Robust Monitoring of Network-wide Aggregates through Gossiping

Wuhib, Fetahi; Dam, Mads; Stadler, Rolf; Clemm, Alexander

doi:10.1109/inm.2007.374787

Cited by 33 publications

(52 citation statements)

References 31 publications

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“…A second work [17] proposes a comparison between GAP (Generic Aggregation Protocol), a basic tree-based aggregation protocol and G-GAP (Gossipbased GAP), a gossip-based protocol for continuous monitoring of aggregates. Surprisingly, in opposition to the first study, this evaluation shows that, for comparative overhead, the tree-based scheme GAP outperforms the gossip protocol G-GAP both in terms of accuracy and robustness even under a dynamic network.…”

Section: Related Workmentioning

confidence: 99%

Impact of Dynamics on Situated and Global Aggregation Schemes

Makhloufi

Doyen

Bonnet

et al. 2011

Managing the Dynamics of Networks and Services

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Abstract. Recently, numerous management approaches have emerged in order to manage networks and services in a decentralized and autonomous way. Some of them propose to minimize their cost by using a situated view when collecting aggregates for the decision making process, while others propose to improve their accuracy by using a more conventional approach which is global view. So far, little attention is given to the evaluation of situated view while many studies propose to evaluate global approaches. As a consequence, there is no work in the literature that compares the performance of situated and global aggregation schemes. Being able to choose the suitable approach for a given context is still a real challenge. Mastering it will ensure the efficiency of the autonomous management system. In this paper, we present a comparative study of situated and global schemes deployed over large scale and dynamic networks. We consider two factors: network and information dynamics. We implement typical aggregation schemes from each category and then we compare them according to the accuracy of the estimated aggregates and the efficiency of the decision making process.

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Section: Related Workmentioning

confidence: 99%

Impact of Dynamics on Situated and Global Aggregation Schemes

Makhloufi

Doyen

Bonnet

et al. 2011

Managing the Dynamics of Networks and Services

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“…However, we know that in a static network with no link errors, algorithms employing specialized routes such as tree-based algorithms will perform better than gossip algorithms in terms of communication overhead. In [28], Wuhib et al compare gossip algorithms with tree-based aggregation schemes for monitoring of wireline networks. They focus on models of "crash failures" where one node may crash and completely leave the network, and they find that tree-based schemes are more efficient than gossip algorithms in this context.…”

Section: Real Sensor Network Data and General Topologiesmentioning

confidence: 99%

Efficient Decentralized Approximation via Selective Gossip

Üstebay

Castro

Rabbat

2011

IEEE J. Sel. Top. Signal Process.

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Abstract-Recently, gossip algorithms have received much attention from the wireless sensor network community due to their simplicity, scalability and robustness. Motivated by applications such as compression and distributed transform coding, we propose a new gossip algorithm called Selective Gossip. Unlike traditional randomized gossip which computes the average of scalar values, we run gossip algorithms in parallel on the elements of a vector. The goal is to compute only the entries which are above a defined threshold in magnitude, i.e., significant entries. Nodes adaptively approximate the significant entries while abstaining from calculating the insignificant ones. Consequently, network lifetime and bandwidth are preserved. We show that with the proposed algorithm nodes reach consensus on the values of the significant entries and on the indices of insignificant ones. We illustrate the performance of our algorithm with a field estimation application. For regular topologies, selective gossip computes an approximation of the field using the wavelet transform. For irregular network topologies, we construct an orthonormal transform basis using eigenvectors of the graph Laplacian. Using two real sensor network datasets we show substantial communication savings over randomized gossip. We also propose a decentralized adaptive threshold mechanism such that nodes estimate the threshold while approximating the entries of the vector for computing the best -term approximation of the data.Index Terms-Distributed algorithms, field estimation, sparse approximation, wireless sensor networks.

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“…In [23]- [25] the authors utilize gossip algorithms to obtain an estimate of the value distribution at any node in the network. These works follow a very different approach to monitoring than to our solution does, for which, until now, the support of accuracy objectives has not been shown.…”

Section: Related Workmentioning

confidence: 99%

H-GAP: estimating histograms of local variables with accuracy objectives for distributed real-time monitoring

Jurca

Stadler

2010

IEEE Trans. Netw. Serv. Manage.

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Abstract-We present H-GAP, a protocol for continuous monitoring, which provides a management station with the value distribution of local variables across the network. The protocol estimates the histogram of local state variables for a given accuracy and with minimal overhead. H-GAP is decentralized and asynchronous to achieve robustness and scalability, and it executes on an overlay interconnecting management processes in network devices. On this overlay, the protocol maintains a spanning tree and updates the histogram through incremental aggregation. The protocol is tunable in the sense that it allows controlling, at runtime, the trade-off between protocol overhead and an accuracy objective. This functionality is realized through dynamic configuration of local filters that control the flow of updates towards the management station. The paper includes an analysis of the problem of histogram aggregation over aggregation trees, a formulation of the global optimization problem, and a distributed solution containing heuristic, tree-based algorithms. Using SUM as an example, we show how general aggregation functions over local variables can be efficiently computed with H-GAP. We evaluate our protocol through simulation using real traces. The results demonstrate the controllability of H-GAP in a selection of scenarios and its efficiency in large-scale networks.

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Robust Monitoring of Network-wide Aggregates through Gossiping

Cited by 33 publications

References 31 publications

Impact of Dynamics on Situated and Global Aggregation Schemes

Impact of Dynamics on Situated and Global Aggregation Schemes

Efficient Decentralized Approximation via Selective Gossip

H-GAP: estimating histograms of local variables with accuracy objectives for distributed real-time monitoring

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