al-Turjman ◽  
Deepak Gupta ◽  
Irina V. Pustokhina ◽  
...  

2019 ◽  
Vol 99 ◽  
pp. 346-355 ◽  
Author(s):  
Xianlong Zhao ◽  
Kexin Yang ◽  
Qimei Chen ◽  
Duo Peng ◽  
Hao Jiang ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1446 ◽  
Author(s):  
Liang Huang ◽  
Xu Feng ◽  
Luxin Zhang ◽  
Liping Qian ◽  
Yuan Wu

This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs’ energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm.


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