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Improved bounds on the throughput efficiency of greedy maximal scheduling in wireless networks
 in Proc. ACM MOBIHOC’09
, 2009
"... Due to its low complexity, Greedy Maximal Scheduling (GMS), also known as Longest Queue First (LQF), has been studied extensively for wireless networks. However, GMS can result in degraded throughput performance in general wireless networks. In this paper, we prove that GMS achieves 100 % throughput ..."
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Cited by 22 (3 self)
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Due to its low complexity, Greedy Maximal Scheduling (GMS), also known as Longest Queue First (LQF), has been studied extensively for wireless networks. However, GMS can result in degraded throughput performance in general wireless networks. In this paper, we prove that GMS achieves 100 % throughput in all networks with eight nodes or less, under the twohop interference model. Further, we obtain performance bounds that improve upon previous results for larger networks up to a certain size. We also provide a simple proof to show that GMS can be implemented using only local neighborhood information in networks of any size.
A parallel approximation algorithm for the weighted maximum matching problem
 In Proc. Seventh Int. Conf. on Parallel Processing and Applied Mathematics (PPAM
, 2007
"... Abstract. We consider the problem of computing a weighted edge matching in a large graph using a parallel algorithm. This problem has application in several areas of combinatorial scientific computing. Since an exact algorithm for the weighted matching problem is both fairly expensive to compute and ..."
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Cited by 8 (2 self)
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Abstract. We consider the problem of computing a weighted edge matching in a large graph using a parallel algorithm. This problem has application in several areas of combinatorial scientific computing. Since an exact algorithm for the weighted matching problem is both fairly expensive to compute and hard to parallelise we instead consider fast approximation algorithms. We analyse a distributed algorithm due to Hoepman [8] and show how this can be turned into a parallel algorithm. Through experiments using both complete as well as sparse graphs we show that our new parallel algorithm scales well using up to 32 processors. 1
Parallel Community Detection for Massive Graphs
"... Abstract. Tackling the current volume of graphstructured data requires parallel tools. We extend our work on analyzing such massive graph data with the first massively parallel algorithm for community detection that scales to current data sizes, scaling to graphs of over 122 million vertices and ne ..."
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Cited by 8 (4 self)
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Abstract. Tackling the current volume of graphstructured data requires parallel tools. We extend our work on analyzing such massive graph data with the first massively parallel algorithm for community detection that scales to current data sizes, scaling to graphs of over 122 million vertices and nearly 2 billion edges in under 7300 seconds on a massively multithreaded Cray XMT. Our algorithm achieves moderate parallel scalability without sacrificing sequential operational complexity. Community detection partitions a graph into subgraphs more densely connected within the subgraph than to the rest of the graph. We take an agglomerative approach similar to Clauset, Newman, and Moore’s sequential algorithm, merging pairs of connected intermediate subgraphs to optimize different graph properties. Working in parallel opens new approaches to high performance. On smaller data sets, we find the output’s modularity compares well with the standard sequential algorithms.
Efficient Distributed Weighted Matchings on Trees
 In Proc. SIROCCO 2006
"... Abstract. In this paper, we study distributed algorithms to compute a weighted matching that have constant (or at least sublogarithmic) running time and that achieve approximation ratio 2 + ɛ or better. In fact we present two such synchronous algorithms, that work on arbitrary weighted trees. The f ..."
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Cited by 6 (2 self)
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Abstract. In this paper, we study distributed algorithms to compute a weighted matching that have constant (or at least sublogarithmic) running time and that achieve approximation ratio 2 + ɛ or better. In fact we present two such synchronous algorithms, that work on arbitrary weighted trees. The first algorithm is a randomised distributed algorithm that computes a weighted matching of an arbitrary weighted tree, that approximates the maximum weighted matching by a factor 2 + ɛ. The running time is O(1). The second algorithm is deterministic, and approximates the maximum weighted matching by a factor 2 + ɛ, but has running time O(log ∗ V ). Our algorithms can also be used to compute maximum unweighted matchings on regular and almost regular graphs within a constant approximation. 1
Distributed Lowcomplexity Maximum Throughput Scheduling in Wireless Backhaul Networks
 in Proc. IEEE INFOCOM 2007
, 2006
"... Abstract — We introduce a lowcomplexity distributed slotted MAC protocol that can support all feasible arrival rates in a wireless backhaul network (WBN). For arbitrary wireless networks, such a maximum throughput protocol has been notoriously hard to realize because (i) even if global topology inf ..."
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Cited by 4 (1 self)
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Abstract — We introduce a lowcomplexity distributed slotted MAC protocol that can support all feasible arrival rates in a wireless backhaul network (WBN). For arbitrary wireless networks, such a maximum throughput protocol has been notoriously hard to realize because (i) even if global topology information is available, the problem of computing the optimal link transmission set at each slot is NPcomplete (ii) no bounds exist on the number of steps required for such a computation (perslot overhead). For the logical tree structures induced by the WBN traffic matrices, we first introduce a centralized algorithm that solves the optimal scheduling problem in a number of steps at most linear in the number of nodes in the network. This is achieved by discovering and exploiting a novel set of graphtheoretical properties of the WBN contention graph. Guided by the centralized algorithm, we design a distributed protocol where, at the beginning of each slot, nodes coordinate and incrementally compute the optimal link transmission set. We then introduce an algorithm to compute the minimum number of steps to complete this computation, thus minimizing the perslot overhead. Using both analysis and simulations, we show that in practice our protocol yields low overhead when implemented over existing wireless technologies and significantly outperforms existing suboptimal distributed slotted scheduling mechanisms. I.
Algorithms, Theory
"... This paper aims at achieving optimal rate allocation for data aggregation in wireless sensor networks. We first formulate this rate allocation problem as a network utility maximization problem. Due to its nonconvexity, we take a couple of variable substitutions on the original problem and transform ..."
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This paper aims at achieving optimal rate allocation for data aggregation in wireless sensor networks. We first formulate this rate allocation problem as a network utility maximization problem. Due to its nonconvexity, we take a couple of variable substitutions on the original problem and transform it into an approximate problem, which is convex. We then apply duality theory to decompose this approximate problem into a rate control subproblem and a scheduling subproblem. Based on this decomposition, a distributed algorithm for joint rate control and scheduling is designed, and proved to approach arbitrarily close to the optimum of the approximate problem. Finally, we show that our approximate solution can achieve nearoptimal performance through both theoretical analysis and simulations.
Maximum Weighted Matching Using the Partitioned Global Address Space Model
"... Efficient parallel algorithms for problems such as maximum weighted matching are central to many areas of combinatorial scientific computing. Manne and Bisseling [13] presented a parallel approximation algorithm which is well suited to distributed memory computers. This algorithm is based on a distr ..."
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Efficient parallel algorithms for problems such as maximum weighted matching are central to many areas of combinatorial scientific computing. Manne and Bisseling [13] presented a parallel approximation algorithm which is well suited to distributed memory computers. This algorithm is based on a distributed protocol due to Hoepman [9]. In the current paper, a partitioned global address space (PGAS) implementation is presented. PGAS programmers have the conveniences of using a shared memory model, which provides implicit communication between processes using normal loads and stores. Since the shared memory is partitioned according to the affinity of a process, one is also able to exploit data locality. This paper addresses the main differences between the PGAS and MPI implementations of the ManneBisseling algorithm. It highlights some advantages of using the PGAS model such as shorter, simpler code, similarity to the sequential algorithm, and options for finegrained and coarsegrained communication. 1.
Quality of Information based Data Selection and Transmission in Wireless Sensor Networks
"... Abstract—In this paper, we provide a quality of information (QoI) based data selection and transmission service for classification missions in sensor networks. We first identify the two aspects of QoI, data reliability and data redundancy, and then propose metrics to estimate them. In particular, re ..."
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Abstract—In this paper, we provide a quality of information (QoI) based data selection and transmission service for classification missions in sensor networks. We first identify the two aspects of QoI, data reliability and data redundancy, and then propose metrics to estimate them. In particular, reliability implies the degree to which a sensor node contributes to the classification mission, and can be estimated through exploring the agreement between this node and the majority of others. On the other hand, redundancy represents the information overlap among different sensor nodes, and can be measured via investigating the similarity of their clustering results. Based on the proposed QoI metrics, we formulate an optimization problem that aims at maximizing the reliability of sensory data while eliminating their redundancies under the constraint of network resources. We decompose this problem into a data selection subproblem and a data transmission subproblem, and develop a distributed algorithm to solve them separately. The advantages of our schemes are demonstrated through the simulations on not only synthetic data but also a set of real audio records.
THE GENEROUS FINANCIAL HELP OF THE TECHNION IS GRATEFULLY ACKNOWLEDGED. Acknowledgments
"... I would like to thank the many people who made this work possible and made the experience truly remarkable. I am deeply indebted to my adviser, Prof. Idit Keidar, for her continuous guidance and unwavering confidence in where we were going. Working and thinking with you has been humbling, and I than ..."
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I would like to thank the many people who made this work possible and made the experience truly remarkable. I am deeply indebted to my adviser, Prof. Idit Keidar, for her continuous guidance and unwavering confidence in where we were going. Working and thinking with you has been humbling, and I thank you for always being available and keen to help when I needed it. I would like to thank Prof. Yoram Moses, for his friendship and advice over the years. I thank my fellow students at the Technion, who struggled and succeeded with me, encouraged and motivated me. Thank you Ayelet, Kirill and Daniel, and my friends in Idit’s group – Dima, Ittai, Nathaniel, Alex and Oved. I want to thank the staff of the EE faculty for their help and kindness, and I owe special thanks to Keren SekerGafni, who with patience and care made all the administrative fuss painless. I want to thank my family who supported me and pampered me endlessly during stressful times; my two incredible parents who are an inspiration to me. I would also like to thank the Guz family who were drawn into this with me and were ever positive and encouraging.