The Experts below are selected from a list of 22221 Experts worldwide ranked by ideXlab platform
Changjiu Xian - One of the best experts on this subject based on the ideXlab platform.
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energy aware scheduling for real time Multiprocessor Systems with uncertain task execution time
Design Automation Conference, 2007Co-Authors: Changjiu XianAbstract:This paper presents an energy-aware method to schedule multiple real-time tasks in Multiprocessor Systems that support dynamic voltage scaling (DVS). The key difference from existing approaches is that we consider the probabilistic distributions of the tasks' execution time to partition the workload for better energy reduction. We analyze the problem of energy-aware scheduling for Multiprocessor with probabilistic workload information and derive its mathematical formulation. As the problem is NP-hard, we present a polynomial-time heuristic method to transform the problem into a probability-based load balancing problem that is then solved with worst-fit decreasing bin-packing heuristic. Simulation results with synthetic, multimedia, and stereo- vision tasks show that our method saves significantly more energy than existing methods.
Alan Burns - One of the best experts on this subject based on the ideXlab platform.
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a survey of hard real time scheduling for Multiprocessor Systems
ACM Computing Surveys, 2011Co-Authors: Robert I Davis, Alan BurnsAbstract:This survey covers hard real-time scheduling algorithms and schedulability analysis techniques for homogeneous Multiprocessor Systems. It reviews the key results in this field from its origins in the late 1960s to the latest research published in late 2009. The survey outlines fundamental results about Multiprocessor real-time scheduling that hold independent of the scheduling algorithms employed. It provides a taxonomy of the different scheduling methods, and considers the various performance metrics that can be used for comparison purposes. A detailed review is provided covering partitioned, global, and hybrid scheduling algorithms, approaches to resource sharing, and the latest results from empirical investigations. The survey identifies open issues, key research challenges, and likely productive research directions.
Hao Chen - One of the best experts on this subject based on the ideXlab platform.
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energy aware data allocation and task scheduling on heterogeneous Multiprocessor Systems with time constraints
IEEE Transactions on Emerging Topics in Computing, 2014Co-Authors: Yan Wang, Hao ChenAbstract:In this paper, we address the problem of energy-aware heterogeneous data allocation and task scheduling on heterogeneous Multiprocessor Systems for real-time applications. In a heterogeneous distributed shared-memory Multiprocessor system, an important problem is how to assign processors to real-time application tasks, allocate data to local memories, and generate an efficient schedule in such a way that a time constraint can be met and the total system energy consumption can be minimized. We propose an optimal approach, i.e., an integer linear programming method, to solve this problem. As the problem has been conclusively shown to be computationally very complicated, we also present two heuristic algorithms, i.e., task assignment considering data allocation (TAC-DA) and task ratio greedy scheduling (TRGS), to generate near-optimal solutions for real-time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm that is commonly used to solve heterogeneous task scheduling problems. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance. We conducted experimental performance evaluation on two heterogeneous Multiprocessor Systems. The average reduction rates of the total energy consumption of the TAC-DA and TRGS algorithms to that of the greedy algorithm are 13.72% and 15.76%, respectively, on the first system, and 19.76% and 24.67%, respectively, on the second system. To the best of our knowledge, this is the first study to solve the problem of task scheduling incorporated with data allocation and energy consumption on heterogeneous distributed shared-memory Multiprocessor Systems.
Daniel F Garcia - One of the best experts on this subject based on the ideXlab platform.
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utilization bounds for edf scheduling on real time Multiprocessor Systems
Real-time Systems, 2004Co-Authors: Jose Maria Cuenca Lopez, J L Diaz, Daniel F GarciaAbstract:The utilization bound for earliest deadline first (EDF) scheduling is extended from uniprocessors to homogeneous Multiprocessor Systems with partitioning strategies. First results are provided for a basic task model, which includes periodic and independent tasks with deadlines equal to periods. Since the Multiprocessor utilization bounds depend on the allocation algorithm, different allocation algorithms have been considered, ranging from simple heuristics to optimal allocation algorithms. As Multiprocessor utilization bounds for EDF scheduling depend strongly on task sizes, all these bounds have been obtained as a function of a parameter which takes task sizes into account. Theoretically, the utilization bounds for Multiprocessor EDF scheduling can be considered a partial solution to the bin-packing problem, which is known to be NP-complete. The basic task model is extended to include resource sharing, release jitter, deadlines less than periods, aperiodic tasks, non-preemptive sections, context switches, and mode changes.
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worst case utilization bound for edf scheduling on real time Multiprocessor Systems
Euromicro Conference on Real-Time Systems, 2000Co-Authors: Jose Maria Cuenca Lopez, J L Diaz, Manuel Garcia, Daniel F GarciaAbstract:Presents the utilization bound for earliest deadline first (EDF) scheduling on homogeneous Multiprocessor Systems with partitioning strategies. Assuming that tasks are pre-emptively scheduled on each processor according to the EDF algorithm, and allocated according to the first-fit (FF) heuristic, we prove that the worst-case achievable utilization is 0.5(n+1), where n is the number of processors. This bound is valid for arbitrary utilization factors. Moreover, if all the tasks have utilization factors under a value /spl alpha/, the previous bound is raised, and the new utilization bound considering /spl alpha/ is calculated. In addition, we prove that no uniprocessor scheduling algorithm/allocation algorithm pair can provide a higher worst-case achievable utilization than that of EDF-FF. Finally, simulation provides the average-case achievable utilization for EDF-FF.
Niraj K Jha - One of the best experts on this subject based on the ideXlab platform.
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graceful degradation in algorithm based fault tolerant Multiprocessor Systems
IEEE Transactions on Parallel and Distributed Systems, 1997Co-Authors: S Yajnik, Niraj K JhaAbstract:Algorithm-based fault tolerance (ABFT) is a technique which improves the reliability of a Multiprocessor system by providing concurrent error detection and fault location capability to it. It encodes data at the system level and modifies the algorithm to operate on the encoded data in order to expose both transient and permanent faults in any processor. Work done till now in this area takes care of only the fault detection and location part of the problem. However, if spare processors are not available, then after a faulty processor has been located, the work initially assigned to it has to be mapped to some nonfaulty processors in the system in such a way that the fault tolerance capability of the system is still maintained with as small a degradation in performance as possible. In this paper, we propose an integrated deterministic solution to the above problem which combines concurrent error detection and fault location with graceful degradation. There exists no previous deterministic ABFT method for the design of general t-fault locating Systems, even for the case of t=1. We propose a general method for designing one-fault locating/s-fault detecting Systems. We use an extended model for representing ABFT Systems. This model considers the processors computing the checks to be a part of the ABFT system, so that faults in the check computing processors can also be detected and located using a simple diagnosis algorithm, and the checks can be mapped to other nonfaulty processors in the system.
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graceful degradation in algorithm based fault tolerant Multiprocessor Systems
International Symposium on Circuits and Systems, 1994Co-Authors: S Yajnik, Niraj K JhaAbstract:Algorithm-based fault tolerance (ABFT) is a technique for improving the reliability of a Multiprocessor system by providing concurrent error detection and fault location capability to it. In this paper, we propose the first integrated solution to the problem of fault detection, location and graceful degradation in ABFT Systems. Unlike most previous methods, we use an extended model for representing ABFT Systems, which allows faults to occur in check computing processors. >
