The Experts below are selected from a list of 45759 Experts worldwide ranked by ideXlab platform
Ian Goldberg - One of the best experts on this subject based on the ideXlab platform.
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congestion aware Path Selection for tor
Financial Cryptography, 2012Co-Authors: Tao Wang, Kevin Bauer, Clara Forero, Ian GoldbergAbstract:Tor, an anonymity network formed by volunteer nodes, uses the estimated bandwidth of the nodes as a central feature of its Path Selection algorithm. The current load on nodes is not considered in this algorithm, however, and we observe that some nodes persist in being under-utilized or congested. This can degrade the network’s performance, discourage Tor adoption, and consequently reduce the size of Tor’s anonymity set. In an effort to reduce congestion and improve load balancing, we propose a congestion-aware Path Selection algorithm. Using latency as an indicator of congestion, clients use opportunistic and lightweight active measurements to evaluate the congestion state of nodes, and reject nodes that appear congested. Through experiments conducted on the live Tor network, we verify our hypothesis that clients can infer congestion using latency and show that congestion-aware Path Selection can improve performance.
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Financial Cryptography - Congestion-Aware Path Selection for Tor
Financial Cryptography and Data Security, 2012Co-Authors: Tao Wang, Kevin Bauer, Clara Forero, Ian GoldbergAbstract:Tor, an anonymity network formed by volunteer nodes, uses the estimated bandwidth of the nodes as a central feature of its Path Selection algorithm. The current load on nodes is not considered in this algorithm, however, and we observe that some nodes persist in being under-utilized or congested. This can degrade the network’s performance, discourage Tor adoption, and consequently reduce the size of Tor’s anonymity set. In an effort to reduce congestion and improve load balancing, we propose a congestion-aware Path Selection algorithm. Using latency as an indicator of congestion, clients use opportunistic and lightweight active measurements to evaluate the congestion state of nodes, and reject nodes that appear congested. Through experiments conducted on the live Tor network, we verify our hypothesis that clients can infer congestion using latency and show that congestion-aware Path Selection can improve performance.
Jacob A Abraham - One of the best experts on this subject based on the ideXlab platform.
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Testability-Driven Statistical Path Selection
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2012Co-Authors: Jaeyong Chung, Jinjun Xiong, Vladimir Zolotov, Jacob A AbrahamAbstract:In the face of large-scale process variations, statistical timing methodology has advanced significantly over the last few years, and statistical Path Selection takes advantage of it in at-speed testing. In deterministic Path Selection, the separation of Path Selection and test generation is known to require time consuming iteration between the two processes. This paper shows that in statistical Path Selection, this is not only the case, but also the quality of results can be severely degraded even after the iteration. To deal with this issue, we consider testability in the first place by integrating a satisfiability (SAT) solver, and this necessitates a new statistical Path Selection method. We integrate the SAT solver in a novel way that leverages the conflict analysis of modern SAT solvers, which provides more than 4X speedup without special optimizations of the SAT solver for this particular application. Our proposed method is based on a generalized Path criticality metric whose properties allow efficient pruning. Our experimental results show that the proposed method achieves 47% better quality of results on average, and up to 361X speedup compared to statistical Path Selection followed by test generation.
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testability driven statistical Path Selection
Design Automation Conference, 2011Co-Authors: Jaeyong Chung, Jinjun Xiong, Vladimir Zolotov, Jacob A AbrahamAbstract:In the face of large-scale process variations, statistical timing methodology has advanced significantly over the last few years, and statistical Path Selection takes advantage of it in at-speed testing. In deterministic Path Selection, the separation of Path Selection and test generation is known to require time consuming iteration between the two processes. This paper shows that in statistical Path Selection, this is not only the case, but also the quality of results can be severely degraded even after the iteration. To deal with this issue, we consider testability in the first place by integrating a SAT solver, and this necessitates a new statistical Path Selection method. Our proposed method is based on a generalized Path criticality metric which properties allow efficient pruning. Our experimental results show that the proposed method achieves 47% better quality of results on average, and up to 361x speedup compared to statistical Path Selection followed by test generation.
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DAC - Testability driven statistical Path Selection
Proceedings of the 48th Design Automation Conference on - DAC '11, 2011Co-Authors: Jaeyong Chung, Jinjun Xiong, Vladimir Zolotov, Jacob A AbrahamAbstract:In the face of large-scale process variations, statistical timing methodology has advanced significantly over the last few years, and statistical Path Selection takes advantage of it in at-speed testing. In deterministic Path Selection, the separation of Path Selection and test generation is known to require time consuming iteration between the two processes. This paper shows that in statistical Path Selection, this is not only the case, but also the quality of results can be severely degraded even after the iteration. To deal with this issue, we consider testability in the first place by integrating a SAT solver, and this necessitates a new statistical Path Selection method. Our proposed method is based on a generalized Path criticality metric which properties allow efficient pruning. Our experimental results show that the proposed method achieves 47% better quality of results on average, and up to 361x speedup compared to statistical Path Selection followed by test generation.
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Critical Path Selection for Delay Testing Considering Coupling Noise
Journal of Electronic Testing, 2009Co-Authors: Rajeshwary Tayade, Jacob A AbrahamAbstract:The objective of delay testing is to detect any defects or variations that manifest into timing failures. In Path based delay testing this is done by testing a subset of Paths in the circuit that are more likely to fail and hence are critical. Since Path delays are vector dependent, the set of critical Paths selected depends on the vectors assumed when estimating the Path delays. This implies that to find the real critical Paths, it is important to consider the effect of dynamic (vector dependent) delay effects such as coupling noise and supply noise during Path Selection. In this work a methodology to incorporate the effect of coupling noise during Path Selection is described. For any given Path, both logic and timing constraints are extracted and a constrained optimization problem is formulated to estimate the maximum Path delay in the presence of coupling noise.
Vladimir Zolotov - One of the best experts on this subject based on the ideXlab platform.
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Testability-Driven Statistical Path Selection
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2012Co-Authors: Jaeyong Chung, Jinjun Xiong, Vladimir Zolotov, Jacob A AbrahamAbstract:In the face of large-scale process variations, statistical timing methodology has advanced significantly over the last few years, and statistical Path Selection takes advantage of it in at-speed testing. In deterministic Path Selection, the separation of Path Selection and test generation is known to require time consuming iteration between the two processes. This paper shows that in statistical Path Selection, this is not only the case, but also the quality of results can be severely degraded even after the iteration. To deal with this issue, we consider testability in the first place by integrating a satisfiability (SAT) solver, and this necessitates a new statistical Path Selection method. We integrate the SAT solver in a novel way that leverages the conflict analysis of modern SAT solvers, which provides more than 4X speedup without special optimizations of the SAT solver for this particular application. Our proposed method is based on a generalized Path criticality metric whose properties allow efficient pruning. Our experimental results show that the proposed method achieves 47% better quality of results on average, and up to 361X speedup compared to statistical Path Selection followed by test generation.
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testability driven statistical Path Selection
Design Automation Conference, 2011Co-Authors: Jaeyong Chung, Jinjun Xiong, Vladimir Zolotov, Jacob A AbrahamAbstract:In the face of large-scale process variations, statistical timing methodology has advanced significantly over the last few years, and statistical Path Selection takes advantage of it in at-speed testing. In deterministic Path Selection, the separation of Path Selection and test generation is known to require time consuming iteration between the two processes. This paper shows that in statistical Path Selection, this is not only the case, but also the quality of results can be severely degraded even after the iteration. To deal with this issue, we consider testability in the first place by integrating a SAT solver, and this necessitates a new statistical Path Selection method. Our proposed method is based on a generalized Path criticality metric which properties allow efficient pruning. Our experimental results show that the proposed method achieves 47% better quality of results on average, and up to 361x speedup compared to statistical Path Selection followed by test generation.
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DAC - Testability driven statistical Path Selection
Proceedings of the 48th Design Automation Conference on - DAC '11, 2011Co-Authors: Jaeyong Chung, Jinjun Xiong, Vladimir Zolotov, Jacob A AbrahamAbstract:In the face of large-scale process variations, statistical timing methodology has advanced significantly over the last few years, and statistical Path Selection takes advantage of it in at-speed testing. In deterministic Path Selection, the separation of Path Selection and test generation is known to require time consuming iteration between the two processes. This paper shows that in statistical Path Selection, this is not only the case, but also the quality of results can be severely degraded even after the iteration. To deal with this issue, we consider testability in the first place by integrating a SAT solver, and this necessitates a new statistical Path Selection method. Our proposed method is based on a generalized Path criticality metric which properties allow efficient pruning. Our experimental results show that the proposed method achieves 47% better quality of results on average, and up to 361x speedup compared to statistical Path Selection followed by test generation.
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Statistical Path Selection for At-Speed Test
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2010Co-Authors: Vladimir Zolotov, Jinjun Xiong, Hanif Fatemi, Chandu VisweswariahAbstract:Process variations make at-speed testing significantly more difficult. They cause subtle delay changes that are distributed in contrast to the localized nature of a traditional fault model. Due to parametric variations, different Paths can be critical in different parts of the process space, and the union of such Paths must be tested to obtain good process space coverage. This paper proposes an integrated at-speed structural testing methodology, and develops a novel branch-and-bound algorithm that elegantly and efficiently solves the hitherto open problem of statistical Path tracing. The resulting Paths are used for at-speed structural testing. A new test quality metric is proposed, and Paths which maximize this metric are selected. After chip timing has been performed, the Path Selection procedure is extremely efficient. Path Selection for a multimillion gate chip design can be completed in a matter of seconds.
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statistical Path Selection for at speed test
International Conference on Computer Aided Design, 2008Co-Authors: Vladimir Zolotov, Jinjun Xiong, Hanif Fatemi, Chandu VisweswariahAbstract:Process variations make at-speed testing significantly more difficult. They cause subtle delay changes that are distributed rather than the localized nature of a traditional fault model. Due to parametric variations, different Paths can be critical in different parts of the process space, and the union of such Paths must be tested to obtain good process space coverage. This paper proposes a novel branch-and-bound algorithm that elegantly and efficiently solves the hitherto open problem of statistical Path tracing. The resulting Paths are used for at-speed structural testing. A new Test Quality Metric (TQM) is proposed and Paths which maximize this metric are selected. After chip timing has been performed, the Path Selection procedure is extremely efficient. Path Selection for a multi-million gate chip design can be completed in a matter of seconds.
Jinjun Xiong - One of the best experts on this subject based on the ideXlab platform.
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Testability-Driven Statistical Path Selection
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2012Co-Authors: Jaeyong Chung, Jinjun Xiong, Vladimir Zolotov, Jacob A AbrahamAbstract:In the face of large-scale process variations, statistical timing methodology has advanced significantly over the last few years, and statistical Path Selection takes advantage of it in at-speed testing. In deterministic Path Selection, the separation of Path Selection and test generation is known to require time consuming iteration between the two processes. This paper shows that in statistical Path Selection, this is not only the case, but also the quality of results can be severely degraded even after the iteration. To deal with this issue, we consider testability in the first place by integrating a satisfiability (SAT) solver, and this necessitates a new statistical Path Selection method. We integrate the SAT solver in a novel way that leverages the conflict analysis of modern SAT solvers, which provides more than 4X speedup without special optimizations of the SAT solver for this particular application. Our proposed method is based on a generalized Path criticality metric whose properties allow efficient pruning. Our experimental results show that the proposed method achieves 47% better quality of results on average, and up to 361X speedup compared to statistical Path Selection followed by test generation.
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testability driven statistical Path Selection
Design Automation Conference, 2011Co-Authors: Jaeyong Chung, Jinjun Xiong, Vladimir Zolotov, Jacob A AbrahamAbstract:In the face of large-scale process variations, statistical timing methodology has advanced significantly over the last few years, and statistical Path Selection takes advantage of it in at-speed testing. In deterministic Path Selection, the separation of Path Selection and test generation is known to require time consuming iteration between the two processes. This paper shows that in statistical Path Selection, this is not only the case, but also the quality of results can be severely degraded even after the iteration. To deal with this issue, we consider testability in the first place by integrating a SAT solver, and this necessitates a new statistical Path Selection method. Our proposed method is based on a generalized Path criticality metric which properties allow efficient pruning. Our experimental results show that the proposed method achieves 47% better quality of results on average, and up to 361x speedup compared to statistical Path Selection followed by test generation.
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DAC - Testability driven statistical Path Selection
Proceedings of the 48th Design Automation Conference on - DAC '11, 2011Co-Authors: Jaeyong Chung, Jinjun Xiong, Vladimir Zolotov, Jacob A AbrahamAbstract:In the face of large-scale process variations, statistical timing methodology has advanced significantly over the last few years, and statistical Path Selection takes advantage of it in at-speed testing. In deterministic Path Selection, the separation of Path Selection and test generation is known to require time consuming iteration between the two processes. This paper shows that in statistical Path Selection, this is not only the case, but also the quality of results can be severely degraded even after the iteration. To deal with this issue, we consider testability in the first place by integrating a SAT solver, and this necessitates a new statistical Path Selection method. Our proposed method is based on a generalized Path criticality metric which properties allow efficient pruning. Our experimental results show that the proposed method achieves 47% better quality of results on average, and up to 361x speedup compared to statistical Path Selection followed by test generation.
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Statistical Path Selection for At-Speed Test
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2010Co-Authors: Vladimir Zolotov, Jinjun Xiong, Hanif Fatemi, Chandu VisweswariahAbstract:Process variations make at-speed testing significantly more difficult. They cause subtle delay changes that are distributed in contrast to the localized nature of a traditional fault model. Due to parametric variations, different Paths can be critical in different parts of the process space, and the union of such Paths must be tested to obtain good process space coverage. This paper proposes an integrated at-speed structural testing methodology, and develops a novel branch-and-bound algorithm that elegantly and efficiently solves the hitherto open problem of statistical Path tracing. The resulting Paths are used for at-speed structural testing. A new test quality metric is proposed, and Paths which maximize this metric are selected. After chip timing has been performed, the Path Selection procedure is extremely efficient. Path Selection for a multimillion gate chip design can be completed in a matter of seconds.
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statistical Path Selection for at speed test
International Conference on Computer Aided Design, 2008Co-Authors: Vladimir Zolotov, Jinjun Xiong, Hanif Fatemi, Chandu VisweswariahAbstract:Process variations make at-speed testing significantly more difficult. They cause subtle delay changes that are distributed rather than the localized nature of a traditional fault model. Due to parametric variations, different Paths can be critical in different parts of the process space, and the union of such Paths must be tested to obtain good process space coverage. This paper proposes a novel branch-and-bound algorithm that elegantly and efficiently solves the hitherto open problem of statistical Path tracing. The resulting Paths are used for at-speed structural testing. A new Test Quality Metric (TQM) is proposed and Paths which maximize this metric are selected. After chip timing has been performed, the Path Selection procedure is extremely efficient. Path Selection for a multi-million gate chip design can be completed in a matter of seconds.
Paul Syverson - One of the best experts on this subject based on the ideXlab platform.
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As-awareness in Tor Path Selection
Proceedings of the 16th ACM conference on Computer and communications security - CCS '09, 2009Co-Authors: Moa Edman, Paul SyversonAbstract:Tor is an anonymous communications network with thousands of router nodes worldwide. An intuition reflected in much of the literature on anonymous communications is that, as an anonymity network grows, it becomes more secure against a given observer because the observer will see less of the network. In particular, as the Tor network grows from volunteers operating relays all over the world, it becomes less and less likely for a single autonomous system (AS) to be able to observe both ends of an anonymous connection. Yet, as the network continues to grow significantly, no analysis has been done to determine if this intuition is correct. Further, modifications to Tor’s Path Selection algorithm to help clients avoid an AS-level observer have not been proposed and analyzed. Five years ago a previous study examined the AS-level threat against client and destination addresses chosen a priori to be likely or interesting to examine. Using an AS- level Path inference algorithm with improved accuracy, more extensive Internet routing data, and, most importantly, a model of typical Tor client AS-level sources and destinations based on data gathered from the live network, we demonstrate that the threat of a single AS observing both ends of an anonymous Tor connection is greater than previously thought. We look at the growth of the Tor network over the past five years and show that its explosive growth has had only a small impact on the network’s robustness against an AS-level attacker. Finally, we propose and evaluate the effectiveness of some simple, AS-aware Path Selection algorithms that avoid the computational overhead imposed by full AS-level Path inference algorithms. Our results indicate that a novel heuristic we propose is more effective against an AS-level observer than other commonly proposed heuristics for improving location diversity in Path Selection.
