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243
A Tutorial on Decomposition Methods for Network Utility Maximization
 IEEE J. Sel. Areas Commun
, 2006
"... Abstract—A systematic understanding of the decomposability structures in network utility maximization is key to both resource allocation and functionality allocation. It helps us obtain the most appropriate distributed algorithm for a given network resource allocation problem, and quantifies the com ..."
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Cited by 178 (4 self)
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Abstract—A systematic understanding of the decomposability structures in network utility maximization is key to both resource allocation and functionality allocation. It helps us obtain the most appropriate distributed algorithm for a given network resource allocation problem, and quantifies the comparison across architectural alternatives of modularized network design. Decomposition theory naturally provides the mathematical language to build an analytic foundation for the design of modularized and distributed control of networks. In this tutorial paper, we first review the basics of convexity, Lagrange duality, distributed subgradient method, Jacobi and Gauss–Seidel iterations, and implication of different time scales of variable updates. Then, we introduce primal, dual, indirect, partial, and hierarchical decompositions, focusing on network utility maximization problem formulations and the meanings of primal and dual decompositions in terms of network architectures. Finally, we present recent examples on: systematic search for alternative decompositions; decoupling techniques for coupled objective functions; and decoupling techniques for coupled constraint sets that are not readily decomposable. Index Terms—Congestion control, crosslayer design, decomposition, distributed algorithm, network architecture, network control by pricing, network utility maximization, optimization, power control, resource allocation. I.
Optimal Energy Management Policies for Energy Harvesting Sensor Nodes
"... We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management ..."
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Cited by 131 (4 self)
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We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable suboptimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.
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 83 (9 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.
On Combining ShortestPath and BackPressure Routing Over Multihop Wireless Networks
, 2008
"... Abstract—Backpressure based algorithms based on the algorithm by Tassiulas and Ephremides have recently received much attention for jointly routing and scheduling over multihop wireless networks. However a significant weakness of this approach has been in routing, because the traditional backpress ..."
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Cited by 63 (5 self)
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Abstract—Backpressure based algorithms based on the algorithm by Tassiulas and Ephremides have recently received much attention for jointly routing and scheduling over multihop wireless networks. However a significant weakness of this approach has been in routing, because the traditional backpressure algorithm explores and exploits all feasible paths between each source and destination. While this extensive exploration is essential in order to maintain stability when the network is heavily loaded, under light or moderate loads, packets may be sent over unnecessarily long routes and the algorithm could be very inefficient in terms of endtoend delay and routing convergence times. This paper proposes new routing/scheduling backpressure algorithms that not only guarantees network stability (throughput optimality), but also adaptively selects a set of optimal routes based on shortestpath information in order to minimize average pathlengths between each source and destination pair. Our results indicate that under the traditional backpressure algorithm, the endtoend packet delay first decreases and then increases as a function of the network load (arrival rate). This surprising lowload behavior is explained due to the fact that the traditional backpressure algorithm exploits all paths (including very long ones) even when the traffic load is light. On the otherhand, the proposed algorithm adaptively selects a set of routes according to the traffic load so that long paths are used only when necessary, thus resulting in much smaller endtoend packet delays as compared to the traditional backpressure algorithm. I.
QCSMA: Queuelength based CSMA/CA algorithms for achieving maximum throughput and low delay in wireless networks
 IN IEEE INFOCOM
, 2010
"... Recently, it has been shown that CSMAtype random access algorithms can achieve the maximum possible throughput in wireless ad hoc networks. However, the delay performance of these algorithms can be quite bad. On the other hand, although some simple heuristics (such as distributed approximations of ..."
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Cited by 59 (5 self)
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Recently, it has been shown that CSMAtype random access algorithms can achieve the maximum possible throughput in wireless ad hoc networks. However, the delay performance of these algorithms can be quite bad. On the other hand, although some simple heuristics (such as distributed approximations of greedy maximal scheduling) can yield much better delay performance for a large set of arrival rates, they may only achieve a fraction of the capacity region in general. In this paper, we propose a discretetime version of the CSMAtype random access algorithm that allows us to incorporate simple heuristics which lead to very good delay performance while retaining the throughputoptimality property. Central to our results is a discretetime distributed randomized algorithm that generates data transmission schedules according to a productform distribution, a counterpart of similar results obtained earlier for continuoustime models under the perfect CSMA assumption where collisions can never occur. An appealing feature of this algorithm is that it explicitly takes collisions into account during the exchange of control packets.
Crosslayer optimization for energyefficient wireless communications: a survey,” to be published
"... Abstract—Since battery technology has not progressed as rapidly as semiconductor technology, power efficiency has become increasingly important in wireless networking, in addition to the traditional quality and performance measures, such as bandwidth, throughput, and fairness. Energyefficient desi ..."
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Cited by 45 (7 self)
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Abstract—Since battery technology has not progressed as rapidly as semiconductor technology, power efficiency has become increasingly important in wireless networking, in addition to the traditional quality and performance measures, such as bandwidth, throughput, and fairness. Energyefficient design requires a cross layer approach as power consumption is affected by all aspects of system design, ranging from silicon to applications. This article presents a comprehensive overview of recent advances in crosslayer design for energyefficient wireless communications. We particularly focus on a systembased approaches towards energy optimal transmission and resource management across time, frequency, and spatial domains. Details related to energyefficient hardware implementations are also covered. Index Terms – energy efficiency, crosslayer, wireless communications, energy aware I.
CrossLayer Latency Minimization in Wireless Networks with SINR Constraints
 MOBIHOC’07, SEPTEMBER 9–14, 2007, MONTREAL, QUEBEC, CANADA
, 2007
"... Recently, there has been substantial interest in the design of cross
layer protocols for wireless networks. These protocols optimize
certain performance metric(s) of interest (e.g. latency, energy, rate)
by jointly optimizing the performance of multiple layers of the
protocol stack. Algorithm desig ..."
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Cited by 41 (2 self)
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Recently, there has been substantial interest in the design of cross
layer protocols for wireless networks. These protocols optimize
certain performance metric(s) of interest (e.g. latency, energy, rate)
by jointly optimizing the performance of multiple layers of the
protocol stack. Algorithm designers often use geometricgraph
theoretic models for radio interference to design such crosslayer
protocols. In this paper we study the problem of designing cross
layer protocols for multihop wireless networks using a more real
istic Signal to Interference plus Noise Ratio (SINR) model for radio
interference. The following crosslayer latency minimization prob
lem is studied: Given a set V of transceivers, and a set of source
destination pairs, (i) choose power levels for all the transceivers, (ii)
choose routes for all connections, and (iii) construct an endtoend
schedule such that the SINR constraints are satisfied at each time
step so as to minimize the makespan of the schedule (the time
by which all packets have reached their respective destinations).
We present a polynomialtime algorithm with provable worstcase
performance guarantee for this crosslayer latency minimization
problem. As corollaries of the algorithmic technique we show that
a number of variants of the crosslayer latency minimization prob
lem can also be approximated efficiently in polynomial time. Our
work extends the results of Kumar et al. (Proc. SODA, 2004) and
Moscibroda et al. (Proc. MOBIHOC, 2006). Although our algo
rithm considers multiple layers of the protocol stack, it can natu
rally be viewed as compositions of tasks specific to each layer —
this allows us to improve the overall performance while preserving
the modularity of the layered structure.
Distributed CSMA/CA algorithms for achieving maximum throughput in wireless networks
 in Proc. Inf. Theory Appl. Workshop
, 2009
"... Recently, it has been shown that CSMAtype random access algorithms can achieve the maximum throughput in wireless ad hoc networks. Central to these results is a distributed randomized algorithm which selects schedules according a productform distribution. The productform distribution is achieved ..."
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Cited by 34 (1 self)
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Recently, it has been shown that CSMAtype random access algorithms can achieve the maximum throughput in wireless ad hoc networks. Central to these results is a distributed randomized algorithm which selects schedules according a productform distribution. The productform distribution is achieved by considering a continuoustime Markov model of an idealized CSMA protocol under which collisions cannot occur. In this paper, we present an algorithm which achieves the same productform distribution in a discretetime setting where collision of data packets is avoided through the exchange of control messages (however, the control messages are allowed to collide as in the 802.11 suite of protocols). In our discretetime model, each time slot consists of a few control minislots followed by a data slot. We show that two control minislots are sufficient for our distributed scheduling algorithm to realize the same steadystate distribution as in the continuoustime case. Thus, the overhead can be as low as twice the ratio of a control minislot to a data slot. 1
Optimal Scheduling for Fair Resource Allocation in Ad Hoc Networks with Elastic and Inelastic Traffic
, 907
"... Abstract—This paper studies the problem of congestion control and scheduling in ad hoc wireless networks that have to support a mixture of besteffort and realtime traffic. Optimization and stochastic network theory have been successful in designing architectures for fair resource allocation to mee ..."
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Cited by 33 (4 self)
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Abstract—This paper studies the problem of congestion control and scheduling in ad hoc wireless networks that have to support a mixture of besteffort and realtime traffic. Optimization and stochastic network theory have been successful in designing architectures for fair resource allocation to meet longterm throughput demands. However, to the best of our knowledge, strict packet delay deadlines were not considered in this framework previously. In this paper, we propose a model for incorporating the quality of service (QoS) requirements of packets with deadlines in the optimization framework. The solution to the problem results in a joint congestion control and scheduling algorithm which fairly allocates resources to meet the fairness objectives of both elastic and inelastic flows, and perpacket delay requirements of inelastic flows. I.
Markov approximation for combinatorial network optimization
, 2010
"... Many important network design problems can be formulated as a combinatorial optimization problem. A large number of such problems, however, cannot readily be tackled by distributed algorithms. The Markov approximation framework studied in this paper is a general technique for synthesizing distribut ..."
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Cited by 31 (14 self)
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Many important network design problems can be formulated as a combinatorial optimization problem. A large number of such problems, however, cannot readily be tackled by distributed algorithms. The Markov approximation framework studied in this paper is a general technique for synthesizing distributed algorithms. We show that when using the logsumexp function to approximate the optimal value of any combinatorial problem, we end up with a solution that can be interpreted as the stationary probability distribution of a class of timereversible Markov chains. Certain carefully designed Markov chains among this class yield distributed algorithms that solve the logsumexp approximated combinatorial network optimization problem. By three case studies, we illustrate that Markov approximation technique not only can provide fresh perspective to existing distributed solutions, but also can help us generate new distributed algorithms in various domains with provable performance. We believe the Markov approximation framework will find applications in many network optimization problems, and this paper serves as a call for participation.