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30
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 266 (64 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.
Routing Without Routes: The Backpressure Collection Protocol
"... Current data collection protocols for wireless sensor networks are mostly based on quasistatic minimumcost routing trees. We consider an alternative, highlyagile approach called backpressure routing, in which routing and forwarding decisions are made on a perpacket basis. Although there is a con ..."
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Cited by 57 (6 self)
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Current data collection protocols for wireless sensor networks are mostly based on quasistatic minimumcost routing trees. We consider an alternative, highlyagile approach called backpressure routing, in which routing and forwarding decisions are made on a perpacket basis. Although there is a considerable theoretical literature on backpressure routing, it has not been implemented on practical systems to date due to concerns about packet looping, the effect of link losses, large packet delays, and scalability. Addressing these concerns, we present the Backpressure Collection Protocol (BCP) for sensor networks, the first ever implementation of dynamic backpressure routing in wireless networks. In particular, we demonstrate for the first time that replacing the traditional FIFO queue service in backpressure routing with LIFO queues reduces the average endtoend packet delays for delivered packets drastically (75 % under high load, 98 % under low load). Further, we improve backpressure scalability by introducing a new concept of floating queues into the backpressure framework. Under static network settings, BCP shows a more than 60 % improvement in maxmin rate over the state of the art Collection Tree Protocol (CTP). We also empirically demonstrate the superior delivery performance of BCP in highly dynamic network settings, including conditions of extreme external interference and highly mobile sinks. 1.
Distributed opportunistic scheduling for ad hoc communications with imperfect channel information,” Submitted to
 V. CONCLUSION In
"... Abstract — Distributed opportunistic scheduling is studied for wireless adhoc networks, where many links contend for one channel using random access. In such networks, distributed opportunistic scheduling (DOS) involves a process of joint channel probing and distributed scheduling. It has been show ..."
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Cited by 47 (9 self)
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Abstract — Distributed opportunistic scheduling is studied for wireless adhoc networks, where many links contend for one channel using random access. In such networks, distributed opportunistic scheduling (DOS) involves a process of joint channel probing and distributed scheduling. It has been shown that under perfect channel estimation, the optimal DOS for maximizing the network throughput is a pure threshold policy. In this paper, this formalism is generalized to explore DOS under noisy channel estimation, where the transmission rate needs to be backed off from the estimated rate to reduce the outage. It is shown that the optimal scheduling policy remains to be thresholdbased, and that the rate threshold turns out to be a function of the variance of the estimation error and be a functional of the backoff rate function. Since the optimal backoff rate is intractable, a suboptimal linear backoff scheme that backs off the estimated signaltonoise ratio (SNR) and hence the rate is proposed. The corresponding optimal backoff ratio and rate threshold can be obtained via an iterative algorithm. Finally, simulation results are provided to illustrate the tradeoff caused by increasing training time to improve channel estimation at the cost of probing efficiency. I.
Order optimal delay for opportunistic scheduling in multiuser wireless uplinks and downlinks
 Proc. of Allerton Conf. on Communication, Control, and Computing (invited paper
, 2006
"... Abstract — We consider a onehop wireless network with independent time varying channels and N users, such as a multiuser uplink or downlink. We first show that general classes of scheduling algorithms that do not consider queue backlog necessarily incur average delay that grows at least linearly wi ..."
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Cited by 44 (6 self)
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Abstract — We consider a onehop wireless network with independent time varying channels and N users, such as a multiuser uplink or downlink. We first show that general classes of scheduling algorithms that do not consider queue backlog necessarily incur average delay that grows at least linearly with N. We then construct a dynamic queuelength aware algorithm that stabilizes the system and achieves an average delay that is independent of N. This is the first analytical demonstration that O(1) delay is achievable in such a multiuser wireless setting. The delay bounds are achieved via a technique of queue grouping together with basic Lyapunov stability and statistical multiplexing concepts.
Delay Reduction via Lagrange Multipliers in Stochastic Network Optimization
, 2009
"... In this paper, we consider the problem of reducing network delay in stochastic network utility optimization problems. We start by studying the recently proposed quadratic Lyapunov function based algorithms (QLA). We show that for every stochastic problem, there is a corresponding deterministic prob ..."
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Cited by 30 (14 self)
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In this paper, we consider the problem of reducing network delay in stochastic network utility optimization problems. We start by studying the recently proposed quadratic Lyapunov function based algorithms (QLA). We show that for every stochastic problem, there is a corresponding deterministic problem, whose dual optimal solution “exponentially attracts” the network backlog process under QLA. In particular, the probability that the backlog vector under QLA deviates from the attractor is exponentially decreasing in their Euclidean distance. This not only helps to explain how QLA achieves the desired performance but also suggests that one can roughly “subtract out ” a Lagrange multiplier from the system induced by QLA. We thus develop a family of Fast Quadratic Lyapunov based Algorithms (FQLA) that achieve an [O(1/V), O(log 2 (V))] performancedelay tradeoff for problems with a discrete set of action options, and achieve a squareroot tradeoff for continuous problems. This is similar to the optimal performancedelay tradeoffs achieved in prior work by Neely (2007) via driftsteering methods, and shows that QLA algorithms can also be used to approach such performance. These results highlight the “network gravity ” role of Lagrange Multipliers in network scheduling. This role can be viewed as the counterpart of the “shadow price” role of Lagrange Multipliers in flow regulation for classic flowbased network problems.
Delaybased network utility maximization
 in Proc. IEEE INFOCOM 2010
, 2010
"... Abstract—It is well known that maxweight policies based on a queue backlog index can be used to stabilize stochastic networks, and that similar stability results hold if a delay index is used. Using Lyapunov Optimization, we extend this analysis to design a utility maximizing algorithm that uses ex ..."
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Cited by 22 (1 self)
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Abstract—It is well known that maxweight policies based on a queue backlog index can be used to stabilize stochastic networks, and that similar stability results hold if a delay index is used. Using Lyapunov Optimization, we extend this analysis to design a utility maximizing algorithm that uses explicit delay information from the headofline packet at each user. The resulting policy is shown to ensure deterministic worstcase delay guarantees, and to yield a throughpututility that differs from the optimally fair value by an amount that is inversely proportional to the delay guarantee. Our results hold for a general class of 1hop networks, including packet switches and multiuser wireless systems with time varying reliability. I.
Stochastic Optimization for Markov Modulated Networks with Application to Delay Constrained Wireless Scheduling
, 2009
"... We consider a wireless system with a small number of delay constrained users and a larger number of users without delay constraints. We develop a scheduling algorithm that reacts to time varying channels and maximizes throughput utility (to within a desired proximity), stabilizes all queues, and sa ..."
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Cited by 13 (8 self)
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We consider a wireless system with a small number of delay constrained users and a larger number of users without delay constraints. We develop a scheduling algorithm that reacts to time varying channels and maximizes throughput utility (to within a desired proximity), stabilizes all queues, and satisfies the delay constraints. The problem is solved by reducing the constrained optimization to a set of weighted stochastic shortest path problems, which act as natural generalizations of maxweight policies to Markov modulated networks. We also present approximation results that do not require apriori statistical knowledge, and discuss the additional complexity and delay incurred as compared to systems without delay constraints. The solution technique is general and applies to other constrained stochastic network optimization problems.
Lifobackpressure achieves near optimal utilitydelay tradeoff. ArXiv
, 2010
"... There has been considerable recent work developing a new stochastic network utility maximization framework using Backpressure algorithms, also known as MaxWeight. A key open problem has been the development of utilityoptimal algorithms that are also delay efficient. In this paper, we show that Back ..."
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Cited by 13 (7 self)
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There has been considerable recent work developing a new stochastic network utility maximization framework using Backpressure algorithms, also known as MaxWeight. A key open problem has been the development of utilityoptimal algorithms that are also delay efficient. In this paper, we show that Backpressure, when combined with the LIFO queueing discipline (called LIFOBackpressure), is able to achieve a utility that is within O(1/V) of the optimal for any V ≥ 1, while maintaining an average delay of O([log(V)] 2) for all but a tiny fraction of the network traffic. This result holds for general stochastic network optimization problems and general Markovian dynamics. Remarkably, the performance of LIFOBackpressure can be achieved by simply changing the queueing discipline; it requires no other modifications of the original Backpressure algorithm. We validate the results through empirical measurements from a sensor network testbed, which show good match between theory and practice. I.
Stochastic Network Utility Maximization A tribute to Kelly’s paper published in this journal a decade ago
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Opportunism, backpressure, and stochastic optimization with the wireless broadcast advantage
 Asilomar Conference on Signals, Systems, and Computers
, 2008
"... Abstract — This paper provides a tutorial treatment of recent stochastic network optimization techniques, including Lyapunov network optimization, backpressure, and maxweight decision making. A new technique of place holder bits that improves delay for networking problems with general costs is also ..."
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Cited by 12 (7 self)
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Abstract — This paper provides a tutorial treatment of recent stochastic network optimization techniques, including Lyapunov network optimization, backpressure, and maxweight decision making. A new technique of place holder bits that improves delay for networking problems with general costs is also presented. An example application is given for the problem of energyaware scheduling and routing in a wireless mobile network with channel errors and multireceiver diversity. The Diversity Backpressure