The Experts below are selected from a list of 240 Experts worldwide ranked by ideXlab platform

Nic Wilson - One of the best experts on this subject based on the ideXlab platform.

  • a soft global Precedence Constraint
    International Joint Conference on Artificial Intelligence, 2009
    Co-Authors: David Lesaint, Deepak Mehta, Barry Osullivan, Luis Quesada, Nic Wilson
    Abstract:

    Hard and soft Precedence Constraints play a key role in many application domains. In telecommunications, one application is the configuration of callcontrol feature subscriptions where the task is to sequence a set of user-selected features subject to a set of hard (catalogue) Precedence Constraints and a set of soft (user-selected) Precedence Constraints. When no such consistent sequence exists, the task is to find an optimal relaxation by discarding some features or user Precedences. For this purpose, we present the global Constraint SOFTPREC. Enforcing Generalized Arc Consistency (GAC) on SOFTPREC is NP-complete. Therefore, we approximate GAC based on domain pruning rules that follow from the semantics of SOFTPREC; this pruning is polynomial. Empirical results demonstrate that the search effort required by SOFTPREC is up to one order of magnitude less than the previously known best CP approach for the feature subscription problem. SOFTPREC is also applicable to other problem domains including minimum cutset problems for which initial experiments confirm the interest.

  • IJCAI - A soft global Precedence Constraint
    2009
    Co-Authors: David Lesaint, Deepak Mehta, Luis Quesada, Barry O'sullivan, Nic Wilson
    Abstract:

    Hard and soft Precedence Constraints play a key role in many application domains. In telecommunications, one application is the configuration of callcontrol feature subscriptions where the task is to sequence a set of user-selected features subject to a set of hard (catalogue) Precedence Constraints and a set of soft (user-selected) Precedence Constraints. When no such consistent sequence exists, the task is to find an optimal relaxation by discarding some features or user Precedences. For this purpose, we present the global Constraint SOFTPREC. Enforcing Generalized Arc Consistency (GAC) on SOFTPREC is NP-complete. Therefore, we approximate GAC based on domain pruning rules that follow from the semantics of SOFTPREC; this pruning is polynomial. Empirical results demonstrate that the search effort required by SOFTPREC is up to one order of magnitude less than the previously known best CP approach for the feature subscription problem. SOFTPREC is also applicable to other problem domains including minimum cutset problems for which initial experiments confirm the interest.

David Lesaint - One of the best experts on this subject based on the ideXlab platform.

  • a soft global Precedence Constraint
    International Joint Conference on Artificial Intelligence, 2009
    Co-Authors: David Lesaint, Deepak Mehta, Barry Osullivan, Luis Quesada, Nic Wilson
    Abstract:

    Hard and soft Precedence Constraints play a key role in many application domains. In telecommunications, one application is the configuration of callcontrol feature subscriptions where the task is to sequence a set of user-selected features subject to a set of hard (catalogue) Precedence Constraints and a set of soft (user-selected) Precedence Constraints. When no such consistent sequence exists, the task is to find an optimal relaxation by discarding some features or user Precedences. For this purpose, we present the global Constraint SOFTPREC. Enforcing Generalized Arc Consistency (GAC) on SOFTPREC is NP-complete. Therefore, we approximate GAC based on domain pruning rules that follow from the semantics of SOFTPREC; this pruning is polynomial. Empirical results demonstrate that the search effort required by SOFTPREC is up to one order of magnitude less than the previously known best CP approach for the feature subscription problem. SOFTPREC is also applicable to other problem domains including minimum cutset problems for which initial experiments confirm the interest.

  • IJCAI - A soft global Precedence Constraint
    2009
    Co-Authors: David Lesaint, Deepak Mehta, Luis Quesada, Barry O'sullivan, Nic Wilson
    Abstract:

    Hard and soft Precedence Constraints play a key role in many application domains. In telecommunications, one application is the configuration of callcontrol feature subscriptions where the task is to sequence a set of user-selected features subject to a set of hard (catalogue) Precedence Constraints and a set of soft (user-selected) Precedence Constraints. When no such consistent sequence exists, the task is to find an optimal relaxation by discarding some features or user Precedences. For this purpose, we present the global Constraint SOFTPREC. Enforcing Generalized Arc Consistency (GAC) on SOFTPREC is NP-complete. Therefore, we approximate GAC based on domain pruning rules that follow from the semantics of SOFTPREC; this pruning is polynomial. Empirical results demonstrate that the search effort required by SOFTPREC is up to one order of magnitude less than the previously known best CP approach for the feature subscription problem. SOFTPREC is also applicable to other problem domains including minimum cutset problems for which initial experiments confirm the interest.

Sheng-de Wang - One of the best experts on this subject based on the ideXlab platform.

  • Heuristic task assignment for distributed computing systems
    Information Sciences, 1992
    Co-Authors: Chiun-chieh Hsu, Sheng-de Wang
    Abstract:

    Abstract This paper addresses the problem of assigning a task with Precedence Constraint to a distributed computing system. The task turnaround time including communication overhead and idle time is adopted to measure the performance of task assignment. The task assignment in this paper requires one to determine not only the assignment of modules, but also the sequence of message transmission to balance processor loads and diminish communication overhead. The search for the optimal task assignment with Precedence Constraint is known to be NP-complete in the strong sense. A heuristic algorithm with polynomial time complexity is then proposed in order to effectively solve the task assignment problem. The experimental results reveal that the proposed approach is able to obtain a near-optimal or even the optimal task assignment.

Arijit Mondal - One of the best experts on this subject based on the ideXlab platform.

  • timing analysis of Precedence Constraint messages scheduled with slot multiplexing over dynamic segment of flexray
    IEEE Transactions on Automation Science and Engineering, 2020
    Co-Authors: Niraj Kumar, Arijit Mondal
    Abstract:

    FlexRay offers high-speed reliable transmission in the vehicle domain. Thus, it is one of the most popular in-vehicle communication protocols for the applications that are safety-critical or applications with very low transmission time requirement. Due to the real-time Constraint, the worst case end-to-end delay (EED) for such applications transmitting over a shared bus must be known. This paper addresses the problem of computation of EED for a given set of tasks with Precedence Constraint and corresponding messages, which are scheduled with slot multiplexing for event-triggered communication over the dynamic segment of FlexRay. First, computing precise delay has been shown to be an intractable problem. Subsequently, motivated by the intractability, an efficient estimation technique has been proposed to compute approximate EED. Unlike the common practice of considering the network in isolation, we present a comprehensive timing analysis, which considers the effects of task execution as well. Moreover, EED is allowed to be greater than interactivation time of task graph. The extensive simulation has been performed on the test cases generated with uniform as well as normal distribution. Note to Practitioners —In the past few decades, due to the large-scale use and sophistication of automotive electronic systems, various in-vehicle communication networks and protocols are also evolving. FlexRay is one such in-vehicle communication protocol, which provides the mechanism for the fault-tolerant high-speed communication over FlexRay bus. FlexRay is widely being employed for safety-critical applications in the automotive domain. For such applications, often the timing Constraint is imposed with a bound on the worst case EED. Thus, timing properties of the designed network must be verified before applying it on the real domain such that the worst case EED for the propagation of an event at sensor end to actuator end is known. The proposed estimation technique can be used to compute approximate EED for an application scheduled with other applications over the dynamic segment of FlexRay. In addition, various guidelines for designing the network in order to reduce overestimation have also been presented.

  • schedule synthesis for the messages with Precedence Constraint over dynamic segment of the flexray protocol
    2019 9th International Symposium on Embedded Computing and System Design (ISED), 2019
    Co-Authors: Niraj Kumar, Arijit Mondal
    Abstract:

    FlexRay is the leading in-vehicle communication protocol for safety-critical applications in the automotive industry. Due to the richness of configurable parameters, topologies and support to time-triggered as well as event-triggered communication, FlexRay is widely being employed for the safety-critical applications. In this work, we address the problem of priority assignment for the given set of dependent messages which are to be scheduled for the event-triggered communication. As the problem of scheduling has been reported to be computationally intractable, thus we propose three efficient heuristics of varying yields and complexity. Each heuristic computes a schedule that respects the dependency and protocol-specific Constraints as well as minimizes the end-to-end delay. Unlike most of the existing works, rather than considering the network in isolation, the effect of the execution time of tasks has also been taken into consideration. To evaluate the performance of the proposed heuristics extensive simulation has been performed. Additionally, the guidelines to design the network have also been provided, such that the end-to-end delay is minimum.

Deepak Mehta - One of the best experts on this subject based on the ideXlab platform.

  • a soft global Precedence Constraint
    International Joint Conference on Artificial Intelligence, 2009
    Co-Authors: David Lesaint, Deepak Mehta, Barry Osullivan, Luis Quesada, Nic Wilson
    Abstract:

    Hard and soft Precedence Constraints play a key role in many application domains. In telecommunications, one application is the configuration of callcontrol feature subscriptions where the task is to sequence a set of user-selected features subject to a set of hard (catalogue) Precedence Constraints and a set of soft (user-selected) Precedence Constraints. When no such consistent sequence exists, the task is to find an optimal relaxation by discarding some features or user Precedences. For this purpose, we present the global Constraint SOFTPREC. Enforcing Generalized Arc Consistency (GAC) on SOFTPREC is NP-complete. Therefore, we approximate GAC based on domain pruning rules that follow from the semantics of SOFTPREC; this pruning is polynomial. Empirical results demonstrate that the search effort required by SOFTPREC is up to one order of magnitude less than the previously known best CP approach for the feature subscription problem. SOFTPREC is also applicable to other problem domains including minimum cutset problems for which initial experiments confirm the interest.

  • IJCAI - A soft global Precedence Constraint
    2009
    Co-Authors: David Lesaint, Deepak Mehta, Luis Quesada, Barry O'sullivan, Nic Wilson
    Abstract:

    Hard and soft Precedence Constraints play a key role in many application domains. In telecommunications, one application is the configuration of callcontrol feature subscriptions where the task is to sequence a set of user-selected features subject to a set of hard (catalogue) Precedence Constraints and a set of soft (user-selected) Precedence Constraints. When no such consistent sequence exists, the task is to find an optimal relaxation by discarding some features or user Precedences. For this purpose, we present the global Constraint SOFTPREC. Enforcing Generalized Arc Consistency (GAC) on SOFTPREC is NP-complete. Therefore, we approximate GAC based on domain pruning rules that follow from the semantics of SOFTPREC; this pruning is polynomial. Empirical results demonstrate that the search effort required by SOFTPREC is up to one order of magnitude less than the previously known best CP approach for the feature subscription problem. SOFTPREC is also applicable to other problem domains including minimum cutset problems for which initial experiments confirm the interest.