TY - JOUR
T1 - Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks
AU - Chen, Lixing
AU - Zhou, Sheng
AU - Xu, Jie
N1 - Funding Information:
Manuscript received March 16, 2017; revised December 6, 2017 and April 25, 2018; accepted May 22, 2018; approved by IEEE/ACM TRANS-ACTIONS ON NETWORKING Editor S. Chong. Date of publication June 21, 2018; date of current version August 16, 2018. This work was supported by the Nature Science Foundation of China under Grant 91638204, Grant 61571265, and Grant 61621091. (Corresponding author: Lixing Chen.) L. Chen and J. Xu are with the Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146 USA (e-mail: lx.chen@miami.edu; jiexu@miami.edu).
PY - 2018/8
Y1 - 2018/8
N2 - The (ultra-)dense deployment of small-cell base stations (SBSs) endowed with cloud-like computing functionalities paves the way for pervasive mobile edge computing, enabling ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. To handle spatially uneven computation workloads in the network, cooperation among SBSs via workload peer offloading is essential to avoid large computation latency at overloaded SBSs and provide high quality of service to end users. However, performing effective peer offloading faces many unique challenges due to limited energy resources committed by self-interested SBS owners, uncertainties in the system dynamics, and co-provisioning of radio access and computing services. This paper develops a novel online SBS peer offloading framework, called online peer offloading (OPEN), by leveraging the Lyapunov technique, in order to maximize the long-term system performance while keeping the energy consumption of SBSs below individual long-term constraints. OPEN works online without requiring information about future system dynamics, yet provides provably near-optimal performance compared with the oracle solution that has the complete future information. In addition, this paper formulates a peer offloading game among SBSs and analyzes its equilibrium and efficiency loss in terms of the price of anarchy to thoroughly understand SBSs' strategic behaviors, thereby enabling decentralized and autonomous peer offloading decision making. Extensive simulations are carried out and show that peer offloading among SBSs dramatically improves the edge computing performance.
AB - The (ultra-)dense deployment of small-cell base stations (SBSs) endowed with cloud-like computing functionalities paves the way for pervasive mobile edge computing, enabling ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. To handle spatially uneven computation workloads in the network, cooperation among SBSs via workload peer offloading is essential to avoid large computation latency at overloaded SBSs and provide high quality of service to end users. However, performing effective peer offloading faces many unique challenges due to limited energy resources committed by self-interested SBS owners, uncertainties in the system dynamics, and co-provisioning of radio access and computing services. This paper develops a novel online SBS peer offloading framework, called online peer offloading (OPEN), by leveraging the Lyapunov technique, in order to maximize the long-term system performance while keeping the energy consumption of SBSs below individual long-term constraints. OPEN works online without requiring information about future system dynamics, yet provides provably near-optimal performance compared with the oracle solution that has the complete future information. In addition, this paper formulates a peer offloading game among SBSs and analyzes its equilibrium and efficiency loss in terms of the price of anarchy to thoroughly understand SBSs' strategic behaviors, thereby enabling decentralized and autonomous peer offloading decision making. Extensive simulations are carried out and show that peer offloading among SBSs dramatically improves the edge computing performance.
KW - Edge computing
KW - energy efficiency
KW - load management
KW - peer-to-peer computing
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U2 - 10.1109/TNET.2018.2841758
DO - 10.1109/TNET.2018.2841758
M3 - Article
AN - SCOPUS:85049084094
VL - 26
SP - 1619
EP - 1932
JO - IEEE/ACM Transactions on Networking
JF - IEEE/ACM Transactions on Networking
SN - 1063-6692
IS - 4
M1 - 8392534
ER -