Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading

Abstract

Mobile-edge computation offloading (MECO) offloads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.

Online combinatorial based mechanism for MEC network resource allocation

“…x i = 1 if MU i is allocated the resource, and x i = 0 otherwise. Now, (10) and (11) are the constraints of the fill area ( Figure 3C). The others are the same as IP1.…”

“…Till now, lots of work have been carried out for the resources allocation problem of the MEC system. At first, the offloading decision and resources allocation depend on the properties of the task, ie, binary offloadable task or partial offloadable task . For the former, the tasks are either locally implemented or offloaded to the cloud to execute, while for the latter, a task can be segmented into at least two parts, and one of them is performed at local device, and the other is offloaded to the cloud platform.…”

“…At first, the offloading decision and resources allocation depend on the properties of the task, ie, binary offloadable task 5-10 or partial offloadable task. [11][12][13][14] For the former, the tasks are either locally implemented or offloaded to the cloud to execute, while for the latter, a task can be segmented into at least two parts, and one of them is performed at local device, and the other Int J Commun Syst. 2019;32:e3928.wileyonlinelibrary.com/journal/dac…”

A Krill Herd Behaviour Inspired Load Balancing of Tasks in Cloud Computing

“…Changsheng You et al [30] suggested a resource allotment aimed at a multiuser MECO framework based on time-division multiple access (TDMA) accompanied by orthogonal frequency-division multiple access (OFDMA). To begin with, the ideal resource allocation intended for the TDMA MECO framework with infinite or finite cloud computation capacity was framed as a convex optimization issue for reducing the weighted totality mobile energy consumption under the limitation of computation latency.…”

Reliability-Centric Analysis of Offloaded Computation in Cooperative Wearable Applications

“…The development of 3C solutions has a long history behind, as it comes from conventional cloud computing [16], through fog structures [17], all the way to the network edge [18,19]. This transition, in turn, underpins a new concept of mobile edge computing (MEC) as an emerging computing and caching paradigm [20].…”