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Yu Liu

Bio: Yu Liu is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Spare part & Backup. The author has an hindex of 7, co-authored 8 publications receiving 605 citations.

Papers
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Journal ArticleDOI
TL;DR: Numerical results comparing several SCA algorithms show that SSR has the best trade-off between solution optimality and computation speed.
Abstract: The design of survivable mesh based communication networks has received considerable attention in recent years. One task is to route backup paths and allocate spare capacity in the network to guarantee seamless communications services survivable to a set of failure scenarios. This is a complex multi-constraint optimization problem, called the spare capacity allocation (SCA) problem. This paper unravels the SCA problem structure using a matrix-based model, and develops a fast and efficient approximation algorithm, termed successive survivable routing (SSR). First, per-flow spare capacity sharing is captured by a spare provision matrix (SPM) method. The SPM matrix has a dimension the number of failure scenarios by the number of links. It is used by each demand to route the backup path and share spare capacity with other backup paths. Next, based on a special link metric calculated from SPM, SSR iteratively routes/updates backup paths in order to minimize the cost of total spare capacity. A backup path can be further updated as long as it is not carrying any traffic. Furthermore, the SPM method and SSR algorithm are generalized from protecting all single link failures to any arbitrary link failures such as those generated by Shared Risk Link Groups or all single node failures. Numerical results comparing several SCA algorithms show that SSR has the best trade-off between solution optimality and computation speed.

237 citations

Proceedings ArticleDOI
22 Apr 2001
TL;DR: Numerical results comparing several SCA algorithms show that SSR has the best trade-off between solution optimality and computation speed.
Abstract: Spare capacity allocation (SCA) is an important part of a fault tolerant network design. In the spare capacity allocation problem one seeks to determine where to place spare capacity in the network and how much spare capacity must be allocated to guarantee seamless communications services survivable to a set of failure scenarios (e.g., any single link failure). Formulated as a multi-commodity flow integer programming problem, SCA is known to be NP-hard. We provide a two-pronged attack to approximate the optimal SCA solution: unravel the SCA structure and find an effective algorithm. First, a literature review on the SCA problem and its algorithms is provided. Second, a integer programming model for SCA is provided. Third, a simulated annealing algorithm using the above INP model is introduced. Next, the structure of SCA is modeled by a matrix method. The per-flow based backup path information are aggregated into a square matrix, called the spare provision matrix (SPM). The size of the SPM is the number of links. Using the SPM as the state information, a new adaptive algorithm is then developed to approximate the optimal SCA solution termed successive survivable routing (SSR). SSR routes link-disjoint backup paths for each traffic flow one at a time. Each flow keeps updating its backup path according to the current network state as long as the backup path is not carrying any traffic. In this way, SSR can be implemented by shortest path algorithms using advertised state information with complexity of O( Link/sup 2/). The analysis also shows that SSR is using a necessary condition of the optimal solution. The numerical results show that SSR has near optimal spare capacity allocation with substantial advantages in computation speed.

177 citations

Patent
10 Aug 2001
TL;DR: In this paper, a method for deriving a backup path routing spare capacity template that is feasible, scalable, adaptive, much faster, and near global optimal in redundancy reduction is given.
Abstract: A method is given for deriving a backup path routing spare capacity template that is feasible, scalable, adaptive, much faster, and near global optimal in redundancy reduction. The method includes determining working paths, aggregating the working paths into a first matrix, determining backup paths, aggregating the backup paths into a second matrix, and deriving the template from the first and second matrices. A method is also given for successively approximating the optimal spare capacity allocation needed for a network. The method includes, determining the link cost associated with a selected traffic flow's backup path, determining an updated link cost that is less than the current link cost, determining a backup path with the updated link cost, and notifying the rest of the network of the backup path.

88 citations

Proceedings ArticleDOI
25 Nov 2001
TL;DR: A novel matrix formulation of the arc-flow SCA node failure model, where working paths are given before pre-planned backup paths are routed and reserved, and successive survivable routing is extended to solve the above SCA model.
Abstract: This paper addresses the spare capacity allocation (SCA) problem considering any single node failure in mesh networks. The SCA node failure problem aims at finding backup routes and providing sufficient spare capacity to protect traffic when any single node fails in a communication network. Here, we introduce our novel matrix formulation of the arc-flow SCA node failure model. In this model, working paths are given before pre-planned backup paths are routed and reserved. Because backup paths can not be guaranteed if general shortest path routing of working paths is used, we give a graph algorithm to find the working path which has at least one node-disjoint backup path. We extend our recent approximation algorithm, successive survivable routing (SSR), to solve the above SCA model. Numerical comparison shows that SSR has the best trade-off between solution optimality and computation speed.

38 citations


Cited by
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Journal Article
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

Journal ArticleDOI
TL;DR: It is proved that the problem of finding an eligible pair of working and backup paths for a new lightpath request requiring shared-path protection under the current network state is NP-complete and a heuristic is developed to compute a feasible solution with high probability.
Abstract: This paper investigates the problem of dynamic survivable lightpath provisioning in optical mesh networks employing wavelength-division multiplexing (WDM). In particular, we focus on shared-path protection because it is resource efficient due to the fact that backup paths can share wavelength links when their corresponding working paths are mutually diverse. Our main contributions are as follows. 1) First, we prove that the problem of finding an eligible pair of working and backup paths for a new lightpath request requiring shared-path protection under the current network state is NP-complete. 2) Then, we develop a heuristic, called CAFES, to compute a feasible solution with high probability. 3) Finally, we design another heuristic, called OPT, to optimize resource consumption for a given solution. The merits of our approaches are that they capture the essence of shared-path protection and approach to optimal solutions without enumerating paths. We evaluate the effectiveness of our heuristics and the results are found to be promising.

247 citations

Journal ArticleDOI
TL;DR: Numerical results comparing several SCA algorithms show that SSR has the best trade-off between solution optimality and computation speed.
Abstract: The design of survivable mesh based communication networks has received considerable attention in recent years. One task is to route backup paths and allocate spare capacity in the network to guarantee seamless communications services survivable to a set of failure scenarios. This is a complex multi-constraint optimization problem, called the spare capacity allocation (SCA) problem. This paper unravels the SCA problem structure using a matrix-based model, and develops a fast and efficient approximation algorithm, termed successive survivable routing (SSR). First, per-flow spare capacity sharing is captured by a spare provision matrix (SPM) method. The SPM matrix has a dimension the number of failure scenarios by the number of links. It is used by each demand to route the backup path and share spare capacity with other backup paths. Next, based on a special link metric calculated from SPM, SSR iteratively routes/updates backup paths in order to minimize the cost of total spare capacity. A backup path can be further updated as long as it is not carrying any traffic. Furthermore, the SPM method and SSR algorithm are generalized from protecting all single link failures to any arbitrary link failures such as those generated by Shared Risk Link Groups or all single node failures. Numerical results comparing several SCA algorithms show that SSR has the best trade-off between solution optimality and computation speed.

237 citations

Proceedings ArticleDOI
07 Nov 2002
TL;DR: This work proposes an efficient path-selection algorithm for restoration of connections over shared bandwidth in a fully distributedGMPLS architecture and describes how to extend GMPLS signaling protocols to collect the necessary information efficiently.
Abstract: In MPLS/GMPLS networks, a range of restoration schemes are required to support different tradeoffs between service interruption time and network resource utilization. In light of these tradeoffs, path-based, end-to-end shared restoration provides a very attractive solution. However, efficient use of capacity for shared restoration strongly relies on the selection procedure of restoration paths. We propose an efficient path-selection algorithm for restoration of connections over shared bandwidth in a fully distributed GMPLS architecture. We also describe how to extend GMPLS signaling protocols to collect the necessary information efficiently. To evaluate the algorithm's performance, we compare it via simulation with two other well-known algorithm on a typical intercity backbone network. The key figure-of-merit for restoration capacity efficiency is restoration overbuild, i.e., the extra capacity required to meet the network restoration objective as a percentage of the capacity of the network with no restoration. Our simulation results show that our algorithm uses significantly less restoration overbuild (63-68%) compared to the other two algorithms (83-90%).

184 citations

Proceedings ArticleDOI
22 Apr 2001
TL;DR: Numerical results comparing several SCA algorithms show that SSR has the best trade-off between solution optimality and computation speed.
Abstract: Spare capacity allocation (SCA) is an important part of a fault tolerant network design. In the spare capacity allocation problem one seeks to determine where to place spare capacity in the network and how much spare capacity must be allocated to guarantee seamless communications services survivable to a set of failure scenarios (e.g., any single link failure). Formulated as a multi-commodity flow integer programming problem, SCA is known to be NP-hard. We provide a two-pronged attack to approximate the optimal SCA solution: unravel the SCA structure and find an effective algorithm. First, a literature review on the SCA problem and its algorithms is provided. Second, a integer programming model for SCA is provided. Third, a simulated annealing algorithm using the above INP model is introduced. Next, the structure of SCA is modeled by a matrix method. The per-flow based backup path information are aggregated into a square matrix, called the spare provision matrix (SPM). The size of the SPM is the number of links. Using the SPM as the state information, a new adaptive algorithm is then developed to approximate the optimal SCA solution termed successive survivable routing (SSR). SSR routes link-disjoint backup paths for each traffic flow one at a time. Each flow keeps updating its backup path according to the current network state as long as the backup path is not carrying any traffic. In this way, SSR can be implemented by shortest path algorithms using advertised state information with complexity of O( Link/sup 2/). The analysis also shows that SSR is using a necessary condition of the optimal solution. The numerical results show that SSR has near optimal spare capacity allocation with substantial advantages in computation speed.

177 citations