The Experts below are selected from a list of 121839 Experts worldwide ranked by ideXlab platform
Michele Cirinei - One of the best experts on this subject based on the ideXlab platform.
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brute force determination of multiprocessor schedulability for sets of sporadic hard deadline tasks
International Conference on Principles of Distributed Systems, 2007Co-Authors: T.p. Baker, Michele CirineiAbstract:This report describes a necessary and sufficient test for the schedulability of a set of sporadic hard-deadline tasks on a multiprocessor platform, using any of a variety of scheduling policies including global fixed task-priority and earliest-deadline-first (EDF). The contribution is to establish an upper bound on the computational complexity of this problem, for which no algorithm has yet been described. The compute time and storage complexity of the algorithm, which performs an exhaustive search of a very Large State Space, make it practical only for tasks sets with very small integer periods. However, as a research tool, it can provide a clearer picture than has been previously available of the real success rates of global preemptive priority scheduling policies and low-complexity sufficient tests of schedulability.
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OPODIS - Brute-force determination of multiprocessor schedulability for sets of sporadic hard-deadline tasks
Lecture Notes in Computer Science, 2007Co-Authors: T.p. Baker, Michele CirineiAbstract:This report describes a necessary and sufficient test for the schedulability of a set of sporadic hard-deadline tasks on a multiprocessor platform, using any of a variety of scheduling policies including global fixed task-priority and earliest-deadline-first (EDF). The contribution is to establish an upper bound on the computational complexity of this problem, for which no algorithm has yet been described. The compute time and storage complexity of the algorithm, which performs an exhaustive search of a very Large State Space, make it practical only for tasks sets with very small integer periods. However, as a research tool, it can provide a clearer picture than has been previously available of the real success rates of global preemptive priority scheduling policies and low-complexity sufficient tests of schedulability.
Urbashi Mitra - One of the best experts on this subject based on the ideXlab platform.
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On Solving MDPs With Large State Space: Exploitation of Policy Structures and Spectral Properties
IEEE Transactions on Communications, 2019Co-Authors: Libin Liu, Arpan Chattopadhyay, Urbashi MitraAbstract:In this paper, a point-to-point network transmission control problem is formulated as a Markov decision process (MDP). Classical dynamic programming techniques such as value iteration, policy iteration, and linear programming can be employed to solve the optimization problem, but they suffer from high-computational complexity in networks with Large State Space. To achieve complexity reduction, the structure of the optimal policy can be exploited and incorporated into standard algorithms. In addition, function approximation can also be applied, where the value function is approximated by the linear combination of some basis vectors in a lower dimensional subSpace. The main challenge for function approximation lies in the absence of general guidelines for subSpace construction. In this paper, a proper subSpace for projection is first generated based on system information, and more general construction methods are proposed using tools from graph signal processing (GSP). Graph symmetrization methods are also used to tackle the directed nature of the probability transition graph so that the well-developed GSP theory for undirected graphs can be employed. The numerical results for a typical wireless system show that standard algorithms with structural information incorporated can achieve 50% complexity reduction without performance loss. The subSpace generated from the system can achieve zero policy error with faster runtime, and the GSP approach can also provide a proper subSpace for perfect reconstruction of the optimal policy. It is also shown that how the proposed method can be applied to other MDP problems.
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exploiting policy structure for solving mdps with Large State Space
Conference on Information Sciences and Systems, 2018Co-Authors: Libin Liu, Arpan Chattopadhyay, Urbashi MitraAbstract:Markov decision processes provide good models for many systems, including wireless communication networks. The goal herein is to develop optimal control policies for wireless networks. While classical methods such as value iteration and policy iteration have been employed to determine optimal policies in a moderate complexity manner; they still suffer from complexity challenges for very Large scale networks. Previously, subSpace approximation has been employed to find optimal controllers in reduced dimensions. Herein, an alternative approach is considered wherein the properties of the policy structure are exploited to determine solutions in a reduced dimension. The numerical results show that this new approach achieves a faster convergence rate with a negligible loss of performance.
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CISS - Exploiting policy structure for solving MDPs with Large State Space
2018 52nd Annual Conference on Information Sciences and Systems (CISS), 2018Co-Authors: Libin Liu, Arpan Chattopadhyay, Urbashi MitraAbstract:Markov decision processes provide good models for many systems, including wireless communication networks. The goal herein is to develop optimal control policies for wireless networks. While classical methods such as value iteration and policy iteration have been employed to determine optimal policies in a moderate complexity manner; they still suffer from complexity challenges for very Large scale networks. Previously, subSpace approximation has been employed to find optimal controllers in reduced dimensions. Herein, an alternative approach is considered wherein the properties of the policy structure are exploited to determine solutions in a reduced dimension. The numerical results show that this new approach achieves a faster convergence rate with a negligible loss of performance.
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on exploiting spectral properties for solving mdp with Large State Space
Allerton Conference on Communication Control and Computing, 2017Co-Authors: Libin Liu, Arpan Chattopadhyay, Urbashi MitraAbstract:A Large number of systems are well-modeled by Markov Decision Processes (MDPs). In particular, certain wireless communication networks and biological networks admit such models. Herein, moderate complexity strategies are proposed for computing the optimal policy for a Large State Space with long run discounted cost MDP, by exploiting spectral properties of the probability transition matrices (PTM). Methods such as value iteration and policy iteration for such problems are computationally prohibitive for Large State Spaces. Reduced dimensional policy iteration can be achieved by projecting the value function on a proper subSpace. However there is no clear method for determining the optimal subSpace. To this end, Graph signal processing methods have the potential to provide a solution. In order to use spectral techniques, an appropriate positive semi-definite (PSD) matrix is generated from the PTM and the single stage cost vector. Low complexity computation of the value function is enabled by the bases of this dominant subSpace. The proposed projections are combined with policy iteration to find the optimal policy. Finally, numerical results on a wireless system are provided to highlight the performances and trade-offs of these various algorithms, and we found that direct spectral decomposition of outer product of PTM gives us best performance in general.
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Allerton - On exploiting spectral properties for solving MDP with Large State Space
2017 55th Annual Allerton Conference on Communication Control and Computing (Allerton), 2017Co-Authors: Libin Liu, Arpan Chattopadhyay, Urbashi MitraAbstract:A Large number of systems are well-modeled by Markov Decision Processes (MDPs). In particular, certain wireless communication networks and biological networks admit such models. Herein, moderate complexity strategies are proposed for computing the optimal policy for a Large State Space with long run discounted cost MDP, by exploiting spectral properties of the probability transition matrices (PTM). Methods such as value iteration and policy iteration for such problems are computationally prohibitive for Large State Spaces. Reduced dimensional policy iteration can be achieved by projecting the value function on a proper subSpace. However there is no clear method for determining the optimal subSpace. To this end, Graph signal processing methods have the potential to provide a solution. In order to use spectral techniques, an appropriate positive semi-definite (PSD) matrix is generated from the PTM and the single stage cost vector. Low complexity computation of the value function is enabled by the bases of this dominant subSpace. The proposed projections are combined with policy iteration to find the optimal policy. Finally, numerical results on a wireless system are provided to highlight the performances and trade-offs of these various algorithms, and we found that direct spectral decomposition of outer product of PTM gives us best performance in general.
Sandeep S Kulkarni - One of the best experts on this subject based on the ideXlab platform.
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exploiting symbolic techniques in automated synthesis of distributed programs with Large State Space
International Conference on Distributed Computing Systems, 2007Co-Authors: Borzoo Bonakdarpour, Sandeep S KulkarniAbstract:Automated formal analysis methods such as program verification and synthesis algorithms often suffer from time complexity of their decision procedures and also high Space complexity known as the State explosion problem. Symbolic techniques, in which elements of a problem are represented by Boolean formulae, are desirable in the sense that they often remedy the State explosion problem and time complexity of decision procedures. Although symbolic techniques have successfully been used in program verification, their benefits have not yet been exploited in the context of program synthesis and transformation extensively. In this paper, we present a symbolic method for automatic synthesis of fault-tolerant distributed programs. Our experimental results on synthesis of classical fault-tolerant distributed problems such as Byzantine agreement and token ring show a significant performance improvement by several orders of magnitude in both time and Space complexity. To the best of our knowledge, this is the first illustration where programs with Large State Space (beyond 2100) is handled during synthesis.
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ICDCS - Exploiting Symbolic Techniques in Automated Synthesis of Distributed Programs with Large State Space
27th International Conference on Distributed Computing Systems (ICDCS '07), 2007Co-Authors: Borzoo Bonakdarpour, Sandeep S KulkarniAbstract:Automated formal analysis methods such as program verification and synthesis algorithms often suffer from time complexity of their decision procedures and also high Space complexity known as the State explosion problem. Symbolic techniques, in which elements of a problem are represented by Boolean formulae, are desirable in the sense that they often remedy the State explosion problem and time complexity of decision procedures. Although symbolic techniques have successfully been used in program verification, their benefits have not yet been exploited in the context of program synthesis and transformation extensively. In this paper, we present a symbolic method for automatic synthesis of fault-tolerant distributed programs. Our experimental results on synthesis of classical fault-tolerant distributed problems such as Byzantine agreement and token ring show a significant performance improvement by several orders of magnitude in both time and Space complexity. To the best of our knowledge, this is the first illustration where programs with Large State Space (beyond 2100) is handled during synthesis.
Frank Van Ham - One of the best experts on this subject based on the ideXlab platform.
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Large State Space visualization
Tools and Algorithms for Construction and Analysis of Systems, 2003Co-Authors: Jan Friso Groote, Frank Van HamAbstract:Insight in the global structure of a State Space is of great help in the analysis of the underlying process. We present a tool to visualize the structure of very Large State Spaces. It uses a clustering method to obtain a simplified representation, which is used as a backbone for the display of the entire State Space. With this tool we are able to answer questions about the global structure of a State Space that cannot easily be answered by conventional methods. We show this by presenting a number of visualizations of real-world protocols.
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TACAS - Large State Space visualization
Tools and Algorithms for the Construction and Analysis of Systems, 2003Co-Authors: Jan Friso Groote, Frank Van HamAbstract:Insight in the global structure of a State Space is of great help in the analysis of the underlying process. We present a tool to visualize the structure of very Large State Spaces. It uses a clustering method to obtain a simplified representation, which is used as a backbone for the display of the entire State Space. With this tool we are able to answer questions about the global structure of a State Space that cannot easily be answered by conventional methods. We show this by presenting a number of visualizations of real-world protocols.
T.p. Baker - One of the best experts on this subject based on the ideXlab platform.
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brute force determination of multiprocessor schedulability for sets of sporadic hard deadline tasks
International Conference on Principles of Distributed Systems, 2007Co-Authors: T.p. Baker, Michele CirineiAbstract:This report describes a necessary and sufficient test for the schedulability of a set of sporadic hard-deadline tasks on a multiprocessor platform, using any of a variety of scheduling policies including global fixed task-priority and earliest-deadline-first (EDF). The contribution is to establish an upper bound on the computational complexity of this problem, for which no algorithm has yet been described. The compute time and storage complexity of the algorithm, which performs an exhaustive search of a very Large State Space, make it practical only for tasks sets with very small integer periods. However, as a research tool, it can provide a clearer picture than has been previously available of the real success rates of global preemptive priority scheduling policies and low-complexity sufficient tests of schedulability.
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OPODIS - Brute-force determination of multiprocessor schedulability for sets of sporadic hard-deadline tasks
Lecture Notes in Computer Science, 2007Co-Authors: T.p. Baker, Michele CirineiAbstract:This report describes a necessary and sufficient test for the schedulability of a set of sporadic hard-deadline tasks on a multiprocessor platform, using any of a variety of scheduling policies including global fixed task-priority and earliest-deadline-first (EDF). The contribution is to establish an upper bound on the computational complexity of this problem, for which no algorithm has yet been described. The compute time and storage complexity of the algorithm, which performs an exhaustive search of a very Large State Space, make it practical only for tasks sets with very small integer periods. However, as a research tool, it can provide a clearer picture than has been previously available of the real success rates of global preemptive priority scheduling policies and low-complexity sufficient tests of schedulability.
