Ben Liang

University of Toronto, Toronto, Ontario, Canada

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Publications (101)49.94 Total impact

  • 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
  • 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 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;
  • Source
    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
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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
  • Min Dong, Qiang Xiao, Ben Liang
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    ABSTRACT: We consider amplify-and-forward multi-antenna relaying between a single pair of source and destination under per-antenna power constraints. Our objective is to obtain the optimal relay processing matrix to minimize the maximum individual antenna power for a given received SNR target. The problem is not convex, but it can be shown to satisfy strong Lagrange duality. We reveal a prominent structure of this problem, by establishing its duality with direct point-to-point SIMO beamforming 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, thus converting the original optimization of a N×N matrix with (N+1) constraints, to a dual problem with (N+1) variables and three constraints. We further show that the dual problem has a semi-definite programming form, so that the proposed solution has polynomial worst-case complexity.
    Communications (ICC), 2012 IEEE International Conference on; 01/2012
  • Qiang Xiao, Min Dong, Ben Liang
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    ABSTRACT: We analyze the loss due to distributed nature of relay beamforming in an amplify-and-forward relay network. The optimal relay beamforming in both centralized multi-antenna single relay scenario and distributed single-antenna multi-relay scenario are compared in terms of SNR, where individual antenna/relay power budgets are assumed to account for the realistic practical constraints. For centralized relay beamforming, we show that the optimal beamforming for SNR maximization can be obtained by relay per-antenna power minimization along with bi-section search. Comparing the SNR under distributed and centralized relay beamforming, we show that distributed beamforming incurs no loss when relaying is noiseless. For noisy relaying, numerical results show that the loss due to distributed processing is around 1-2dB for two to eight relays (or antennas) at the practical SNR range of interests. Next, we consider the total in-network power minimization of the source and the multi-antenna relay under per-note per-antenna power constraints, disallowing “power sharing” among nodes and/or antennas. We proposed a heuristic iterative approach. Simulation shows that significant power saving can be made for joint in-network power minimization at both source and relay.
    Signals, Systems and Computers (ASILOMAR), 2012 Conference Record of the Forty Sixth Asilomar Conference on; 01/2012
  • Source
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    ABSTRACT: We aim to enhance the end-to-end rate of a general dual-hop relay network with multiple channels and finite modulation formats, by jointly optimizing channel pairing, power allocation, and integer bit loading. Such an optimization problem has both a discrete feasible region, due to the combinatoric nature of channel pairing, and a discrete objective, due to the bit loading requirement. For this type of mixed-integer programming problems, the Lagrange dual method generally is inapplicable, due to the non-zero duality gap. However, by exploring the structure of our problem, we are able to bound the gap to within one bit, allowing the extraction of an exact optimal integer solution. We further present complexity reduction techniques, and demonstrate that the proposed solution only requires a computational complexity that is polynomial in the number of channels, realizing efficient implementation in practical systems. Through numerical experiments, we show that the jointly optimal solution can significantly outperform common sub-optimal alternatives.
    Proceedings - IEEE INFOCOM 01/2012;
  • Wei Wang, Baochun Li, Ben Liang
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    ABSTRACT: Cloud resources are usually priced in multiple markets with different service guarantees. For example, Amazon EC2 prices virtual instances under three pricing schemes -- the subscription option (a.k.a., Reserved Instances), the pay-as-you-go offer (a.k.a., On-Demand Instances), and an auction-like spot market (a.k.a., Spot Instances) -- simultaneously. There arises a new problem of capacity segmentation: how can a provider allocate resources to different categories of pricing schemes, so that the total revenue is maximized? In this paper, we consider an EC2-like pricing scheme with traditional pay-as-you-go pricing augmented by an auction market, where bidders periodically bid for resources and can use the instances for as long as they wish, until the clearing price exceeds their bids. We show that optimal periodic auctions must follow the design of m+1-price auction with seller's reservation price. Theoretical analysis also suggests the connections between periodic auctions and EC2 spot market. Furthermore, we formulate the optimal capacity segmentation strategy as a Markov decision process over some demand prediction window. To mitigate the high computational complexity of the conventional dynamic programming solution, we develop a near-optimal solution that has significantly lower complexity and is shown to asymptotically approach the optimal revenue.
    Distributed Computing Systems (ICDCS), 2012 IEEE 32nd International Conference on; 01/2012
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    M. Hajiaghayi, Min Dong, Ben Liang
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    ABSTRACT: We study the problem of channel pairing and power allocation in a multichannel multihop relay network to enhance the end-to-end data rate. Both amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies are considered. Given fixed power allocation to the channels, we show that channel pairing over multiple hops can be decomposed into independent pairing problems at each relay, and a sorted-SNR channel pairing strategy is sum-rate optimal, where each relay pairs its incoming and outgoing channels by their SNR order. For the joint optimization of channel pairing and power allocation under both total and individual power constraints, we show that the problem can be decoupled into two subproblems solved separately. This separation principle is established by observing the equivalence between sorting SNRs and sorting channel gains in the jointly optimal solution. It significantly reduces the computational complexity in finding the jointly optimal solution. It follows that the channel pairing problem in joint optimization can be again decomposed into independent pairing problems at each relay based on sorted channel gains. The solution for optimizing power allocation for DF relaying is also provided, as well as an asymptotically optimal solution for AF relaying. Numerical results are provided to demonstrate substantial performance gain of the jointly optimal solution over some suboptimal alternatives. It is also observed that more gain is obtained from optimal channel pairing than optimal power allocation through judiciously exploiting the variation among multiple channels. Impact of the variation of channel gain, the number of channels, and the number of hops on the performance gain is also studied through numerical examples.
    IEEE Transactions on Signal Processing 11/2011; · 2.81 Impact Factor
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    M. Hajiaghayi, Min Dong, Ben Liang
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    ABSTRACT: We consider the problem of jointly optimizing channel pairing, channel-user assignment, and power allocation in a single-relay multiple-access system. The optimization objective is to maximize the weighted sum-rate under total and individual power constraints on the transmitters. By observing the special structure of a three-dimensional assignment problem derived from the original problem, we propose a polynomial-time algorithm based on continuity relaxation and dual minimization. The proposed method is shown to be optimal for all relaying strategies that give a concave rate function in terms of power constraints.
    INFOCOM, 2011 Proceedings IEEE; 05/2011
  • Min Dong, M. Hajiaghayi, B. Liang
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    ABSTRACT: In this paper, we consider the amplified-and-forward relaying in a multichannel system with linear processing capability at the relay. We propose an analytical approach to study the linear processing performance with an aim to maximize the end-to-end achievable rate, assuming equal power amplification at the relay. For the class of permutation matrices as the special case of linear processing, the problem reduces to finding the optimal channel pairing scheme that maps incoming channels to outgoing channels at the relay. The proposed unified approach allows us to obtain the corresponding optimal permutation for channel pairing, for either relaying with or without the direct path available. Particular to the case when the direct path is available, such optimal pairing strategy has not been shown before. We further demonstrate that the so obtained optimal permutation is in fact also optimal among all unitary matrices for achievable rate maximization, thus, establishing the optimality of channel pairing approach among unitary linear processing schemes. Simulation results are presented to demonstrate the achievable gain of optimal channel pairing compared with non-optimal linear processing and non-pairing.
    Information Sciences and Systems (CISS), 2011 45th Annual Conference on; 04/2011

Publication Stats

1k Citations
49.94 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
  • 1999–2004
    • Cornell University
      • Department of Electrical and Computer Engineering
      Ithaca, NY, United States