A Linear Algebraic Approach for Loss Tomography in Mesh Topologies Using Network Coding

Gui, Jiaqi; Shah-Mansouri, Vahid; Wong, Vincent W. S.

doi:10.1109/icc.2010.5501840

Cited by 4 publications

(7 citation statements)

References 12 publications

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“…The size of the probe packets is decided by means of combining the different sets of subgraphs with overlapping links as one subgraph set, where the cardinality of the set of leaf nodes that corresponds to the combination of these subgraph sets is the maximum of the cardinalities of the sets of leaf nodes corresponding to each individual subgraph set. This extends the lower bound on probe size of [34] for a set of subgraphs with overlapping links, as follows: for the probes that are transmitted on subgraph set G e , the probe size should satisfy l G e ≥ max e∈E R (G e ) P(e) to obtain valid end-to-end observations. The minimum probe packet size is max e∈E R (G e ) P (e) , where E R (G e ) is the set of end links in the subgraph set G e and P (e) is the number of paths that contain link e.…”

Section: Loss Estimationmentioning

confidence: 84%

“…The work in [35] extends [34] by addressing the problems that were not explored there. Firstly, an algorithm is developed in order to find a valid probe coding scheme such that the minimum probe size is achieved.…”

Section: Loss Estimationmentioning

confidence: 98%

“…In [34], a linear algebraic approach for developing consistent estimators of link loss rates in mesh topologies using network coding is presented. The network is modeled as a directed acyclic graph G = (V, E ) and S, R represent the set of source nodes and the set of receiver nodes, respectively.…”

Section: Loss Estimationmentioning

confidence: 99%

“…Fragouli et al [27] binary trees Sattari et al [28] m-ary trees, M-by-N DAGs Jithin et al [30] M-by-N DAGs, asynchronous sources Yao et al [31] RLNC, NRSC, passive tomography Mohammad et al [33] RLNC, congested link location Gui et al [34] mesh DAG topologies Gui et al [35] mesh DAG topologies, minimum probe size Shah-Mansouri et al [36] WSN, RLNC, subspace property, virtual sources Sattari et al [37] trees and general topologies Sattari et al [38] trees with multiple sources Fan et al [52] weighted 1 minimization, expander graphs Chen et al [53] link congestion probabilities, greedy iterative algorithm Takemoto et al [54] measurement paths construction, low-quality link detection Morita et al [56] wireless multihop networks, GFT, spatially dependent channels Bandara et al [57] scalable, adaptive fault localization Firooz et al [41] k-identifiability, expander graphs Fattaholmanan et al [43] collaborative distributed framework, individual matrix for every node Wang et al [44] expander graphs, 1 minimization Fan et al [45] synchronization errors, constrained 1 − 2 optimization Nakanishi et al [46] no clock synchronization, reflective NT Nakanishi et al [47] synchronization errors, differential routing matrix Kinsho et al [48] mobile networks, GFT & passive measurements Wei et al [49] dynamic networks, line graph model Table 2. Overview of key points, advantages, and performance of selected tomographic methods.…”

Section: Network Compressed Loss Delay Topology Comments Coding Sensingmentioning

confidence: 99%

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A Review of Advanced Algebraic Approaches Enabling Network Tomography for Future Network Infrastructures

et al. 2020

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Network tomography has emerged as one of the lean approaches for efficient network monitoring, especially aiming at addressing the ever-increasing requirements for scaling and efficiency in modern network architectures and infrastructures. In this paper, we explore network coding and compressed sensing as enabling technologies in the context of network tomography. Both approaches capitalize on algebraic tools for achieving accuracy while allowing scaling of operation as the size of the monitored network increases. Initially, a brief overview of the tomographic problems and the related classification of methods is provided to better comprehend the problems encountered and solutions provided to date. Subsequently, we present representative approaches that employ either one of the aforementioned technologies and we comparatively describe their fundamental operation. Eventually, we provide a qualitative comparison of features and approaches that can be used for further research and technology development for network monitoring in future Internet infrastructures.

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Section: Loss Estimationmentioning

confidence: 84%

Section: Loss Estimationmentioning

confidence: 98%

Section: Loss Estimationmentioning

confidence: 99%

Section: Network Compressed Loss Delay Topology Comments Coding Sensingmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Advanced Algebraic Approaches Enabling Network Tomography for Future Network Infrastructures

et al. 2020

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“…Lin et al [86] proposed a passive loss tomography scheme with network coding for wireless sensor networks. See Gui et al [43] also.…”

Section: Link Loss Rate Inferencementioning

confidence: 99%

Survey of Network Coding and Its Applications

Matsuda

Noguchi

Takine

2011

IEICE Trans. Commun.

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SUMMARYThis survey summarizes the state-of-the-art research on network coding, mainly focusing on its applications to computer networking. Network coding generalizes traditional store-and-forward routing techniques by allowing intermediate nodes in networks to encode several received packets into a single coded packet before forwarding. Network coding was proposed in 2000, and since then, it has been studied extensively in the field of computer networking. In this survey, we first summarize linear network coding and provide a taxonomy of network coding research, i.e., the network coding design problem and network coding applications. Moreover, the latter is subdivided into throughput/capacity enhancement, robustness enhancement, network tomography, and security. We then discuss the fundamental characteristics of network coding and diverse applications of network coding in details, following the above taxonomy.

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A Survey on Network Tomography With Network Coding

Qin

Dai

Huang

et al. 2014

IEEE Commun. Surv. Tutorials

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Abstract-The overhead of internal network monitoring motivates techniques of network tomography. Network coding (NC) presents a new opportunity for network tomography as NC introduces topology-dependent correlation that can be further exploited in topology estimation. Compared with traditional methods, network tomography with NC has many advantages such as the improvement of tomography accuracy and the reduction of complexity in choosing monitoring paths. In this paper we first introduce the problem of tomography with NC and then propose the taxonomy criteria to classify various methods. We also present existing solutions and future trend. We expect that our comprehensive review on network tomography with NC can serve as a good reference for researchers and practitioners working in the area.Index Terms-network tomography, network coding, topology recovery, link loss estimation, link delay inference, bottleneck discovery, failure localization

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A Linear Algebraic Approach for Loss Tomography in Mesh Topologies Using Network Coding

Cited by 4 publications

References 12 publications

A Review of Advanced Algebraic Approaches Enabling Network Tomography for Future Network Infrastructures

A Review of Advanced Algebraic Approaches Enabling Network Tomography for Future Network Infrastructures

Survey of Network Coding and Its Applications

A Survey on Network Tomography With Network Coding

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