TY - JOUR
T1 - Optimal schedule of mobile edge computing for internet of things using partial information
AU - Lyu, Xinchen
AU - Ni, Wei
AU - Tian, Hui
AU - Liu, Ren Ping
AU - Wang, Xin
AU - Giannakis, Georgios B.
AU - Paulraj, Arogyaswami
N1 - Publisher Copyright:
© 1983-2012 IEEE.
PY - 2017/11
Y1 - 2017/11
N2 - Mobile edge computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can get complex tasks offloaded to and processed at powerful infrastructure. Scheduling is challenging due to stochastic task arrivals and wireless channels, congested air interface, and more prominently, prohibitive feedbacks from thousands of devices. In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks. A perturbed Lyapunov function is designed to stochastically maximize a network utility balancing throughput and fairness. A knapsack problem is solved per slot for the optimal schedule, provided up-to-date knowledge on the data and energy backlogs of all devices. The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback. Corroborated by simulations, our approach is able to dramatically reduce feedbacks at no cost of optimality. The number of devices that need to feed back is reduced to less than 60 out of a total of 5000 IoT devices.
AB - Mobile edge computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can get complex tasks offloaded to and processed at powerful infrastructure. Scheduling is challenging due to stochastic task arrivals and wireless channels, congested air interface, and more prominently, prohibitive feedbacks from thousands of devices. In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks. A perturbed Lyapunov function is designed to stochastically maximize a network utility balancing throughput and fairness. A knapsack problem is solved per slot for the optimal schedule, provided up-to-date knowledge on the data and energy backlogs of all devices. The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback. Corroborated by simulations, our approach is able to dramatically reduce feedbacks at no cost of optimality. The number of devices that need to feed back is reduced to less than 60 out of a total of 5000 IoT devices.
KW - Internet of Things
KW - Lyapunov optimization
KW - Mobile edge computing
KW - Partial information
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U2 - 10.1109/JSAC.2017.2760186
DO - 10.1109/JSAC.2017.2760186
M3 - Article
AN - SCOPUS:85031779007
SN - 0733-8716
VL - 35
SP - 2606
EP - 2615
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
IS - 11
M1 - 8063331
ER -