Dynamic and self-stabilizing distributed matching

Chattopadhyay, Subhendu; Highám, Lisa; Seyffarth, Karen

doi:10.1145/571825.571877

Cited by 22 publications

(9 citation statements)

References 12 publications

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“…We leave as future work the exploration of the auction algorithm or other distributed implementations of the matching algorithm and the multicommodity flow algorithm [4,2]. There is also future work to be done on the theoretical aspects of the Deterministic Kinetically Stable Matching Algorithm, including refining the integrality gap, explaining why fractional optimal solutions appear only very rarely, and designing algorithms with theoretical approximation guarantees.…”

Section: Resultsmentioning

confidence: 99%

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Kinetically stable task assignment for networks of microservers

Abrams

Chen

Guibas

et al. 2006

Proceedings of the Fifth International Conference on Information Processing in Sensor Networks - IPSN '06

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This paper studies task assignment in a network of resource constrained computing platforms (called microservers). A task is an abstraction of a computational agent or data that is hosted by the microservers. For example, in an object tracking scenario, a task represents a mobile tracking agent, such as a vehicle location update computation, that runs on microservers, which can receive sensor data pertaining to the object of interest. Due to object motion, the microservers that can observe a particular object change over time and there is overhead involved in migrating tasks among microservers. Furthermore, communication, processing, or memory constraints, allow a microserver to only serve a limited number of objects at the same time. Our overall goal is to assign tasks to microservers so as to minimize the number of migrations, and thus be kinetically stable, while guaranteeing that as many tasks as possible are monitored at all times. When the task trajectories are known in advance, we show that this problem is NP-complete (even over just two time steps), has an integrality gap of at least 2, and can be solved optimally in polynomial time if we allow tasks to be assigned fractionally. When only probabilistic information about future movement of the tasks is known, we propose two algorithms: a multi-commodity flow based algorithm and a maximum matching algorithm. We use simulations to compare the performance of these algorithms against the optimum task allocation strategy.

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Section: Resultsmentioning

confidence: 99%

“…It can be solved using standard centralized algorithms with running time Θ(max(K, N ) 3 + KN W ) (recall that W is the size of the look-ahead window). Also, there are several possible distributed implementations [4,2,6,7] that can be adapted to our setting.…”

Section: Matching Algorithmmentioning

confidence: 99%

Kinetically stable task assignment for networks of microservers

Abrams

Chen

Guibas

et al. 2006

Proceedings of the Fifth International Conference on Information Processing in Sensor Networks - IPSN '06

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“…Surprisingly, few distributed algorithms to compute (an approximation of) the maximum weighted matching of the communication graph are known. For unweighted graphs, there are deterministic distributed algorithms computing the maximal matching in trees [KS00], and bipartite and general graphs [CHS02]. Randomised algorithms for the general case [II86] also exist.…”

Section: Introductionmentioning

confidence: 99%

Efficient Distributed Weighted Matchings on Trees

Hoepman

Kutten

Lotker

2006

Structural Information and Communication Complexity

143

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Wattenhofer et al.[WW04] derive a complicated distributed algorithm to compute a weighted matching of an arbitrary weighted graph, that is at most a factor 5 away from the maximum weighted matching of that graph. We show that a variant of the obvious sequential greedy algorithm [Pre99], that computes a weighted matching at most a factor 2 away from the maximum, is easily distributed. This yields the best known distributed approximation algorithm for this problem so far.

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“…Chattopadhyay, Higham, and Seyffarth [5] have described a distributed maximum matching algorithm which operates by repeatedly finding and removing augmenting paths. However, this algorithm converges to an optimal solution in O(N n) rounds where n is the actual number of processors in the system and N is an upperbound on n. The algorithm's performance before convergence is not known.…”

Section: Introductionmentioning

confidence: 99%

Maximizing Throughput in Minimum Rounds in an Application-Level Relay Service

Annexstein¹,

Berman²,

Strunjas³

et al. 2007

2007 Proceedings of the Ninth Workshop on Algorithm Engineering and Experiments (ALENEX)

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Application-level network relays possess many desirable properties, including support for communication between disconnected clients, increasing bandwidth between distant clients, and enabling routing around Internet failures. One problem not considered by existing systems is how to assign client load to relay servers in order to maximize throughput of the relay-system. In this paper, we are interested in the particular case where network conditions change frequently so that the ability of clients to adapt flow is restricted and each round of activity is critical. To this end, we present an algorithm, called Aggressive Increase, AAI which improves its competitive ratio in each time round that the network conditions persists. Given a relay network where a client connects to at most N servers, if network conditions persist for log(N) rounds then the algorithm's throughput becomes constant competitive. Our results improve upon the competitive ratio of previous work (of Awerbuch, Hajiaghayi, Kleinberg, and Leighton [2]). In addition we show that the AAI algorithm performs well in simulation studies as compared with the algorithm of [2] and an adaptation of the multiplicative increase algorithm of [8]. On a variety of input graphs, we show that the AAI algorithm typically reaches close to peak bandwidth levels within only a small constant (< 10) number of rounds.

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Dynamic and self-stabilizing distributed matching

Cited by 22 publications

References 12 publications

Kinetically stable task assignment for networks of microservers

Kinetically stable task assignment for networks of microservers

Efficient Distributed Weighted Matchings on Trees

Maximizing Throughput in Minimum Rounds in an Application-Level Relay Service

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