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138
PROMISE: Peer-to-Peer Media Streaming Using CollectCast
, 2003
"... We present the design, implementation, and evaluation of PROMISE, a novel peer-to-peer media streaming system encompassing the key functions of peer lookup, peer-based aggregated streaming, and dynamic adaptations to network and peer conditions. Particularly, PROMISE is based on a new application l ..."
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Cited by 172 (12 self)
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We present the design, implementation, and evaluation of PROMISE, a novel peer-to-peer media streaming system encompassing the key functions of peer lookup, peer-based aggregated streaming, and dynamic adaptations to network and peer conditions. Particularly, PROMISE is based on a new application level P2P service called CollectCast. CollectCast performs three main functions: (1) inferring and leveraging the underlying network topology and performance information for the selection of senders; (2) monitoring the status of peers and connections and reacting to peer/connection failure or degradation with low overhead; (3) dynamically switching active senders and standby senders, so that the collective network performance out of the active senders remains satisfactory. Based on both real-world measurement and simulation, we evaluate the performance of PROMISE, and discuss lessons learned from our experience with respect to the practicality and further optimization of PROMISE.
Network tomography: recent developments
- Statistical Science
, 2004
"... Today's Int ernet is a massive, dist([/#][ net work which cont inuest o explode in size as ecommerce andrelatH actH]M/# grow. Thehet([H(/#]H( and largelyunregulatS stregula of t/ Int/HH3 renderstnde such as dynamicroutc/[ opt2]3fl/ service provision, service level verificatflH( and det(2][/ of ..."
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Cited by 132 (4 self)
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Today's Int ernet is a massive, dist([/#][ net work which cont inuest o explode in size as ecommerce andrelatH actH]M/# grow. Thehet([H(/#]H( and largelyunregulatS stregula of t/ Int/HH3 renderstnde such as dynamicroutc/[ opt2]3fl/ service provision, service level verificatflH( and det(2][/ of anomalous/malicious behaviorext/[(22 challenging. The problem is compounded bytS fact tct onecannot rely ont[ cooperatH2 of individual servers and routSS t aid intS collect[3 of net workt/[S measurement vits fort/]3 t/]3] In many ways, net workmonit]/#[ and inference problems bear a st[fl[ resemblancet otnc "inverse problems" in which key aspect of asystfl are not direct/ observable. Familiar signal processing orst[]23/#[S problems such ast omographic imagereconst[/#[S] and phylogenet# tog identn/HH2[M have int erest3/ connect[HU t tonn arising in net working. This artflMM int/ ducesnet workt/H3]S]/ y, a new field which we believe will benefit greatU from tm wealt of stH2](/#S( ttH2 andalgorit#S( It focuses especially on recent development s int2 field includingtl applicat[fl of pseudolikelihoodmetfl ds andt reeestfl3](/# formulat]M23 Keyw ords:Net workt/HflS33/ y, pseudo-likelihood,t opology identn/]H22(/ tn est/]H tst 1 Introducti6 Nonet work is an island, ent/S ofitS[S] everynet work is a piece of an int/]SS work, a part of t/ main . Alt[]][ administHSHSS of small-scale net works can monit( localt ra#ccondit][/ and ident ify congest/# point s and performance botU((2/ ks, very few net works are complet/# # Rui Castroan Robert Nowak are with theDepartmen t of Electricalan ComputerEnterX Rice Unc ersity,Houston TX; Mark Coates is with the Departmen t of Electricalan ComputerEnterX McGill UnG ersity,Mon treal, Quebec,Can Gan Lian an Bin Yu are with theDepartmen t of Statistics,...
An Algebraic Approach to Practical and Scalable Overlay Network Monitoring
- IN ACM SIGCOMM
, 2004
"... Overlay network monitoring enables distributed Internet applications to detect and recover from path outages and periods of degraded performance within seconds. For an overlay network with n end hosts, existing systems either require O(n²) measurements, and thus lack scalability, or can only estima ..."
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Cited by 100 (9 self)
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Overlay network monitoring enables distributed Internet applications to detect and recover from path outages and periods of degraded performance within seconds. For an overlay network with n end hosts, existing systems either require O(n²) measurements, and thus lack scalability, or can only estimate the latency but not congestion or failures. Our earlier extended abstract [1] briefly proposes an algebraic approach that selectively monitors k linearly independent paths that can fully describe all the O(n²) paths. The loss rates and latency of these k paths can be used to estimate the loss rates and latency of all other paths. Our scheme only assumes knowledge of the underlying IP topology, with links dynamically varying between lossy and normal. In this
Pseudo Likelihood Estimation in Network Tomography
, 2003
"... Network monitoring and diagnosis are key to improving network performance. The difficulties of performance monitoring lie in today's fast growing Internet, accompanied by increasingly heterogeneous and unregulated structures. Moreover, these tasks become even harder since one cannot rely on the ..."
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Cited by 85 (4 self)
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Network monitoring and diagnosis are key to improving network performance. The difficulties of performance monitoring lie in today's fast growing Internet, accompanied by increasingly heterogeneous and unregulated structures. Moreover, these tasks become even harder since one cannot rely on the collaboration of individual routers and servers to directly measure network traffic. Even though the aggregatory nature of possible network measurements gives rise to inverse problems, existing methods for solving inverse problems are usually computationally intractable or statistically inefficient.
Network delay tomography
- IEEE Transactions on Signal Processing
, 2003
"... Abstract—The substantial overhead of performing internal network monitoring motivates techniques for inferring spatially localized information about performance using only end-to-end measurements. In this paper, we present a novel methodology for inferring the queuing delay distributions across inte ..."
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Cited by 66 (3 self)
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Abstract—The substantial overhead of performing internal network monitoring motivates techniques for inferring spatially localized information about performance using only end-to-end measurements. In this paper, we present a novel methodology for inferring the queuing delay distributions across internal links in the network based solely on unicast, end-to-end measurements. The major contributions are: 1) we formulate a measurement procedure for estimation and localization of delay distribution based on end-to-end packet pairs; 2) we develop a simple way to compute maximum likelihood estimates (MLEs) using the expectation-maximization (EM) algorithm; 3) we develop a new estimation methodology based on recently proposed nonparametric, wavelet-based density estimation method; and 4) we optimize the computational complexity of the EM algorithm by developing a new fast Fourier transform implementation. Realistic network simulations are carried out using network-level simulator ns-2 to demonstrate the accuracy of the estimation procedure. Index Terms—Computer network performance, delay estimation, Internet, tomography. I.
Network Tomography of Binary Network Performance Characteristics
- IEEE Transactions on Information Theory
, 2006
"... In network performance tomography, characteristics of the network interior, such as link loss and packet latency, are inferred from correlated end-to-end measurements. Most work to date is based on exploiting packet level correlations, e.g., of multicast packets or unicast emulations of them. Howeve ..."
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Cited by 61 (3 self)
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In network performance tomography, characteristics of the network interior, such as link loss and packet latency, are inferred from correlated end-to-end measurements. Most work to date is based on exploiting packet level correlations, e.g., of multicast packets or unicast emulations of them. However, these methods are often limited in scope—multicast is not widely deployed—or require deployment of additional hardware or software infrastructure. Some recent work has been successful in reaching a less detailed goal: identifying the lossiest network links using only uncorrelated end-to-end measurements. In this paper we abstract the properties of network performance that allow this to be done and exploit them with a quick and simple inference algorithm that, with high likelihood, identifies the worst performing links. We give several examples of real network performance measures that exhibit the required properties. Moreover, the algorithm is sufficiently simple that we can analyze its performance explicitly.
A wavelet-based approach to detect shared congestion
- in Proc. ACM SIGCOMM
"... Abstract—Per-flow congestion control helps endpoints fairly and efficiently share network resources. Better utilization of network resources can be achieved, however, if congestion management algorithms can determine when two different flows share a congested link. Such knowledge can be used to impl ..."
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Cited by 60 (13 self)
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Abstract—Per-flow congestion control helps endpoints fairly and efficiently share network resources. Better utilization of network resources can be achieved, however, if congestion management algorithms can determine when two different flows share a congested link. Such knowledge can be used to implement cooperative congestion control or improve the overlay topology of a P2P system. Previous techniques to detect shared congestion either assume a common source or destination node, drop-tail queueing, or a single point of congestion. We propose in this paper a novel technique, applicable to any pair of paths on the Internet, without such limitations. Our technique employs a signal processing method, wavelet denoising, to separate queueing delay caused by network congestion from various other delay variations. Our wavelet-based technique is evaluated through both simulations and Internet experiments. We show that, when detecting shared congestion of paths with a common endpoint, our technique provides faster convergence and higher accuracy while using fewer packets than previous techniques, and that it also accurately determines when there is no shared congestion. Furthermore, we show that our technique is robust and accurate for paths without a common endpoint or synchronized clocks; more specifically, it can tolerate a synchronization offset of up to one second between two packet flows. I.
Towards unbiased end-to-end network diagnosis
- In ACM SIGCOMM
, 2006
"... Abstract—Internet fault diagnosis is extremely important for end-users, overlay network service providers (like Akamai [1]), and even Internet service providers (ISPs). However, because link-level properties cannot be uniquely determined from end-to-end measurements, the accuracy of existing statist ..."
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Cited by 55 (2 self)
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Abstract—Internet fault diagnosis is extremely important for end-users, overlay network service providers (like Akamai [1]), and even Internet service providers (ISPs). However, because link-level properties cannot be uniquely determined from end-to-end measurements, the accuracy of existing statistical diagnosis approaches is subject to uncertainty from statistical assumptions about the network. In this paper, we propose a novel least-biased end-to-end network diagnosis (in short, LEND) system for inferring link-level properties like loss rate. We define a minimal identifiable link sequence (MILS) as a link sequence of minimal length whose properties can be uniquely identified from end-to-end measurements. We also design efficient algorithms to find all the MILSs and infer their loss rates for diagnosis. Our LEND system works for any network topology and for both directed and undirected properties and incrementally adapts to network topology and property changes. It gives highly accurate estimates of the loss rates of MILSs, as indicated by both extensive simulations and Internet experiments. Furthermore, we demonstrate that such diagnosis can be achieved with fine granularity and in near real-time even for reasonably large overlay networks. Finally, LEND can supplement existing statistical inference approaches and provide smooth tradeoff between diagnosis accuracy and granularity. Index Terms—Internet diagnosis, linear algebra, network measurement. I.
Traffic Matrix Estimation on a Large IP Backbone -- A Comparison on Real Data
- IMC'04
, 2004
"... This paper considers the problem of estimating the point-to -point traffic matrix in an operational IP backbone. Contrary to previous studies, that have used a partial traffic matrix or demands estimated from aggregated Netflow traces, we use a unique data set of complete traffic matrices from a glo ..."
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Cited by 51 (1 self)
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This paper considers the problem of estimating the point-to -point traffic matrix in an operational IP backbone. Contrary to previous studies, that have used a partial traffic matrix or demands estimated from aggregated Netflow traces, we use a unique data set of complete traffic matrices from a global IP network measured over five-minute intervals. This allows us to do an accurate data analysis on the time-scale of typical link-load measurements and enables us to make a balanced evaluation of different traffic matrix estimation techniques. We describe the data collection infrastructure, present spatial and temporal demand distributions, investigate the stability of fan-out factors, and analyze the mean-variance relationships between demands. We perform a critical evaluation of existing and novel methods for traffic matrix estimation, including recursive fanout estimation, worst-case bounds, regularized estimation techniques, and methods that rely on mean-variance relationships. We discuss the weaknesses and strengths of the various methods, and highlight differences in the results for the European and American subnetworks.
Compressive sensing over graphs
- in Proc. IEEE INFOCOM
, 2011
"... Abstract—In this paper, motivated by network inference and tomography applications, we study the problem of compressive sensing for sparse signal vectors over graphs. In particular, we are interested in recovering sparse vectors representing the properties of the edges from a graph. Unlike existing ..."
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Cited by 32 (3 self)
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Abstract—In this paper, motivated by network inference and tomography applications, we study the problem of compressive sensing for sparse signal vectors over graphs. In particular, we are interested in recovering sparse vectors representing the properties of the edges from a graph. Unlike existing compressive sensing results, the collective additive measurements we are allowed to take must follow connected paths over the underlying graph. For a sufficiently connected graph with n nodes, it is shown that, using O(k log(n)) path measurements, we are able to recover any k-sparse link vector (with no more than k nonzero elements), even though the measurements have to follow the graph path constraints. We mainly show that the computationally efficient 1 minimization can provide theoretical guarantees for inferring such k-sparse vectors with O(k log(n)) path measurements from the graph. I.
