Ren, C, Lyu, X, Ni, W, Tian, H & Liu, RP 2019, 'Distributed Online Learning of Fog Computing under Nonuniform Device Cardinality', IEEE Internet of Things Journal, vol. 6, no. 1, pp. 1147-1159.View/Download from: Publisher's site
© 2014 IEEE. Processing data around the point of capture, fog computing can support computationally demanding Internet-of-Things (IoT) services. Distributed online optimization is important given the size of IoT, but challenging due to time variations of random traffic and nonuniform connectivity (or cardinality) of edge servers and IoT devices. This paper presents a distributed online learning approach to asymptotically minimizing the time-average cost of fog computing in the absence of the a-priori knowledge on traffic randomness, for light-weight, and delay-tolerant application scenarios. Stochastic gradient descent is exploited to decouple the optimizations between time slots. A graph matching problem is then formulated for every time slot by decoupling and unifying the nonuniform cardinalities, and solved in a distributed manner by developing a new linear (1/2)-approximation method. We prove that the optimality loss resulting from the distributed approximate graph matching method can be compensated and diminish by increasing the learning time. Corroborated by simulations, the proposed distributed online learning is asymptotically optimal and superior to the state of the art in terms of throughput and energy efficiency.
Lyu, X, Tian, H, Ni, W, Zhang, Y, Zhang, P & Liu, RP 2018, 'Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing', IEEE Transactions on Communications, vol. 66, no. 6, pp. 2603-2616.View/Download from: UTS OPUS or Publisher's site
© 1972-2012 IEEE. Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve (1 - ∈)-approximation of the computationally prohibitive maximum energy saving at a time-complexity linearly scaling with devices. ∈ is linear to the quantization interval of energy. The key idea is to transform the mixed integer programming of task admission to an integer programming (IP) problem with the optimal substructure by pre-admitting resource-restrained devices. Another important aspect is a new quantized dynamic programming algorithm which we develop to exploit the optimal substructure and solve the IP. The quantization interval of energy is optimized to achieve an [O(∈),O(1/∈)]-tradeoff between the optimality loss and time complexity of the algorithm. Simulations show that our approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.
Lyu, X, Ren, C, Ni, W, Tian, H & Liu, RP 2018, 'Distributed Optimization of Collaborative Regions in Large-Scale Inhomogeneous Fog Computing', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 36, no. 3, pp. 574-586.View/Download from: UTS OPUS or Publisher's site
Lyu, X, Ni, W, Tian, H, Liu, RP, Wang, X, Giannakis, GB & Paulraj, A 2018, 'Distributed Online Optimization of Fog Computing for Selfish Devices With Out-of-Date Information', IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, vol. 17, no. 11, pp. 7704-7717.View/Download from: UTS OPUS or Publisher's site
Lyu, X, Ren, C, Ni, W, Tian, H, Liu, RP & Guo, YJ 2018, 'Multi-Timescale Decentralized Online Orchestration of Software-Defined Networks', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 36, no. 12, pp. 2716-2730.View/Download from: UTS OPUS or Publisher's site
He, S, Tian, H & Lyu, X 2017, 'Edge Popularity Prediction Based on Social-Driven Propagation Dynamics', IEEE Communications Letters, vol. 21, no. 5, pp. 1027-1030.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. Caching contents in edge networks can reduce latency and lighten the burden on backhaul links. Since the capacity of cache nodes is limited, accurate content popularity distribution is crucial to the effectual usage of cache capacity. However, existing popularity prediction models stem from big data and, hence, may suffer poor accuracy due to the small population in edge caching. In this letter, we propose a social-driven propagation dynamics-based prediction model, which requires neither training phases nor prior knowledge. Specifically, we first explore social relationships to bridge the gap between small population and prediction accuracy under susceptible-infected-recovery model. Then, a discrete-time markov chain approach is proposed to predict the viewing probability of certain contents from the perspective of individuals. Simulations validate that our proposed model outperforms other solutions significantly, by improving up to 94% in accuracy and 99% less runtime overhead.
Lyu, X, Tian, H, Sengul, C & Zhang, P 2017, 'Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds', IEEE Transactions on Vehicular Technology, vol. 66, no. 4, pp. 3435-3447.View/Download from: Publisher's site
He, S, Tian, H, Lyu, X, Nie, G & Fan, S 2017, 'Distributed Cache Placement and User Association in Multicast-Aided Heterogeneous Networks', IEEE Access, vol. 5, pp. 25365-25376.View/Download from: Publisher's site
Lyu, X, Ni, W, Tian, H, Liu, RP, Wang, X, Giannakis, GB & Paulraj, A 2017, 'Optimal schedule of mobile edge computing for internet of things using partial information', IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2606-2615.View/Download from: UTS OPUS or Publisher's site
© 1983-2012 IEEE. 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.
Lyu, X, Tian, H, Ni, W, Liu, RP & Zhang, P 2017, 'Adaptive Centralized Clustering Framework for Software-Defined Ultra-Dense Wireless Networks', IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol. 66, no. 9, pp. 8553-8557.View/Download from: UTS OPUS or Publisher's site