The Experts below are selected from a list of 7902 Experts worldwide ranked by ideXlab platform
Christos G Cassandras - One of the best experts on this subject based on the ideXlab platform.
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A Solution to the Optimal Lot-Sizing Problem as a Stochastic Resource Contention Game
IEEE Transactions on Automation Science and Engineering, 2012Co-Authors: Chen Yao, Christos G CassandrasAbstract:We present a new way to solve the “lot-sizing” problem viewed as a stochastic noncooperative Resource Contention game. We develop a Stochastic Flow Model (SFM) for polling systems with non-negligible changeover times enabling us to formulate lot sizing as an optimization problem without imposing constraints on the distributional characteristics of the random processes in the system. Using Infinitesimal Perturbation Analysis (IPA) methods, we derive gradient estimators of the performance metrics of interests with respect to the lot-size parameters and prove they are unbiased. We then derive an online gradient-based algorithm for obtaining optimal lot sizes from both a system-centric and user-centric perspective. Uncharacteristically for such cases, there is no gap between the two solutions in the two-class case for which we have obtained explicit numerical results. We derive a proof of this phenomenon for a deterministic version of the problem, suggesting that lot-sizing-like scheduling policies in Resource Contention problems have a natural property of balancing certain user-centric and system-centric performance metrics.
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perturbation analysis of stochastic hybrid systems and applications to Resource Contention games
Frontiers of Electrical and Electronic Engineering in China, 2011Co-Authors: Chen Yao, Christos G CassandrasAbstract:We provide an overview of the recently developed general infinitesimal perturbation analysis (IPA) framework for stochastic hybrid systems (SHSs), and establish some conditions under which this framework can be used to obtain unbiased performance gradient estimates in a particularly simple and efficient manner. We also propose a general scheme for systematically deriving an abstraction of a discrete event system (DES) in the form of an SHS. Then, as an application of the general IPA framework, we study a class of stochastic non-cooperative games termed “Resource Contention games” modeled through stochastic flow models (SFMs), where two or more players (users) compete for the use of a sharable Resource. Simulation results are provided for a simple version of such games to illustrate and contrast system-centric and user-centric optimization.
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Resource Contention games in multiclass stochastic flow models
Nonlinear Analysis: Hybrid Systems, 2011Co-Authors: Chen Yao, Christos G CassandrasAbstract:Abstract We consider Resource Contention games in a stochastic hybrid system setting using Stochastic Flow Models (SFM) with multiple classes and class-dependent objectives. We present a general modeling framework for such games, where Infinitesimal Perturbation Analysis (IPA) estimators are derived for the derivatives of various class-dependent objectives. This allows us to study these games from the point of view of system-centric optimization of a performance metric and compare it to the user-centric approach where each user optimizes its own performance metric. We derive explicit solutions for a specific model in which the competing user classes employ threshold control policies and service is provided on a First Come First Serve (FCFS) basis. The unbiasedness of the IPA estimators is established in this case and it is shown that under certain conditions the system-centric and user-centric optimization solutions coincide.
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a solution of the lot sizing problem as a stochastic Resource Contention game
Conference on Decision and Control, 2010Co-Authors: Chen Yao, Christos G CassandrasAbstract:We present a new way to solve the “lot sizing” problem viewed as a stochastic non-cooperative Resource Contention game. We develop a Stochastic Flow Model (SFM) for polling systems with non-negligible changeover times enabling us to formulate lot sizing as an optimization problem with no constraints on the distributional characteristics of the random processes in the system. We then use Infinitesimal Perturbation Analysis (IPA) methods and derive an on-line gradient-based algorithm for obtaining optimal lot sizes from both a system-centric and user-centric perspective and observe that, uncharacteristically for such cases, there is no gap between the two solutions in the two-class case for which we have obtained explicit numerical results.
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CDC - A solution of the lot sizing problem as a stochastic Resource Contention game
49th IEEE Conference on Decision and Control (CDC), 2010Co-Authors: Chen Yao, Christos G CassandrasAbstract:We present a new way to solve the “lot sizing” problem viewed as a stochastic non-cooperative Resource Contention game. We develop a Stochastic Flow Model (SFM) for polling systems with non-negligible changeover times enabling us to formulate lot sizing as an optimization problem with no constraints on the distributional characteristics of the random processes in the system. We then use Infinitesimal Perturbation Analysis (IPA) methods and derive an on-line gradient-based algorithm for obtaining optimal lot sizes from both a system-centric and user-centric perspective and observe that, uncharacteristically for such cases, there is no gap between the two solutions in the two-class case for which we have obtained explicit numerical results.
Chen Yao - One of the best experts on this subject based on the ideXlab platform.
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A Solution to the Optimal Lot-Sizing Problem as a Stochastic Resource Contention Game
IEEE Transactions on Automation Science and Engineering, 2012Co-Authors: Chen Yao, Christos G CassandrasAbstract:We present a new way to solve the “lot-sizing” problem viewed as a stochastic noncooperative Resource Contention game. We develop a Stochastic Flow Model (SFM) for polling systems with non-negligible changeover times enabling us to formulate lot sizing as an optimization problem without imposing constraints on the distributional characteristics of the random processes in the system. Using Infinitesimal Perturbation Analysis (IPA) methods, we derive gradient estimators of the performance metrics of interests with respect to the lot-size parameters and prove they are unbiased. We then derive an online gradient-based algorithm for obtaining optimal lot sizes from both a system-centric and user-centric perspective. Uncharacteristically for such cases, there is no gap between the two solutions in the two-class case for which we have obtained explicit numerical results. We derive a proof of this phenomenon for a deterministic version of the problem, suggesting that lot-sizing-like scheduling policies in Resource Contention problems have a natural property of balancing certain user-centric and system-centric performance metrics.
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perturbation analysis of stochastic hybrid systems and applications to Resource Contention games
Frontiers of Electrical and Electronic Engineering in China, 2011Co-Authors: Chen Yao, Christos G CassandrasAbstract:We provide an overview of the recently developed general infinitesimal perturbation analysis (IPA) framework for stochastic hybrid systems (SHSs), and establish some conditions under which this framework can be used to obtain unbiased performance gradient estimates in a particularly simple and efficient manner. We also propose a general scheme for systematically deriving an abstraction of a discrete event system (DES) in the form of an SHS. Then, as an application of the general IPA framework, we study a class of stochastic non-cooperative games termed “Resource Contention games” modeled through stochastic flow models (SFMs), where two or more players (users) compete for the use of a sharable Resource. Simulation results are provided for a simple version of such games to illustrate and contrast system-centric and user-centric optimization.
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Resource Contention games in multiclass stochastic flow models
Nonlinear Analysis: Hybrid Systems, 2011Co-Authors: Chen Yao, Christos G CassandrasAbstract:Abstract We consider Resource Contention games in a stochastic hybrid system setting using Stochastic Flow Models (SFM) with multiple classes and class-dependent objectives. We present a general modeling framework for such games, where Infinitesimal Perturbation Analysis (IPA) estimators are derived for the derivatives of various class-dependent objectives. This allows us to study these games from the point of view of system-centric optimization of a performance metric and compare it to the user-centric approach where each user optimizes its own performance metric. We derive explicit solutions for a specific model in which the competing user classes employ threshold control policies and service is provided on a First Come First Serve (FCFS) basis. The unbiasedness of the IPA estimators is established in this case and it is shown that under certain conditions the system-centric and user-centric optimization solutions coincide.
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a solution of the lot sizing problem as a stochastic Resource Contention game
Conference on Decision and Control, 2010Co-Authors: Chen Yao, Christos G CassandrasAbstract:We present a new way to solve the “lot sizing” problem viewed as a stochastic non-cooperative Resource Contention game. We develop a Stochastic Flow Model (SFM) for polling systems with non-negligible changeover times enabling us to formulate lot sizing as an optimization problem with no constraints on the distributional characteristics of the random processes in the system. We then use Infinitesimal Perturbation Analysis (IPA) methods and derive an on-line gradient-based algorithm for obtaining optimal lot sizes from both a system-centric and user-centric perspective and observe that, uncharacteristically for such cases, there is no gap between the two solutions in the two-class case for which we have obtained explicit numerical results.
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CDC - A solution of the lot sizing problem as a stochastic Resource Contention game
49th IEEE Conference on Decision and Control (CDC), 2010Co-Authors: Chen Yao, Christos G CassandrasAbstract:We present a new way to solve the “lot sizing” problem viewed as a stochastic non-cooperative Resource Contention game. We develop a Stochastic Flow Model (SFM) for polling systems with non-negligible changeover times enabling us to formulate lot sizing as an optimization problem with no constraints on the distributional characteristics of the random processes in the system. We then use Infinitesimal Perturbation Analysis (IPA) methods and derive an on-line gradient-based algorithm for obtaining optimal lot sizes from both a system-centric and user-centric perspective and observe that, uncharacteristically for such cases, there is no gap between the two solutions in the two-class case for which we have obtained explicit numerical results.
Gerald Kotonya - One of the best experts on this subject based on the ideXlab platform.
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managing Resource Contention in embedded service oriented systems with dynamic orchestration
International Conference on Service Oriented Computing, 2012Co-Authors: Peter Newman, Gerald KotonyaAbstract:As embedded systems become increasingly complex, not only are dependability and timeliness indicators of success, but also the ability to dynamically adapt to changes in the runtime environment. Typically, they operate in Resource-constrained environments and often find application in isolated locations, making them expensive to manage with small Resource changes in their operating environment having a significant impact on system quality. The service-oriented model of deployment offers a possible solution to these challenges; however, Resource Contention between services and Resource saturation can result in significant Quality of Service (QoS) degradation. This emergent QoS is difficult to anticipate before deployment as changes in QoS are often dynamic. This paper presents EQoSystem, a runtime, Resource-aware framework that combines monitoring with dynamic workflow orchestration to mediate Resource Contention within the orchestration environment. The results from a medium-sized case study demonstrate the efficacy of EQoSystem.
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ICSOC - Managing Resource Contention in embedded service-oriented systems with dynamic orchestration
Service-Oriented Computing, 2012Co-Authors: Peter Newman, Gerald KotonyaAbstract:As embedded systems become increasingly complex, not only are dependability and timeliness indicators of success, but also the ability to dynamically adapt to changes in the runtime environment. Typically, they operate in Resource-constrained environments and often find application in isolated locations, making them expensive to manage with small Resource changes in their operating environment having a significant impact on system quality. The service-oriented model of deployment offers a possible solution to these challenges; however, Resource Contention between services and Resource saturation can result in significant Quality of Service (QoS) degradation. This emergent QoS is difficult to anticipate before deployment as changes in QoS are often dynamic. This paper presents EQoSystem, a runtime, Resource-aware framework that combines monitoring with dynamic workflow orchestration to mediate Resource Contention within the orchestration environment. The results from a medium-sized case study demonstrate the efficacy of EQoSystem.
Donghyun Kang - One of the best experts on this subject based on the ideXlab platform.
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ESTImedia - Worst case delay analysis of shared Resource access in partitioned multi-core systems
Proceedings of the 15th IEEE ACM Symposium on Embedded Systems for Real-Time Multimedia - ESTIMedia '17, 2017Co-Authors: Donghyun Kang, Junchul ChoiAbstract:In the worst case response time (WCRT) analysis of multi-core systems with shared Resources, non-deterministic arbitration delay due to Resource Contention should be considered conservatively. In this paper, we propose a novel technique for modeling the shared Resource Contention to find a more accurate upper bound of arbitration delay than the state-of-the-art technique. After computing the worst-case Resource demand from each processing element based on the event stream model, we consider the possible scheduling pattern of tasks to make a tighter estimation. The performance of proposed technique is verified by extensive experiments with MediaBench benchmark applications, synthetic task sets, and a real-life automotive example.
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conservative modeling of shared Resource Contention for dependent tasks in partitioned multi core systems
Design Automation and Test in Europe, 2016Co-Authors: Junchul Choi, Donghyun KangAbstract:In a multi-core system with shared Resources, the accesses to the shared Resources from several cores may experience non-deterministic arbitration delay due to Resource Contention. Such delay should be considered conservatively in the worst case response time (WCRT) analysis of multi-core systems. Recently, several techniques have been proposed to account for arbitration delay for shared Resource Contention, based on the event stream modeling of Resource access. While they all assume independent tasks, in this paper, we propose a conservative modeling technique of shared Resource Contention supporting dependent tasks. To find a tight upper bound of arbitration delay, we derive a shared Resource demand bound for each processing element, considering the task dependency. The proposed technique is not specific to a particular WCRT analysis method, and supports both preemptive and non-preemptive scheduling policy. In the experiments, the significance of considering data dependency of parallel applications and the performance of our technique are verified by synthetic examples and a real-life example.
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DATE - Conservative modeling of shared Resource Contention for dependent tasks in partitioned multi-core systems
Proceedings of the 2016 Design Automation & Test in Europe Conference & Exhibition (DATE), 2016Co-Authors: Junchul Choi, Donghyun KangAbstract:In a multi-core system with shared Resources, the accesses to the shared Resources from several cores may experience non-deterministic arbitration delay due to Resource Contention. Such delay should be considered conservatively in the worst case response time (WCRT) analysis of multi-core systems. Recently, several techniques have been proposed to account for arbitration delay for shared Resource Contention, based on the event stream modeling of Resource access. While they all assume independent tasks, in this paper, we propose a conservative modeling technique of shared Resource Contention supporting dependent tasks. To find a tight upper bound of arbitration delay, we derive a shared Resource demand bound for each processing element, considering the task dependency. The proposed technique is not specific to a particular WCRT analysis method, and supports both preemptive and non-preemptive scheduling policy. In the experiments, the significance of considering data dependency of parallel applications and the performance of our technique are verified by synthetic examples and a real-life example.
Steven Scheding - One of the best experts on this subject based on the ideXlab platform.
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modelling Resource Contention in multi robot task allocation problems with uncertain timing
International Conference on Robotics and Automation, 2018Co-Authors: Andrew W. Palmer, Andrew J. Hill, Steven SchedingAbstract:This paper proposes an analytical framework for modelling Resource Contention in multi-robot systems, where the travel times and task durations are uncertain. It uses several approximation methods to quickly and accurately calculate the probability distributions describing the times at which the tasks start and finish. Specific contributions include exact and fast approximation methods for calculating the probability of a set of independent normally distributed random events occurring in a given order, a method for calculating the most likely and $n$ -th most likely orders of occurrence for a set of independent normally distributed random events that have equal standard deviations, and a method for approximating the conditional probability distributions of the events given a specific order of the events. The complete framework is shown to be faster than a Monte Carlo approach for the same accuracy in two multi-robot task allocation problems. In addition, the importance of incorporating uncertainty is demonstrated through a comparison with a deterministic method. This is a general framework that is agnostic to the optimisation method and objective function used, and is applicable to a wide range of problems.
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ICRA - Modelling Resource Contention in Multi-Robot Task Allocation Problems with Uncertain Timing
2018 IEEE International Conference on Robotics and Automation (ICRA), 2018Co-Authors: Andrew W. Palmer, Andrew J. Hill, Steven SchedingAbstract:This paper proposes an analytical framework for modelling Resource Contention in multi-robot systems, where the travel times and task durations are uncertain. It uses several approximation methods to quickly and accurately calculate the probability distributions describing the times at which the tasks start and finish. Specific contributions include exact and fast approximation methods for calculating the probability of a set of independent normally distributed random events occurring in a given order, a method for calculating the most likely and $n$ -th most likely orders of occurrence for a set of independent normally distributed random events that have equal standard deviations, and a method for approximating the conditional probability distributions of the events given a specific order of the events. The complete framework is shown to be faster than a Monte Carlo approach for the same accuracy in two multi-robot task allocation problems. In addition, the importance of incorporating uncertainty is demonstrated through a comparison with a deterministic method. This is a general framework that is agnostic to the optimisation method and objective function used, and is applicable to a wide range of problems.
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Multi-robot task allocation with Resource Contention and uncertain timing.
arXiv: Multiagent Systems, 2016Co-Authors: Andrew W. Palmer, Andrew J. Hill, Steven SchedingAbstract:This paper proposes an analytical framework for modelling Resource Contention in multi-robot systems, where the travel times and task durations are uncertain. It uses several approximation methods to quickly and accurately calculate the probability distributions describing the times that tasks start and finish. Specific contributions include a method for calculating the probability of a set of independent normally distributed random events occurring in a given order, an upper bound on that probability, and a method for calculating the most likely and $n$-th most likely orders of occurrence for a set of independent normally distributed random events that have equal standard deviations. The complete framework is shown to be much faster than a Monte Carlo approach for the same accuracy in two multi-robot task allocation problems. This is a general framework that is agnostic to the optimisation method and objective function used, and is applicable to a wide range of robotics and non-robotics problems.
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Modelling Resource Contention in multi-robot task allocation problems with uncertain timing.
arXiv: Multiagent Systems, 2016Co-Authors: Andrew W. Palmer, Andrew J. Hill, Steven SchedingAbstract:This paper proposes an analytical framework for modelling Resource Contention in multi-robot systems, where the travel times and task durations are uncertain. It uses several approximation methods to quickly and accurately calculate the probability distributions describing the times at which the tasks start and finish. Specific contributions include a method for calculating the probability of a set of independent normally distributed random events occurring in a given order, an upper bound on that probability, and a method for calculating the most likely and $n$-th most likely orders of occurrence for a set of independent normally distributed random events that have equal standard deviations. The complete framework is shown to be much faster than a Monte Carlo approach for the same accuracy in two multi-robot task allocation problems. This is a general framework that is agnostic to the optimisation method and objective function used, and is applicable to a wide range of robotics and non-robotics problems.
