Ben Liang

University of Toronto, Toronto, Ontario, Canada

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Publications (140)94.87 Total impact

  • Wei Wang, Ben Liang, Baochun Li
    IEEE Transactions on Parallel and Distributed Systems 01/2015; · 2.17 Impact Factor
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    Yicheng Lin, Wei Bao, Wei Yu, Ben Liang
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    ABSTRACT: The joint user association and spectrum allocation problem is studied for multi-tier heterogeneous networks (HetNets) in both downlink and uplink in the interference-limited regime. Users are associated with base-stations (BSs) based on the biased downlink received power. Spectrum is either shared or orthogonally partitioned among the tiers. This paper models the placement of BSs in different tiers as spatial point processes and adopts stochastic geometry to derive the theoretical mean proportionally fair utility of the network based on the coverage rate. By formulating and solving the network utility maximization problem, the optimal user association bias factors and spectrum partition ratios are analytically obtained for the multi-tier network. The resulting analysis reveals that the downlink and uplink user associations do not have to be symmetric. For uplink under spectrum sharing, if all tiers have the same target signal-to-interference ratio (SIR), distance-based user association is shown to be optimal under a variety of path loss and power control settings. For both downlink and uplink, under orthogonal spectrum partition, it is shown that the optimal proportion of spectrum allocated to each tier should match the proportion of users associated with that tier. Simulations validate the analytical results. Under typical system parameters, simulation results suggest that spectrum partition performs better for downlink in terms of utility, while spectrum sharing performs better for uplink with power control.
    12/2014;
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    ABSTRACT: In heterogeneous cellular networks (HCNs), the interference received at a user is correlated over time slots since it comes from the same set of randomly located BSs. This results in the correlations of link successes, thus affecting network performance. Under the assumptions of a K -tier Poisson network, strongest-candidate based BS association, and independent Rayleigh fading, we first quantify the correlation coefficients of interference. We observe that the interference correlation is independent of the number of tiers, BS density, SIR threshold, and transmit power. Then, we study the correlations of link successes in terms of the joint success probability over multiple time slots. We show that the joint success probability is decided by the success probability in a single time slot and a diversity polynomial, which represents the temporal interference correlation. Moreover, the parameters of HCNs have an important influence on the joint success probability by affecting the success probability in a single time slot. Particularly, we obtain the condition under which the joint success probability increases with the BS density and transmit power. We further show that the conditional success probability given prior successes only depends on the path loss exponent and the number of time slots.
    11/2014;
  • Sun Sun, Min Dong, Ben Liang
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    ABSTRACT: Based on the Gauss–Markov channel model, we investigate the stochastic feedback control for transmit beamforming in multiple-input–single-output systems and design practical implementation algorithms leveraging techniques in dynamic programming and reinforcement learning. We first validate the Markov decision process formulation of the underlying feedback control problem with a $4R$-variable ( $4R$-V) state, where $R$ is the number of the transmit antennas. Due to the high complexity of finding an optimal feedback policy under the $4R$-V state, we consider a reduced 2-V state. As opposed to a previous study that assumes the feedback problem under such a 2-V state remaining an MDP formulation, our analysis indicates that the underlying problem is no longer an MDP. Nonetheless, the approximation as an MDP is shown to be justifiable and efficient. Based on the quantized 2-V state and the MDP approximation, we propose practical implementation algorithms for feedback control with unknown state transition probabilities. In particular, we provide model-based offline and online learning algorithms, as well as a model-free learning algorithm. We investigate and compare these algorithms through extensive simulations and provide their efficiency analysis. According to these results, the application rule of these algorithms is established under both statistically stable and unstable channels.
    IEEE Transactions on Wireless Communications 09/2014; 13(9):4731-4745. · 2.76 Impact Factor
  • Wei Bao, Ben Liang
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    ABSTRACT: We present a new method for spectrum allocation in a heterogeneous cellular network with multiple tiers of randomly placed base stations and random user session arrivals. Different from previous works, inelastic network traffic is considered, so as to accommodate application sessions with fixed data rate requirements. We first quantify the average downlink sum throughput of the network in terms of a given spectrum allocation vector. We then derive concave upper and lower bounds to the throughput to allow efficient approximate solutions to optimize spectrum allocation. We show that the proposed approach has a worst case optimization performance gap of 12.6% and further demonstrate via simulation that its actual performance is often near optimal.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Wei Wang, Ben Liang, Baochun Li
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    ABSTRACT: Middleboxes are ubiquitous in today's networks. They perform deep packet processing such as content-based filtering and transformation, which requires multiple categories of resources (e.g., CPU, memory bandwidth, and link bandwidth). Depending on the processing requirement of traffic, packet processing for different flows may consume vastly different amounts of resources. Multi-resource fair queueing allows flows to obtain a fair share of these resources, providing service isolation across flows. However, previous solutions for multi-resource fair queueing are either expensive to implement at high speeds, or incurring high scheduling delay for flows with uneven weights. In this paper, we present a new fair queueing algorithm, called Group Multi-Resource Round Robin (GMR3), that schedules packets in O(1) time, while achieving near-perfect fairness with a low scheduling delay bounded by a small constant. To our knowledge, it is the first provably fair, highly efficient multi-resource fair queueing algorithm with bounded delay.
    IEEE INFOCOM 2014 - IEEE Conference on Computer Communications; 04/2014
  • Wei Wang, Ben Liang, Baochun Li
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    ABSTRACT: Market-driven spectrum auctions offer an efficient way to improve spectrum utilization by transferring unused or underused spectrum from its primary license holder to spectrum-deficient secondary users. Such a spectrum market exhibits strong locality in two aspects: 1) that spectrum is a local resource and can only be traded to users within the license area, and 2) that holders can partition the entire license areas and sell any pieces in the market. We design a spectrum double auction that incorporates such locality in spectrum markets, while keeping the auction economically robust and computationally efficient. Our designs are tailored to cases with and without the knowledge of bid distributions. Complementary simulation studies show that spectrum utilization can be significantly improved when distribution information is available. Therefore, an auctioneer can start from one design without any a priori information, and then switch to the other alternative after accumulating sufficient distribution knowledge. With minor modifications, our designs are also effective for a profit-driven auctioneer aiming to maximize the auction revenue.
    IEEE Transactions on Mobile Computing 01/2014; 13(1):75-88. · 2.91 Impact Factor
  • IEEE Transactions on Wireless Communications 01/2014; 14(2):1-1. · 2.76 Impact Factor
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    Wei Bao, Ben Liang
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    ABSTRACT: Multi-tier architecture improves the spatial reuse of radio spectrum in cellular networks, and user classification allows consideration for diverse user service requirements. However, they introduce complicated heterogeneity in the spatial distribution of transmitters, which brings new challenges in interference analysis. In this work, we present a stochastic geometric model to evaluate the uplink interference in a two-tier network considering multi-type users and base stations. Each type of tier-1 users and tier-2 base stations are modeled as independent homogeneous Poisson point processes, and tier-2 users are modeled as locally non-homogeneous clustered Poisson point processes centered at tier-2 base stations. By applying a superposition-aggregation-superposition approach, we quantify the interference at both tiers. Our model is also able to capture the impact of two types of exclusion regions, where either tier-2 base stations or tier-2 users are restricted in order to avoid cross-tier interference. As an important application of this analytical model, an intensity planning scenario is investigated, in which we aim to maximize the total income of the network operator with respect to the intensities of tier-2 cells, under constraints on the outage probabilities of tier-1 users. The result of our interference analysis suggests that this maximization can be converted to a standard convex optimization problem. Finally, numerical studies further demonstrate the correctness of our analysis.
    IEEE Transactions on Wireless Communications 12/2013; · 2.76 Impact Factor
  • Min Dong, Ben Liang, Qiang Xiao
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    ABSTRACT: We consider amplify-and-forward multi-antenna relaying between a single pair of source and destination under relay per-antenna power constraints. We design the optimal relay processing matrix to minimize the maximum per-antenna power budget for a received SNR target. With given transmit and receive beamformers at the source and destination, respectively, we first focus on the equivalent system with single-antenna source and destination. Although non-convex, we show that the optimization satisfies strong Lagrange duality and can be solved in the Lagrangian dual domain. We reveal a prominent structure of this problem, by establishing its duality with direct SIMO beamforming system with an uncertain noise. This enables us to derive a semi-closed form expression for the optimal relay processing matrix that depends on a set of dual variables, which can be determined through numerical optimization with a significantly reduced problem space. We further show that the dual problem has a semi-definite programming form, which enables efficient numerical optimization methods to determine the dual variables with polynomial complexity. Using this result, the reverse problem of SNR maximization under a set of relay per-antenna power constraints is then addressed. We then consider the maximum relay beamforming achievable rate under different combinations of antenna setups at source and destination. In particular, we generalize the duality to MIMO relay beamforming vs. direct MIMO beamforming, and establish the dual relation of the two systems for different multi-antenna setups at source and destination.
    IEEE Transactions on Signal Processing 12/2013; 61(23):6076-6090. · 3.20 Impact Factor
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    ABSTRACT: A dynamic optimization algorithm is proposed for the joint allocation of subframes, resource blocks, and power in the Type 1 inband relaying scheme mandatory in the LTE-Advanced standard. Following the general framework of Lyapunov optimization, we decompose the original problem into three sub-problems in the forms of convex programming, linear programming, and mixed-integer programming. We solve the last sub-problem in the Lagrange dual domain, showing that it has zero duality gap, and that a primal optimum can be obtained with probability one. The proposed algorithm dynamically adapts to traffic and channel fluctuations, it accommodates both instantaneous and average power constraints, and it obtains arbitrarily near-optimal sum utility of each user's average throughput. Simulation results demonstrate that the joint optimum can significantly outperform suboptimal alternatives.
    IEEE Transactions on Wireless Communications 11/2013; 12(11):5668-5678. · 2.76 Impact Factor
  • Wei Wang, Baochun Li, Ben Liang
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    ABSTRACT: Middleboxes are widely deployed in today's enterprise networks. They perform a wide range of important network functions, including WAN optimizations, intrusion detection systems, network and application level firewalls, etc. Depending on the processing requirement of traffic, packet processing for different traffic flows may consume vastly different amounts of hardware resources (e.g., CPU and link bandwidth). Multi-resource fair queueing allows each traffic flow to receive a fair share of multiple middlebox resources. Previous schemes for multi-resource fair queueing, however, are expensive to implement at high speeds. Specifically, the time complexity to schedule a packet is O(log n), where n is the number of backlogged flows. In this paper, we design a new multi-resource fair queueing scheme that schedules packets in a way similar to Elastic Round Robin. Our scheme requires only O(1) work to schedule a packet and is simple enough to implement in practice. We show, both analytically and experimentally, that our queueing scheme achieves nearly perfect Dominant Resource Fairness.
    2013 21st IEEE International Conference on Network Protocols (ICNP); 10/2013
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    Wei Bao, Ben Liang
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    ABSTRACT: We study joint spectrum allocation and user association in heterogeneous cellular networks with multiple tiers of base stations. A stochastic geometric approach is applied as the basis to derive the average downlink user data rate in a closed-form expression. Then, the expression is employed as the objective function in jointly optimizing spectrum allocation and user association, which is of non-convex programming in nature. A computationally efficient Structured Spectrum Allocation and User Association (SSAUA) approach is proposed, solving the optimization problem optimally when the density of users is low, and near-optimally with a guaranteed performance bound when the density of users is high. A Surcharge Pricing Scheme (SPS) is also presented, such that the designed association bias values can be achieved in Nash equilibrium. Simulations and numerical studies are conducted to validate the accuracy and efficiency of the proposed SSAUA approach and SPS.
    09/2013;
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    Wei Bao, Ben Liang
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    ABSTRACT: We introduce a comprehensive analytical framework to compare between open access and closed access in two-tier femtocell networks, with regard to uplink interference and outage. Interference at both the macrocell and femtocell levels is considered. A stochastic geometric approach is employed as the basis for our analysis. We further derive sufficient conditions for open access and closed access to outperform each other in terms of the outage probability, leading to closed-form expressions to upper and lower bound the difference in the targeted received power between the two access modes. Simulations are conducted to validate the accuracy of the analytical model and the correctness of the bounds.
    08/2013;
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    Wei Wang, Baochun Li, Ben Liang
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    ABSTRACT: We study the multi-resource allocation problem in cloud computing systems where the resource pool is constructed from a large number of heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, and storage. We design a multi-resource allocation mechanism, called DRFH, that generalizes the notion of Dominant Resource Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH provides a number of highly desirable properties. With DRFH, no user prefers the allocation of another user; no one can improve its allocation without decreasing that of the others; and more importantly, no user has an incentive to lie about its resource demand. As a direct application, we design a simple heuristic that implements DRFH in real-world systems. Large-scale simulations driven by Google cluster traces show that DRFH significantly outperforms the traditional slot-based scheduler, leading to much higher resource utilization with substantially shorter job completion times.
    07/2013;
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    Wei Wang, Baochun Li, Ben Liang
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    ABSTRACT: Infrastructure-as-a-Service (IaaS) clouds offer diverse instance purchasing options. A user can either run instances on demand and pay only for what it uses, or it can prepay to reserve instances for a long period, during which a usage discount is entitled. An important problem facing a user is how these two instance options can be dynamically combined to serve time-varying demands at minimum cost. Existing strategies in the literature, however, require either exact knowledge or the distribution of demands in the long-term future, which significantly limits their use in practice. Unlike existing works, we propose two practical online algorithms, one deterministic and another randomized, that dynamically combine the two instance options online without any knowledge of the future. We show that the proposed deterministic (resp., randomized) algorithm incurs no more than 2-alpha (resp., e/(e-1+alpha)) times the minimum cost obtained by an optimal offline algorithm that knows the exact future a priori, where alpha is the entitled discount after reservation. Our online algorithms achieve the best possible competitive ratios in both the deterministic and randomized cases, and can be easily extended to cases when short-term predictions are reliable. Simulations driven by a large volume of real-world traces show that significant cost savings can be achieved with prevalent IaaS prices.
    05/2013;
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    Saied Mehdian, Ben Liang
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    ABSTRACT: An optimal frame transmission scheme is presented for streaming scalable video over a link with limited capacity. The objective is to select a transmission sequence of frames and their transmission schedule such that the overall video quality is maximized. The problem is solved for two general classes of hierarchical prediction structures, which include as a special case the popular dyadic structure. Based on a new characterization of the interdependence among frames in terms of trees, structural properties of an optimal transmission schedule are derived. These properties lead to the development of a jointly optimal frame selection and scheduling algorithm, which has computational complexity that is quadratic in the number of frames. Simulation results show that the optimal scheme substantially outperforms two existing alternatives.
    05/2013;
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    Sun Sun, Min Dong, Ben Liang
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    ABSTRACT: The concept of vehicle-to-grid (V2G) has gained recent interest as more and more electric vehicles (EVs) are put to use. In this paper, we consider a dynamic aggregator-EVs system, where an aggregator centrally coordinates a large number of dynamic EVs to perform regulation service. We propose a Welfare-Maximizing Regulation Allocation (WMRA) algorithm for the aggregator to fairly allocate the regulation amount among its EVs. Compared to previous works, WMRA accommodates a wide spectrum of vital system characteristics, including dynamics of EV, limited EV battery size, EV battery degradation cost, and the cost of using external energy sources for the aggregator. The algorithm operates in real time and does not require any prior knowledge of the statistical information of the system. Theoretically, we demonstrate that WMRA is away from the optimum by O(1/V), where V is a controlling parameter depending on EV's battery size. In addition, our simulation results indicate that WMRA can substantially outperform a suboptimal greedy algorithm.
    Proceedings - IEEE INFOCOM 05/2013;
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    ABSTRACT: We present an algorithm to dynamically allocate transmission power to maximize the throughput-utility in an interference-limited network under an instantaneous sum power constraint with time-varying channels. We consider the equivalent problem of maximum admission with queue stability constraint through Lyapunov optimization. The resultant non-convex minimization problem is solved by an online algorithm consisting of two components: first, successive convex approximations to randomly choose a local minimum, and second, a modified pick-and-compare method for low-complexity convergence to a global minimum. We prove the optimality of this approach, derive its tradeoff between throughput-utility and delay, and demonstrate its performance advantage against existing methods.
    Wireless Communications Letters, IEEE. 02/2013; 2(1):22-25.
  • Wei Wang, Ben Liang, Baochun Li
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    ABSTRACT: Cloud service pricing plays a pivotal role towards the success of cloud computing. Existing pricing schemes, however, either provide no service guarantees (e.g., Spot Instances in Amazon EC2), or use static on-demand pricing in which the price cannot respond quickly to market dynamics (e.g., On-demand Instances in Amazon EC2). To overcome these problems, in this paper we design dynamic auctions where computing instances are periodically auctioned off to accommodate user demands over time. We address the two main challenges of revenue maximization and auction truthfulness. Our design encompasses a capacity allocation scheme, which determines the amount of instances to be auctioned off in each period, as well as the underlying auction mechanisms, based on dynamic payment schemes corresponding to the proposed capacity allocations over time. We show that our design is two-dimensionally truthful, and it is asymptotically optimal when demand is sufficiently high. Furthermore, by identifying certain optimization structures, we substantially reduce the computational complexity of our solution. Extensive simulations show that our design closely tracks market changes, while generating higher revenues than on-demand pricing.
    Quality of Service (IWQoS), 2013 IEEE/ACM 21st International Symposium on; 01/2013

Publication Stats

2k Citations
94.87 Total Impact Points

Institutions

  • 2003–2014
    • University of Toronto
      • Department of Electrical and Computer Engineering
      Toronto, Ontario, Canada
    • Xidian University
      Ch’ang-an, Shaanxi, China
  • 2011
    • University of Ontario Institute of Technology
      • Faculty of Engineering and Applied Science
      Oshawa, Ontario, Canada
  • 2008
    • University College Cork
      • School of Computer Science and Technology
      Cork, M, Ireland
  • 1999–2004
    • Cornell University
      • Department of Electrical and Computer Engineering
      Итак, New York, United States