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 In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks.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 The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback.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.
Bibliographical noteFunding Information:
Manuscript received April 1, 2017; revised September 12, 2017; accepted September 25, 2017. Date of publication October 9, 2017; date of current version December 1, 2017. This work was supported by the National Natural Science Foundation of China under Grant 61471060 and Grant 61421061. (Corresponding author: Hui Tian.) X. Lyu is with the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China, and also with the Global Big Data Technologies Center, University of Technology Sydney, Sydney, NSW 2007, Australia.
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- Internet of Things
- Lyapunov optimization
- Mobile edge computing
- Partial information