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110
Fairness and optimal stochastic control for heterogeneous networks
 Proc. IEEE INFOCOM, March 2005. TRANSACTIONS ON NETWORKING, VOL
, 2008
"... Abstract — We consider optimal control for general networks with both wireless and wireline components and time varying channels. A dynamic strategy is developed to support all traffic whenever possible, and to make optimally fair decisions about which data to serve when inputs exceed network capaci ..."
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Cited by 150 (29 self)
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Abstract — We consider optimal control for general networks with both wireless and wireline components and time varying channels. A dynamic strategy is developed to support all traffic whenever possible, and to make optimally fair decisions about which data to serve when inputs exceed network capacity. The strategy is decoupled into separate algorithms for flow control, routing, and resource allocation, and allows each user to make decisions independent of the actions of others. The combined strategy is shown to yield data rates that are arbitrarily close to the optimal operating point achieved when all network controllers are coordinated and have perfect knowledge of future events. The cost of approaching this fair operating point is an endtoend delay increase for data that is served by the network.
Maximizing Queueing Network Utility subject to Stability: Greedy PrimalDual Algorithm
 Queueing Systems
, 2005
"... Abstract. We study a model of controlled queueing network, which operates and makes control decisions in discrete time. An underlying random network mode determines the set of available controls in each time slot. Each control decision “produces ” a certain vector of “commodities”; it also has assoc ..."
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Cited by 133 (8 self)
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Abstract. We study a model of controlled queueing network, which operates and makes control decisions in discrete time. An underlying random network mode determines the set of available controls in each time slot. Each control decision “produces ” a certain vector of “commodities”; it also has associated “traditional” queueing control effect, i.e., it determines traffic (customer) arrival rates, service rates at the nodes, and random routing of processed customers among the nodes. The problem is to find a dynamic control strategy which maximizes a concave utility function H(X), where X is the average value of commodity vector, subject to the constraint that network queues remain stable. We introduce a dynamic control algorithm, which we call Greedy PrimalDual (GPD) algorithm, and prove its asymptotic optimality. We show that our network model and GPD algorithm accommodate a wide range of applications. As one example, we consider the problem of congestion control of networks where both traffic sources and network processing nodes may be randomly timevarying and interdependent. We also discuss a variety of resource allocation problems in wireless networks, which in particular involve average power consumption constraints and/or optimization, as well as traffic rate constraints.
A tutorial on crosslayer optimization in wireless networks
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 2006
"... This tutorial paper overviews recent developments in optimization based approaches for resource allocation problems in wireless systems. We begin by overviewing important results in the area of opportunistic (channelaware) scheduling for cellular (singlehop) networks, where easily implementable my ..."
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Cited by 128 (13 self)
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This tutorial paper overviews recent developments in optimization based approaches for resource allocation problems in wireless systems. We begin by overviewing important results in the area of opportunistic (channelaware) scheduling for cellular (singlehop) networks, where easily implementable myopic policies are shown to optimize system performance. We then describe key lessons learned and the main obstacles in extending the work to general resource allocation problems for multihop wireless networks. Towards this end, we show that a cleanslate optimization based approach to the multihop resource allocation problem naturally results in a “loosely coupled” crosslayer solution. That is, the algorithms obtained map to different layers (transport, network, and MAC/PHY) of the protocol stack are coupled through a limited amount of information being passed back and forth. It turns out that the optimal scheduling component at the MAC layer is very complex and thus needs simpler (potentially imperfect) distributed solutions. We demonstrate how to use imperfect scheduling in the crosslayer framework and describe recently developed distributed algorithms along these lines. We conclude by describing a set of open research problems.
Crosslayer congestion control, routing and scheduling design in ad hoc wireless networks
 Proc. IEEE Infocom
, 2006
"... Abstract — This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed ..."
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Cited by 98 (11 self)
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Abstract — This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or singlerate wireless devices that can mask channel variations) as a utility maximization problem with these constraints. By dual decomposition, the resource allocation problem naturally decomposes into three subproblems: congestion control, routing and scheduling that interact through congestion price. The global convergence property of this algorithm is proved. We next extend the dual algorithm to handle networks with timevarying channels and adaptive multirate devices. The stability of the resulting system is established, and its performance is characterized with respect to an ideal reference system which has the best feasible rate region at link layer. We then generalize the aforementioned results to a general model of queueing network served by a set of interdependent parallel servers with timevarying service capabilities, which models many design problems in communication networks. We show that for a general convex optimization problem where a subset of variables lie in a polytope and the rest in a convex set, the dualbased algorithm remains stable and optimal when the constraint set is modulated by an irreducible finitestate Markov chain. This paper thus presents a step toward a systematic way to carry out crosslayer design in the framework of “layering as optimization decomposition ” for timevarying channel models. I.
Joint congestion control, routing and MAC for stability and fairness in wireless networks
 IEEE Journal on Selected Areas in Communications
, 2006
"... In this work, we describe and analyze a joint scheduling, routing and congestion control mechanism for wireless networks, that asymptotically guarantees stability of the buffers and fair allocation of the network resources. The queue lengths serve as common information to different layers of the ne ..."
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Cited by 58 (8 self)
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In this work, we describe and analyze a joint scheduling, routing and congestion control mechanism for wireless networks, that asymptotically guarantees stability of the buffers and fair allocation of the network resources. The queue lengths serve as common information to different layers of the network protocol stack. Our main contribution is to prove the asymptotic optimality of a primaldual congestion controller, which is known to model different versions of TCP well.
Lowcomplexity distributed scheduling algorithms for wireless networks
 IEEE/ACM Trans. on Netw
"... Abstract — We consider the problem of distributed scheduling in wireless networks. We present two different algorithms whose performance is arbitrarily close to that of maximal schedules, but which require low complexity due to the fact that they do not necessarily attempt to find maximal schedules. ..."
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Cited by 41 (3 self)
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Abstract — We consider the problem of distributed scheduling in wireless networks. We present two different algorithms whose performance is arbitrarily close to that of maximal schedules, but which require low complexity due to the fact that they do not necessarily attempt to find maximal schedules. The first algorithm requires each link to collect local queuelength information in its neighborhood, and its complexity is independent of the size and topology of the network. The second algorithm is presented for the nodeexclusive interference model, does not require nodes to collect queuelength information even in their local neighborhoods, and its complexity depends only on the maximum node degree in the network. I.
Layering as optimization decomposition
 PROCEEDINGS OF THE IEEE
, 2007
"... Network protocols in layered architectures have historically been obtained on an ad hoc basis, and many of the recent crosslayer designs are conducted through piecemeal approaches. They may instead be holistically analyzed and systematically designed as distributed solutions to some global optimiza ..."
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Cited by 39 (17 self)
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Network protocols in layered architectures have historically been obtained on an ad hoc basis, and many of the recent crosslayer designs are conducted through piecemeal approaches. They may instead be holistically analyzed and systematically designed as distributed solutions to some global optimization problems. This paper presents a survey of the recent efforts towards a systematic understanding of “layering ” as “optimization decomposition”, where the overall communication network is modeled by a generalized Network Utility Maximization (NUM) problem, each layer corresponds to a decomposed subproblem, and the interfaces among layers are quantified as functions of the optimization variables coordinating the subproblems. There can be many alternative decompositions, each leading to a different layering architecture. This paper summarizes the current status of horizontal decomposition into distributed computation and vertical decomposition into functional modules such as congestion control, routing, scheduling, random access, power control, and channel coding. Key messages and methods arising from many recent work are listed, and open issues discussed. Through case studies, it is illustrated how “Layering as Optimization Decomposition” provides a common language to think
Joint Asynchronous Congestion Control and Distributed Scheduling for MultiHop Wireless Networks
 In IEEE INFOCOM
, 2006
"... Abstract — We consider a multihop wireless network shared by many users. For an interference model that only constrains a node to either transmit or receive at a time, but not both, we propose an architecture for fair resource allocation that consists of a distributed scheduling algorithm operating ..."
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Cited by 39 (7 self)
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Abstract — We consider a multihop wireless network shared by many users. For an interference model that only constrains a node to either transmit or receive at a time, but not both, we propose an architecture for fair resource allocation that consists of a distributed scheduling algorithm operating in conjunction with an asynchronous congestion control algorithm. We show that the proposed joint congestion control and scheduling algorithm supports at least onethird of the throughput supportable by any other algorithm, including centralized algorithms. I.
Optimal energy and delay tradeoffs for multiuser wireless downlinks
 Proc. IEEE INFOCOM
, 2006
"... Abstract — We consider the fundamental delay tradeoffs for minimizing energy expenditure in a multiuser wireless downlink with randomly varying channels. First, we extend the BerryGallager bound to a multiuser context, demonstrating that any algorithm that yields average power within O(1/V) of th ..."
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Cited by 37 (13 self)
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Abstract — We consider the fundamental delay tradeoffs for minimizing energy expenditure in a multiuser wireless downlink with randomly varying channels. First, we extend the BerryGallager bound to a multiuser context, demonstrating that any algorithm that yields average power within O(1/V) of the minimum power required for network stability must also have an average queueing delay greater than or equal to Ω ( √ V). We then develop a class of algorithms, parameterized by V, that come within a logarithmic factor of achieving this fundamental tradeoff. The algorithms overcome an exponential state space explosion, and can be implemented in real time without apriori knowledge of traffic rates or channel statistics. Further, we discover a “superfast ” scheduling mode that beats the BerryGallager bound in the exceptional case when power functions are piecewise linear. Index Terms — queueing analysis, stability, optimization, stochastic control, asymptotic tradeoffs
Polynomial complexity algorithms for full utilization of multihop wireless networks
"... In this paper, we propose and study a general framework that allows the development of distributed mechanisms to achieve full utilization of multihop wireless networks. In particular, we develop a generic randomized routing, scheduling and flow control scheme that is applicable to a large class o ..."
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Cited by 35 (12 self)
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In this paper, we propose and study a general framework that allows the development of distributed mechanisms to achieve full utilization of multihop wireless networks. In particular, we develop a generic randomized routing, scheduling and flow control scheme that is applicable to a large class of interference models. We prove that any algorithm which satisfies the conditions of our generic scheme maximizes network throughput and utilization. Then, we focus on a specific interference model, namely the twohop interference model, and develop distributed algorithms with polynomial communication and computation complexity. This is an important result given that earlier throughputoptimal algorithms developed for such a model relies on the solution to an NPhard problem. To the best of our knowledge, this is the first polynomial complexity algorithm that guarantees full utilization in multihop wireless networks. We further show that our algorithmic approach enables us to efficiently approximate the capacity region of a multihop wireless network.