Ben Liang

University of Toronto, Toronto, Ontario, Canada

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Publications (134)68.87 Total impact

  • 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;
  • 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. · 2.40 Impact Factor
  • Source
    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.
    12/2013;
  • 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. · 2.81 Impact Factor
  • Source
    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;
  • Source
    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;
  • Min Dong, Ben Liang
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    ABSTRACT: We consider physical layer multicasting in an amplify-and-forward multi-antenna relay network. Assuming each relay antenna has individual power budget, our objective is to design the relay processing matrix to minimize the maximum individual antenna power for a given received SNR target at each destination. As the problem is NP-hard, we propose an approximate solution by solving the problem in the Lagrange dual domain. Through this Lagrange dual approach, we reveal a prominent structure, which enables us to derive a semi-closed form expression for the relay processing matrix that depends on a set of dual variables. These dual variables can be determined through an efficient semi-definite programming formulation. Compared with the traditional semi-definite relaxation (SDR) approach, the proposed solution has much lower computational complexity. Furthermore, it produces the optimal solution if such solution can be extracted from the SDR approach. Thus, the proposed solution can serve as a good alternative to the SDR approach, for the performance and complexity trade-off.
    Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013 IEEE 5th International Workshop on; 01/2013
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    ABSTRACT: This paper addresses the relation between message delivery delay and reliability for the communication between a vehicle and a road side unit (RSU). We focus on sparse vehicular sensor networks (VSNs), where timely message delivery and reliable transmission are of significant importance. We present a mathematical framework for the message delivery delay distribution for a two-lane road, where vehicles in one direction act as message carriers for the ones in the other direction and have the freedom to leave the road from randomly distributed road junctions with a certain probability. Packet generator vehicles store the original packets till meeting an RSU while sending multiple copies of each packet to packet carrier vehicles. Our analysis offers an analytical tool for an intelligent transportation system (ITS) service provider to determine the minimum RSU density required to cover a road for meeting a probabilistic requirement of the message delay. Extensive computer simulation results show the accuracy of our analysis and clearly indicate the relation of packet delay and the number of packet replicas.
    IEEE Transactions on Wireless Communications 01/2013; 12(9):4402-4413. · 2.42 Impact Factor
  • 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
  • Wei Wang, Ben Liang, Baochun Li
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    ABSTRACT: Middleboxes are widely deployed in today's datacenter networks. They perform a variety of network functions, each requiring multiple hardware resources, such as CPU cycles and link bandwidth. Depending on the functions they go through, packet processing of different traffic flows may consume a vastly different amount of hardware resources. An effective algorithm is therefore highly desired to schedule packets in a way such that multiple resources are shared in a fair and efficient manner. However, we show in this paper that there exists a fairnessefficiency tradeoff when multiple resources are scheduled. Such a tradeoff has never been a problem for traditional singleresource fair queueing (e.g., GPS, WFQ, SCFQ, DRR) - as long as the queueing schemes are work conserving, both fairness and efficiency can be achieved simultaneously - and hence has received little attention. Therefore, a new and important research problem arises: given a desired fairness-efficiency tradeoff, how can we design a packet scheduling algorithm to reinforce such a tradeoff? We present our thoughts and observations in this paper.
    Distributed Computing Systems Workshops (ICDCSW), 2013 IEEE 33rd International Conference on; 01/2013
  • Sun Sun, Min Dong, Ben Liang
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    ABSTRACT: Electric vehicles (EVs) are promising alternatives to provide ancillary services in future smart energy systems. In this paper, we consider an aggregator-EVs system providing regulation service to a power grid. To allocate regulation amount among EVs, we present both synchronous and asynchronous distributed algorithms, which align each EV's interest with the system's benefit. Compared with previous works, our algorithms accommodate a more realistic model of the aggregator-EVs system, in which EV battery degradation cost, EV charging/discharging inefficiency, EV energy gain/loss, the cost of external energy sources, and potential asynchronous communication between the aggregator and each EV are taken into account.We give sufficient conditions under which the proposed algorithms generate the optimal regulation amounts. Simulations are shown to validate our theoretical results.
    Smart Grid Communications (SmartGridComm), 2013 IEEE International Conference on; 01/2013
  • Wei Wang, Di Niu, Baochun Li, Ben Liang
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    ABSTRACT: Infrastructure-as-a-Service clouds offer diverse pricing options, including on-demand and reserved instances with various discounts to attract different cloud users. A practical problem facing cloud users is how to minimize their costs by choosing among different pricing options based on their own demands. In this paper, we propose a new cloud brokerage service that reserves a large pool of instances from cloud providers and serves users with price discounts. The broker optimally exploits both pricing benefits of long-term instance reservations and multiplexing gains. We propose dynamic strategies for the broker to make instance reservations with the objective of minimizing its service cost. These strategies leverage dynamic programming and approximate algorithms to rapidly handle large volumes of demand. Our extensive simulations driven by large-scale Google cluster-usage traces have shown that significant price discounts can be realized via the broker.
    Distributed Computing Systems (ICDCS), 2013 IEEE 33rd International Conference on; 01/2013
  • Wei Wang, Ben Liang, Baochun Li
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    ABSTRACT: Middleboxes have found widespread adoption in today's networks. They perform a variety of network functions such as WAN optimization, intrusion detection, and network-level firewalls. Processing packets to serve these functions often require multiple middlebox resources, e.g., CPU and link band-width. Furthermore, different packet traffic flows may consume significantly different amounts of various resources, depending on the network functions that are applied. Multi-resource fair queueing is therefore needed to allow flows to share multiple middlebox resources in a fair manner. In this paper, we clarify the fairness requirements of a queueing scheme and present Dominant Resource Generalized Processor Sharing (DRGPS), a fluid flow-based fair queueing idealization that strictly realizes Dominant Resource Fairness (DRF) at all times. As a form of Generalized Processor Sharing (GPS) running on multiple resources, DRGPS serves as a benchmark that practical packet-by-packet fair queueing algorithm should follow. With DRGPS, techniques and insights that have been developed for traditional fair queueing can be leveraged to schedule multiple resources. As a case study, we extend Worst-case Fair Weighted Fair Queueing (WF2Q) to the multi-resource setting and analyze its performance.
    Quality of Service (IWQoS), 2013 IEEE/ACM 21st International Symposium on; 01/2013
  • Source
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    ABSTRACT: In this correspondence, we consider the problem of linear processing design at the relay for amplified-and-forward relaying in a multichannel system. Assuming a fixed-gain power amplification at the relay, we study the linear processing structure to maximize the end-to-end achievable rate. For both the cases of relaying with or without direct path, we show that the optimal unitary processing matrix is of permutation structure, i.e., channel pairing is optimal. Furthermore, in each case, the explicit optimal channel pairing strategy is obtained based on sorting certain function of received SNR over the incoming and outgoing subchannels. This result is especially noticeable for the case with direct path, where the optimal linear processing was not known before under any power allocation. Specifically, we show that the pairing is according to the ordering of the relative SNR ratio on a subchannel over first hop to its direct path, and that of SNR strengths on subchannels over the second hop. Simulation results are presented to demonstrate the achievable gain of optimal channel pairing over non-optimal linear processing or no-pairing cases. It is also shown that the performance of channel pairing under the simple fixed-gain power allocation outperforms that under the traditional uniform power allocation.
    IEEE Transactions on Signal Processing 11/2012; 60(11):6108-6114. · 2.81 Impact Factor
  • Source
    Wei Bao, Ben Liang
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    ABSTRACT: The location of active users is an important factor in the performance analysis of mobile multicell networks, but it is difficult to quantify due to the wide variety of user mobility and session patterns. In particular, the channel holding times in each cell may be arbitrarily distributed and dependent on those in other cells. In this work, we study the stationary distribution of users by modeling the system as a multi-route queueing network with Poisson inputs. We consider arbitrary routing and arbitrary joint probability distributions for the channel holding times in each route. Using a decomposition-composition approach, we show that the user distribution (1) is insensitive to the user movement patterns, (2) is insensitive to general and dependently distributed channel holding times, (3) depends only on the average arrival rate and average channel holding time at each cell, and (4) is completely characterized by an open network with M/M/infinity queues. This result is validated by experiments with the Dartmouth user mobility traces.
    IEEE Transactions on Wireless Communications 10/2012; · 2.42 Impact Factor

Publication Stats

2k Citations
68.87 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
  • 2010
    • University of North Carolina at Greensboro
      • Department of Computer Science
      Greensboro, NC, United States
  • 2008
    • University College Cork
      • School of Computer Science and Technology
      Cork, M, Ireland
  • 1999–2004
    • Cornell University
      • Department of Electrical and Computer Engineering
      Ithaca, NY, United States