Energy Minimization and Offloading Number Maximization in Wireless Mobile Edge Computing

Peifeng Li, Yuansheng Luo, Kezhi Wang, Kun Yang

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)
5 Downloads (Pure)

Abstract

With the fast development of mobile edge computing (MEC), user equipments (UEs) can enjoy much higher experience than before by offloading the tasks to its close edge cloud. In this paper, we assume there are several edge clouds, each of which has limited resource. We aim to maximize the number of offloaded tasks and minimize the energy consumption of all the UEs and edge clouds, by selecting the best edge cloud for each UE to offload. We formulate the problem as a mixed-integer non-convex optimization, which is difficult to solve in general. By transforming this problem into a minimum-cost maximum-flow (MCMF) problem, we can solve it efficiently. The simulation shows that our proposed algorithm has better performance and lower complexity than the conventional solutions.
Original languageEnglish
DOIs
Publication statusPublished - Dec 2018
EventIEEE Global Communications Conference - Abu Dhabi National Exhibition Centre, Abu Dhabi, United Arab Emirates
Duration: 9 Dec 201813 Dec 2018
http://globecom2018.ieee-globecom.org/

Conference

ConferenceIEEE Global Communications Conference
Abbreviated titleGLOBECOM 2018
CountryUnited Arab Emirates
CityAbu Dhabi
Period9/12/1813/12/18
Internet address

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