The Experts below are selected from a list of 31362 Experts worldwide ranked by ideXlab platform
Pegah Sattari - One of the best experts on this subject based on the ideXlab platform.
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active learning of Multiple source Multiple Destination topologies
2014Co-Authors: Pegah Sattari, Athina Markopoulou, Maciej Kurant, Animashree Anandkumar, Michael G RabbatAbstract:We consider the problem of inferring the topology of a network with M sources and N receivers (an M-by- N network), by sending probes between the sources and receivers. Prior work has shown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (1-by- N's or 2-by-2's) and then merge them to identify the M-by- N topology. We focus on the second part, which had previously received less attention in the literature. We assume that a 1-by- N topology is given and that all 2-by-2 components can be queried and learned using end-to-end probes. The problem is which 2-by-2's to query and how to merge them with the given 1-by- N, so as to exactly identify the 2-by- N topology, and optimize a number of performance metrics, including the number of queries (which directly translates into measurement bandwidth), time complexity, and memory usage. We provide a lower bound, [N/2], on the number of 2-by-2's required by any active learning algorithm and propose two greedy algorithms. The first algorithm follows the framework of Multiple hypothesis testing, in particular Generalized Binary Search (GBS). The second algorithm is called the Receiver Elimination Algorithm (REA) and follows a bottom-up approach. It requires exactly N-1 steps, which is much less than all (2N) possible 2-by-2's. Simulation results demonstrate that both algorithms correctly identify the 2-by- N topology and are near-optimal, but REA is more efficient in practice.
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active learning of Multiple source Multiple Destination topologies
2013Co-Authors: Pegah Sattari, Athina Markopoulou, Maciej Kurant, Animashree Anandkumar, Michael G RabbatAbstract:We consider the problem of inferring the topology of an M-by-N network by sending probes between M sources and N receivers. Prior work has shown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (i.e., 1-by-N's or 2-by-2's) and then merge these components to identify the M-by-N topology. In this paper, we focus on the second part. In particular, we assume that a 1by-N topology is given and that all 2-by-2 components can be queried and learned using end-to-end probes. The problem is which 2-by-2's to query and how to merge them with the 1-byN, so as to exactly identify the 2-by-N topology, and optimize a number of performance metrics including measurement traffic, time complexity, and memory usage. We provide a lower bound, ⌈N/2⌉, on the number of 2-by-2's required by any active learning algorithm and we also propose a greedy algorithm that is nearoptimal and efficient in practice. It follows a bottom-up approach: at every step, it selects two receivers, queries the corresponding 2-by-2, and merges it with the given 1-by-N. The algorithm requires exactly N - 1 steps, which is much less than all (N:2) possible 2-by-2's, and it correctly identifies the 2-by-N topology.
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Multiple source Multiple Destination topology inference using network coding
2009Co-Authors: Pegah Sattari, Athina Markopoulou, Christina FragouliAbstract:n this paper, we combine network cod- ing and tomographic techniques for topology infer- ence. Our goal is to infer the topology of a network by sending probes between a given set of Multiple sources and Multiple receivers and by having interme- diate nodes perform network coding operations. We combine and extend two ideas that have been devel- oped independently. On one hand, network coding introduces topology-dependent correlation, which can then be exploited at the receivers to infer the topology [1]. On the other hand, it has been shown that a tradi- tional (i.e., without network coding) Multiple source, Multiple receiver tomography problem can be decom- posed into Multiple two source, two receiver subprob- lems [2]. Our first contribution is to show that, when intermediate nodes perform network coding, topolog- ical information contained in network coded packets allows to accurately distinguish among all different 2- by-2 subnetwork components, which was not possible with traditional tomographic techniques. Our second contribution is to use this knowledge to merge the subnetworks and accurately reconstruct the general topology. Our approach is applicable to any general Internet-like topology, and is robust to the presence of delay variability and packet loss.
Michael G Rabbat - One of the best experts on this subject based on the ideXlab platform.
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active learning of Multiple source Multiple Destination topologies
2014Co-Authors: Pegah Sattari, Athina Markopoulou, Maciej Kurant, Animashree Anandkumar, Michael G RabbatAbstract:We consider the problem of inferring the topology of a network with M sources and N receivers (an M-by- N network), by sending probes between the sources and receivers. Prior work has shown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (1-by- N's or 2-by-2's) and then merge them to identify the M-by- N topology. We focus on the second part, which had previously received less attention in the literature. We assume that a 1-by- N topology is given and that all 2-by-2 components can be queried and learned using end-to-end probes. The problem is which 2-by-2's to query and how to merge them with the given 1-by- N, so as to exactly identify the 2-by- N topology, and optimize a number of performance metrics, including the number of queries (which directly translates into measurement bandwidth), time complexity, and memory usage. We provide a lower bound, [N/2], on the number of 2-by-2's required by any active learning algorithm and propose two greedy algorithms. The first algorithm follows the framework of Multiple hypothesis testing, in particular Generalized Binary Search (GBS). The second algorithm is called the Receiver Elimination Algorithm (REA) and follows a bottom-up approach. It requires exactly N-1 steps, which is much less than all (2N) possible 2-by-2's. Simulation results demonstrate that both algorithms correctly identify the 2-by- N topology and are near-optimal, but REA is more efficient in practice.
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active learning of Multiple source Multiple Destination topologies
2013Co-Authors: Pegah Sattari, Athina Markopoulou, Maciej Kurant, Animashree Anandkumar, Michael G RabbatAbstract:We consider the problem of inferring the topology of an M-by-N network by sending probes between M sources and N receivers. Prior work has shown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (i.e., 1-by-N's or 2-by-2's) and then merge these components to identify the M-by-N topology. In this paper, we focus on the second part. In particular, we assume that a 1by-N topology is given and that all 2-by-2 components can be queried and learned using end-to-end probes. The problem is which 2-by-2's to query and how to merge them with the 1-byN, so as to exactly identify the 2-by-N topology, and optimize a number of performance metrics including measurement traffic, time complexity, and memory usage. We provide a lower bound, ⌈N/2⌉, on the number of 2-by-2's required by any active learning algorithm and we also propose a greedy algorithm that is nearoptimal and efficient in practice. It follows a bottom-up approach: at every step, it selects two receivers, queries the corresponding 2-by-2, and merges it with the given 1-by-N. The algorithm requires exactly N - 1 steps, which is much less than all (N:2) possible 2-by-2's, and it correctly identifies the 2-by-N topology.
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Multiple source Multiple Destination network tomography
2004Co-Authors: Michael G Rabbat, Robert Nowak, Mark CoatesAbstract:The problem of identifying topology and inferring link-level performance parameters such as packet drop rate or delay variance using only end-to-end measurements is commonly referred to as network tomography. This paper describes a collaborative framework for performing network tomography on topologies with Multiple sources and Multiple Destinations, without assuming the topology to be known. Using Multiple sources potentially provides a more accurate and refined characterization of the internal network. We present a novel Multiple source active measurement procedure using a semirandomized probing scheme and packet arrival order measurements which do not require precise synchronization between the participating hosts. A decision-theoretic framework is developed enabling the joint characterization of topology and internal performance. We design a statistical test based on the generalized likelihood ratio test and Wilks' theorem. The test quantifies the tradeoff between network topology complexity and performance estimation, and identifies when measurements made by the two sources can be combined to achieve reduced variance performance estimates. The performance and efficacy of the algorithm are assessed through ns-2 simulations and experiments over the Internet
Athina Markopoulou - One of the best experts on this subject based on the ideXlab platform.
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active learning of Multiple source Multiple Destination topologies
2014Co-Authors: Pegah Sattari, Athina Markopoulou, Maciej Kurant, Animashree Anandkumar, Michael G RabbatAbstract:We consider the problem of inferring the topology of a network with M sources and N receivers (an M-by- N network), by sending probes between the sources and receivers. Prior work has shown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (1-by- N's or 2-by-2's) and then merge them to identify the M-by- N topology. We focus on the second part, which had previously received less attention in the literature. We assume that a 1-by- N topology is given and that all 2-by-2 components can be queried and learned using end-to-end probes. The problem is which 2-by-2's to query and how to merge them with the given 1-by- N, so as to exactly identify the 2-by- N topology, and optimize a number of performance metrics, including the number of queries (which directly translates into measurement bandwidth), time complexity, and memory usage. We provide a lower bound, [N/2], on the number of 2-by-2's required by any active learning algorithm and propose two greedy algorithms. The first algorithm follows the framework of Multiple hypothesis testing, in particular Generalized Binary Search (GBS). The second algorithm is called the Receiver Elimination Algorithm (REA) and follows a bottom-up approach. It requires exactly N-1 steps, which is much less than all (2N) possible 2-by-2's. Simulation results demonstrate that both algorithms correctly identify the 2-by- N topology and are near-optimal, but REA is more efficient in practice.
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active learning of Multiple source Multiple Destination topologies
2013Co-Authors: Pegah Sattari, Athina Markopoulou, Maciej Kurant, Animashree Anandkumar, Michael G RabbatAbstract:We consider the problem of inferring the topology of an M-by-N network by sending probes between M sources and N receivers. Prior work has shown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (i.e., 1-by-N's or 2-by-2's) and then merge these components to identify the M-by-N topology. In this paper, we focus on the second part. In particular, we assume that a 1by-N topology is given and that all 2-by-2 components can be queried and learned using end-to-end probes. The problem is which 2-by-2's to query and how to merge them with the 1-byN, so as to exactly identify the 2-by-N topology, and optimize a number of performance metrics including measurement traffic, time complexity, and memory usage. We provide a lower bound, ⌈N/2⌉, on the number of 2-by-2's required by any active learning algorithm and we also propose a greedy algorithm that is nearoptimal and efficient in practice. It follows a bottom-up approach: at every step, it selects two receivers, queries the corresponding 2-by-2, and merges it with the given 1-by-N. The algorithm requires exactly N - 1 steps, which is much less than all (N:2) possible 2-by-2's, and it correctly identifies the 2-by-N topology.
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Multiple source Multiple Destination topology inference using network coding
2009Co-Authors: Pegah Sattari, Athina Markopoulou, Christina FragouliAbstract:n this paper, we combine network cod- ing and tomographic techniques for topology infer- ence. Our goal is to infer the topology of a network by sending probes between a given set of Multiple sources and Multiple receivers and by having interme- diate nodes perform network coding operations. We combine and extend two ideas that have been devel- oped independently. On one hand, network coding introduces topology-dependent correlation, which can then be exploited at the receivers to infer the topology [1]. On the other hand, it has been shown that a tradi- tional (i.e., without network coding) Multiple source, Multiple receiver tomography problem can be decom- posed into Multiple two source, two receiver subprob- lems [2]. Our first contribution is to show that, when intermediate nodes perform network coding, topolog- ical information contained in network coded packets allows to accurately distinguish among all different 2- by-2 subnetwork components, which was not possible with traditional tomographic techniques. Our second contribution is to use this knowledge to merge the subnetworks and accurately reconstruct the general topology. Our approach is applicable to any general Internet-like topology, and is robust to the presence of delay variability and packet loss.
Mehdi Bennis - One of the best experts on this subject based on the ideXlab platform.
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Sum Secrecy Rate Maximization for Relay-Aided Multiple-Source Multiple-Destination Networks
2016Co-Authors: Meng Zhang, Gui Lin, Hanwen Luo, Ming Ding, Mehdi BennisAbstract:This paper studies a Multiple-source MultipleDestination (MSMD) network with the presence of Multiple eavesdroppers, in which an amplify-and-forward (AF) relay is utilized to bridge the communication between the source-Destination pairs to overcome the long-distance attenuation. Considering the physical-layer security issues, we aim to maximize the sum secrecy rate (SSR) subject to the relay power constraint and the quality-of-service (QoS) requirements for legitimate user equipments (L-UEs). First, we propose an algorithm based on the monotonic optimization and the semidefinite programming (MO-SDP). Simulation results show that our proposed MOSDP algorithm exhibits almost the same performance as the optimal solution. To alleviate the problem of high complexity associated with the MO-SDP algorithm, we then propose an alternative solution based on the null-space (NuS) relay precoding, the complexity of which is significantly reduced and it yields a semi-closed-form expression for the solution. Moreover, the performance of the proposed NuS algorithm is evaluated via simulations, and the performance of the NuS algorithm and that of the MO-SDP algorithm are shown to converge at high signalto- noise ratio (SNR) region.
Christina Fragouli - One of the best experts on this subject based on the ideXlab platform.
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Multiple source Multiple Destination topology inference using network coding
2009Co-Authors: Pegah Sattari, Athina Markopoulou, Christina FragouliAbstract:n this paper, we combine network cod- ing and tomographic techniques for topology infer- ence. Our goal is to infer the topology of a network by sending probes between a given set of Multiple sources and Multiple receivers and by having interme- diate nodes perform network coding operations. We combine and extend two ideas that have been devel- oped independently. On one hand, network coding introduces topology-dependent correlation, which can then be exploited at the receivers to infer the topology [1]. On the other hand, it has been shown that a tradi- tional (i.e., without network coding) Multiple source, Multiple receiver tomography problem can be decom- posed into Multiple two source, two receiver subprob- lems [2]. Our first contribution is to show that, when intermediate nodes perform network coding, topolog- ical information contained in network coded packets allows to accurately distinguish among all different 2- by-2 subnetwork components, which was not possible with traditional tomographic techniques. Our second contribution is to use this knowledge to merge the subnetworks and accurately reconstruct the general topology. Our approach is applicable to any general Internet-like topology, and is robust to the presence of delay variability and packet loss.