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Monitor Placement for Maximal Identifiability in Network Tomography
"... We investigate the problem of placing a given number of monitors in a communication network to identify the maximum number of link metrics from endtoend measurements between monitors, assuming that link metrics are additive, and measurement paths cannot contain cycles. Motivated by our previous r ..."
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We investigate the problem of placing a given number of monitors in a communication network to identify the maximum number of link metrics from endtoend measurements between monitors, assuming that link metrics are additive, and measurement paths cannot contain cycles. Motivated by our previous result that complete identification of all link metrics can require a large number of monitors, we focus on partial identification using a limited number of monitors. The basis to our solution is an efficient algorithm for determining all identifiable links for a given monitor placement. Based on this algorithm, we develop a polynomialtime greedy algorithm to incrementally place monitors such that each newly placed monitor maximizes the number of additional identifiable links. We prove that the proposed algorithm is optimal for 2vertexconnected networks, and demonstrate that it is nearoptimal for several real ISP topologies that are not 2vertexconnected. Our solution provides a quantifiable tradeoff between level of identifiability and available monitor resources.
Link Identifiability in Communication Networks with Two Monitors
"... We investigate the problem of identifying individual link performance metrics in a communication network by measuring endtoend metrics of selected paths between monitors, under the assumption that link metrics are additive and constant during the measurement, and measurement paths cannot contain ..."
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Cited by 3 (1 self)
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We investigate the problem of identifying individual link performance metrics in a communication network by measuring endtoend metrics of selected paths between monitors, under the assumption that link metrics are additive and constant during the measurement, and measurement paths cannot contain cycles. In a previous work, we developed an algorithm that places the minimum number of monitors to identify all link metrics. However, even the minimum number can be large in some practical networks (e.g., 60 % of all the nodes), suggesting high monitor deployment cost. In this paper, we study the dual problem where given a fixed number of monitors, we want to place them to maximize the number of identifiable link metrics, with concrete results for the case of two monitors. The significance of the twomonitor case is that all the tomographic computation can be performed at the destination monitor without shipping measurements to a central node, thus enabling endhostbased network monitoring. We develop an efficient algorithm to determine all identifiable links in an arbitrary network with a given placement of two monitors, based on which we propose an optimal twomonitor placement algorithm to maximize the number of identifiable links. Our evaluation on real ISP topologies shows that although a large number of monitors is needed to identify all link metrics, we can usually identify a substantial portion (up to 97%) of the links using a single pair of optimally placed monitors.
Prediction of Bandwidth and Additive Metrics for Large Scale Network Tomography
"... ABSTRACT For real time communication services over the Internet, it is important to be able to predict in advance the quality of a call before relaying it over a particular path. In this paper we show how to predict the distribution of the endtoend bandwidth, latency, jitter, and loss of a call f ..."
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ABSTRACT For real time communication services over the Internet, it is important to be able to predict in advance the quality of a call before relaying it over a particular path. In this paper we show how to predict the distribution of the endtoend bandwidth, latency, jitter, and loss of a call from an arbitrary user X to an arbitrary user Y through particular components of the Internet, given a dataset of millions of calls among other users. This work is the first to infer component bandwidth distributions for an arbitrary network topology, and the first to infer component bandwidth and additive metric distributions for an arbitrary network topology at large scale. On a dataset of over seven million global Skype calls, we demonstrate significant performance improvement compared to a baseline approach used in today's commercial systems.