Optimal schedule of mobile edge computing for internet of things using partial information

Xinchen Lyu, Wei Ni, Hui Tian, Ren Ping Liu, Xin Wang, Georgios B. Giannakis, Arogyaswami Paulraj

Research output: Contribution to journalArticle

58 Scopus citations

Abstract

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 In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks.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 The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback.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.

Original languageEnglish (US)
Article number8063331
Pages (from-to)2606-2615
Number of pages10
JournalIEEE Journal on Selected Areas in Communications
Volume35
Issue number11
DOIs
StatePublished - Nov 2017

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Keywords

  • Internet of Things
  • Lyapunov optimization
  • Mobile edge computing
  • Partial information

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