Ben Liang

University of Toronto, Toronto, Ontario, Canada

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Publications (154)114.93 Total impact


  • No preview · Article · Jan 2016 · IEEE Transactions on Wireless Communications
  • Sun Sun · Min Dong · Ben Liang
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    ABSTRACT: The large-scale integration of renewable generation directly affects the reliability of power grids. We investigate the problem of power balancing in a general renewable-integrated power grid with storage and flexible loads. We consider a power grid that is supplied by one conventional generator (CG) and multiple renewable generators (RGs) each co-located with storage,and is connected with external markets. An aggregator operates the power grid to maintain power balance between supply and demand. Aiming at minimizing the long-term system cost, we first propose a real-time centralized power balancing solution, taking into account the uncertainty of the renewable generation, loads, and energy prices. We then provide a distributed implementation algorithm, significantly reducing both computational burden and communication overhead. We demonstrate that our proposed algorithm is asymptotically optimal as the storage capacity increases and the CG ramping constraint loosens. Moreover, the distributed implementation enjoys a fast convergence rate, and enables each RG and the aggregator to make their own decisions. Simulation shows that our proposed algorithm outperforms alternatives and can achieve near-optimal performance for a wide range of storage capacity.
    No preview · Article · Dec 2015 · IEEE Transactions on Smart Grid
  • Wei Wang · Ben Liang · Baochun Li
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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 − --- (resp., e/(e − 1 + ---)) times the minimum cost obtained by an optimal offline algorithm that knows the exact future a priori, where --- 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.
    No preview · Article · Dec 2015 · IEEE Transactions on Parallel and Distributed Systems
  • Wei Bao · Ben Liang
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    ABSTRACT: We introduce a stochastic analytical framework to compare the performance of open-access and closed-access modes in a two-tier femtocell network with regard to the uplink interference and the outage at both the macrocell and femtocell levels. A stochastic geometric approach is employed as the basis for our analysis. We present numerical methods to characterize the distributions of the uplink interference and the outage probabilities. We further derive sufficient conditions for the open-access and closed-access modes to outperform each other in terms of the outage probability at either the macrocell level or the femtocell level. This leads 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.
    No preview · Article · Nov 2015 · IEEE Transactions on Wireless Communications
  • Wei Bao · Ben Liang
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    ABSTRACT: Horizontal and vertical handoffs are important ramifications of user mobility in multitier heterogeneous wireless networks. They directly impact the signaling overhead and quality of calls. However, they are difficult to analyze due to the irregularly shaped network topologies introduced by multiple tiers of cells. In this paper, a stochastic geometric analysis framework on user mobility is proposed, to capture the spatial randomness and various scales of cell sizes in different tiers. We derive theoretical expressions for the rates of all handoff types experienced by an active user with arbitrary movement trajectory. Furthermore, noting that the data rate of a user depends on the set of cell tiers that it is willing to use, we provide guidelines for optimal tier selection under various user velocities, taking both the handoff rates and the data rate into consideration. Empirical studies using user mobility trace data and extensive simulation are conducted, demonstrating the correctness and usefulness of our analysis.
    No preview · Article · Oct 2015 · IEEE Journal on Selected Areas in Communications
  • Wei Wang · Ben Liang · Baochun Li
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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 coalition behavior of misreporting resource demands can benefit all its members. DRFH also ensures some level of service isolation among the users. 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.
    No preview · Article · Oct 2015 · IEEE Transactions on Parallel and Distributed Systems
  • Sun Sun · Joshua A. Taylor · Min Dong · Ben Liang
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    ABSTRACT: Phase balancing is of paramount importance for power system operation. We consider a substation connected to multiple buses, each with single phase loads, generation, and energy storage. A representative of the substation operates the system and aims to minimize the cost of all buses as well as balancing loads among phases. We first consider ideal energy storage with perfect charging and discharging efficiency, and propose a distributed real-time algorithm taking into account system uncertainty. The proposed algorithm does not require any system statistics and can ensure a certain performance guarantee. We further extend the algorithm to accommodate non-ideal energy storage. The algorithm is evaluated through numerical examples and compared with a greedy algorithm.
    No preview · Article · Jul 2015 · Proceedings of the American Control Conference

  • No preview · Conference Paper · Jun 2015
  • Wei Wang · Di Niu · Ben Liang · Baochun Li
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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 approximation 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.
    No preview · Article · Jun 2015 · IEEE Transactions on Parallel and Distributed Systems
  • Source
    Sun Sun · Ben Liang · Min Dong · Joshua A. Taylor
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    ABSTRACT: Phase balancing is essential to safe power system operation. We consider a substation connected to multiple phases, each with single-phase loads, generation, and energy storage. A representative of the substation operates the system and aims to minimize the cost of all phases and to balance loads among phases. We first consider ideal energy storage with lossless charging and discharging, and propose both centralized and distributed real-time algorithms taking into account system uncertainty. The proposed algorithm does not require any system statistics and asymptotically achieves the minimum system cost with large energy storage. We then extend the algorithm to accommodate more realistic non-ideal energy storage that has imperfect charging and discharging. The performance of the proposed algorithm is evaluated through extensive simulation and compared with that of a benchmark greedy algorithm. Simulation shows that our algorithm leads to strong performance over a wide range of storage characteristics.
    Preview · Article · May 2015 · IEEE Transactions on Power Systems
  • Sun Sun · Min Dong · Ben Liang
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    ABSTRACT: The problem of real-time power balancing in a grid-connected microgrid is studied. We consider a microgrid powered by a conventional generator (CG) and multiple renewable generators (RGs) each co-located with one distributed storage (DS) unit. An aggregator operates the microgrid and aims to minimize the long-term system cost, including all RGs' cost, the CG's cost, and the cost for exploiting external energy markets. We jointly manage the supply side, demand side, and DS units, taking into account the randomness of the system, and incorporating the ramping constraint of the CG. A real-time algorithm is proposed, which does not require any statistics of the system. We analytically characterize the gap between the system cost under our algorithm and the minimum cost, demonstrating that our algorithm is asymptotically optimal as the DS energy capacity increases and the CG ramping constraint loosens. In simulation, we compare the proposed algorithm with a greedy algorithm as well as a lower bound on the optimum. Simulation shows that our algorithm outperforms the greedy one and its performance can be close to the optimum even with small DS units.
    No preview · Article · Jan 2015
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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.
    Preview · Article · Dec 2014 · IEEE Journal on Selected Areas in Communications
  • Sun Sun · Min Dong · Ben Liang
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    ABSTRACT: Power balancing is crucial for the reliability of an electric power grid. In this paper, we consider an aggregator coordinating a group of distributed storage (DS) units to provide power balancing service to a power grid through charging or discharging. We present a real-time, distributed algorithm that enables the DS units to determine their own charging or discharging amounts. The algorithm accommodates a wide spectrum of vital system characteristics, including time-varying power imbalance amount and electricity price, finite battery size constraints, cost of using external energy sources, and battery degradation. We develop a modified Lyapunov optimization framework for real-time power balancing and provide a fast iterative method for distributed implementation. The two components interact through a novel cost cushion parameter that tunes the trade-off between system performance and convergence speed. We show analytically that the algorithm converges quickly and provides asymptotically optimal performance as the capacity of DS units increases. We further study through simulation the algorithm performance over a wide range of parameter values and demonstrate that it is highly competitive over a greedy alternative.
    No preview · Article · Dec 2014 · IEEE Journal of Selected Topics in Signal Processing
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    Min Sheng · Juan Wen · Jiandong Li · Ben Liang
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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.
    Full-text · Article · Nov 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.
    No preview · Article · Sep 2014 · IEEE Transactions on Wireless Communications
  • 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.
    No preview · Conference Paper · May 2014
  • Saied Mehdian · Ben Liang
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    ABSTRACT: We present a jointly optimal selection and scheduling scheme for the lossy transmission of frames governed by a dependency relation and a delay constraint over a link with limited capacity. A main application for this is scalable video streaming. Our objective is to select a subset of frames and decide their transmission schedule such that the overall video quality at the receiver is maximized. The problem is solved for two of the most common classes of dependency structures for video encoding, which include as a special case the popular hierarchical dyadic structure. We formally characterize the structural properties of an optimal transmission schedule in terms of frame dependency. It is shown that regardless of the subset of frames selected for transmission, any optimal schedule has an equivalent canonical form that is a subsequence of a unique universal sequence containing all frames. The canonical form can be computed efficiently through the construction of a dependency tree. This leads to separable but jointly optimal frame selection and scheduling algorithms that have quadratic computational complexity in the number of frames. Simulation with video traces demonstrates that the optimal scheme can substantially outperform existing suboptimal alternatives.
    No preview · Conference Paper · May 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.
    No preview · Conference Paper · Apr 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.
    No preview · Article · Jan 2014 · IEEE Transactions on Mobile Computing
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    ABSTRACT: A cross-network cross-layer design method is proposed to exploit the trunking, diversity, and best service assignment gains available in a heterogeneous wireless network (HWN), consisting of orthogonal radio access networks (RANs) and interference-limited RANs. Accounting for traffic-level dynamics and channel fading, we jointly design the distribution strategy for elastic and inelastic traffic, and the radio resource management strategy for RANs, in a network-separable control architecture. Optimal and quantified near-optimal radio allocation schemes are proposed for each type of RAN, which are combined into an on-line design framework that over time provides asymptotically optimal performance, maximizing the sum throughput utility for elastic traffic while guaranteeing the throughput requirements of inelastic traffic. Extensive simulation results demonstrate substantial performance improvement against suboptimal alternatives.
    No preview · Article · Jan 2014 · IEEE Transactions on Wireless Communications