papers AI Learner
The Github is limit! Click to go to the new site.

Unified Offloading Decision Making and Resource Allocation in ME-RAN

2018-06-26
Kezhi Wang, Kun Yang, Cunhua Pan, Jiangzhou Wang

Abstract

In order to support communication and computation cooperation, we propose ME-RAN architecture, which consists of mobile edge cloud (ME) as the computation provision platform and radio access network (RAN) as the communication interface. Cooperative offloading framework is proposed to achieve the following tasks: (1) to increase user equipments’ computing capacity by triggering offloading action, especially for those UEs which cannot complete the computations locally; (2) to reduce the power consumption for all the UEs by considering limited computing and communication resources. Based on above objectives, we formulate the power minimization problem, which is shown to be a non-convex mix-integer programming. Firstly, Decentralized Local Decision Algorithm (DLDA) is proposed for each UE to estimate the possible local resource consumption and decide if offloading is in its interest. This operation will reduce the overhead and signalling in the later stage. Then, Centralized decision and resource Allocation algoRithm (CAR) is proposed to conduct the decision making and resource allocation in ME-RAN. Moreover, two low complexity algorithms, i.e., UE with largest saved power accepted first (CAR-P) and UE with smallest required data rate accepted first are proposed. Simulations show that the performance of proposed algorithms is very close to the exhaustive search but with much less complexity.

Abstract (translated by Google)
URL

https://arxiv.org/abs/1705.10384

PDF

https://arxiv.org/pdf/1705.10384


Similar Posts

Comments