The Experts below are selected from a list of 30108 Experts worldwide ranked by ideXlab platform
M A Elbestawi - One of the best experts on this subject based on the ideXlab platform.
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optimized feed Scheduling in three axes machining part i fundamentals of the optimized feed Scheduling Strategy
International Journal of Machine Tools & Manufacture, 2003Co-Authors: N Tounsi, M A ElbestawiAbstract:An optimized feed Scheduling Strategy is proposed in this paper to maximize the metal removal rate in 3-axis machining while guaranteeing the machining accuracy. The tool path is assumed defined by a cubic parametric form. In part I of this paper, the fundamentals of this Strategy are presented. This Strategy integrates the feed drive dynamics, described by the acceleration/deceleration (Acc/Dec) profile, with the minimum-time trajectory planning in order to achieve the desired feed rate at the appropriate position. An optimum use of the feed drive capabilities is considered to track the changes in the cutting geometry along the tool path and to ensure an acceptable contour error. Therefore, this Strategy combines different constraints and various criteria in modifying the feed rate to maintain a near-constant cutting force resulting in a highly non-linear problem. The constraints include the cutting force magnitude, the feed rate boundaries, the contour error and the characteristics of the (Acc/Dec) profile of the different feed drive systems. The criteria are the maximum production rate, the machining accuracy and safety. In part II of this paper, the effectiveness of this Strategy is demonstrated using ball end mill operation on a workpiece that provides variable cutting geometry along a non-linear tool path. The performance of this Strategy in terms of productivity, machining safety, and machining accuracy, is compared to a feed Scheduling Strategy based on control points as well as to milling with constant feed rate.
D K Pradhan - One of the best experts on this subject based on the ideXlab platform.
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job Scheduling in mesh multicomputers
IEEE Transactions on Parallel and Distributed Systems, 1998Co-Authors: Das D Sharma, D K PradhanAbstract:A new approach for dynamic job Scheduling in mesh-connected multiprocessor systems, which supports a multiuser environment, is proposed in this paper. Our approach combines a submesh reservation policy with a priority-based Scheduling policy to obtain high performance in terms of high throughput, high utilization, and low turn-around times for jobs. This high performance is achieved at the expense of Scheduling jobs in a strictly fair, FCFS fashion; in fact, the algorithm is parameterized to allow trade-offs between performance and (short-term) POPS fairness. The proposed scheduler can be used with any submesh allocation policy. A fast and efficient implementation of the proposed scheduler has also been presented. The performance of the proposed scheme has been compared with the FCFS policy, the only existing Scheduling Strategy for meshes, to demonstrate the effectiveness of the proposed approach. Simulation results indicate that our Scheduling Strategy outperforms the FCFS policy significantly. Specifically, our Strategy significantly reduces the average waiting delay of jobs over the FCFS policy. The fast implementation of the proposed scheduler results in low allocation and deallocation time overhead, as well as low space overhead.
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job Scheduling in mesh multicomputers
International Conference on Parallel Processing, 1994Co-Authors: Das D Sharma, D K PradhanAbstract:A new approach for dynamic job Scheduling in mesh-connected multiprocessor system, which supports a multi-user environment, is proposed. The proposed job scheduler combines a priority-based Scheduling policy with a submesh reservation policy to obtain high performance in terms of high throughput, high utilization and low turn-around times for jobs. The proposed Scheduling Strategy offers the flexibility of achieving high performance at the expense of short-term 'fairness' towards certain jobs. A fast and efficient implementation of the proposed scheduler has also been presented. Simulation results indicate that our Scheduling Strategy outperforms the FCFS policy significantly by reducing the average waiting delay significantly.
Hongtao Lei - One of the best experts on this subject based on the ideXlab platform.
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a multi objective co evolutionary algorithm for energy efficient Scheduling on a green data center
Computers & Operations Research, 2016Co-Authors: Hongtao Lei, Rui Wang, Tao Zhang, Yajie Liu, Yabing ZhaAbstract:Nowadays, the environment protection and the energy crisis prompt more computing centers and data centers to use the green renewable energy in their power supply. To improve the efficiency of the renewable energy utilization and the task implementation, the computational tasks of data center should match the renewable energy supply. This paper considers a multi-objective energy-efficient task Scheduling problem on a green data center partially powered by the renewable energy, where the computing nodes of the data center are DVFS-enabled. An enhanced multi-objective co-evolutionary algorithm, called OL-PICEA-g, is proposed for solving the problem, where the PICEA-g algorithm with the generalized opposition based learning is applied to search the suitable computing node, supply voltage and clock frequency for the task computation, and the smart time Scheduling Strategy is employed to determine the start and finish time of the task on the chosen node. In the experiments, the proposed OL-PICEA-g algorithm is compared with the PICEA-g algorithm, the smart time Scheduling Strategy is compared with two other Scheduling strategies, i.e., Green-Oriented Scheduling Strategy and Time-Oriented Scheduling Strategy, different parameters are also tested on the randomly generated instances. Experimental results confirm the superiority and effectiveness of the proposed algorithm. HighlightsWe consider multi-objective energy efficient Scheduling on a green data center.The task model, energy model and Scheduling model are defined for the problem.An enhanced PICEA-g algorithm is proposed for solving the problem.The proposed algorithm is compared and tested on the generated instances.
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sgeess smart green energy efficient Scheduling Strategy with dynamic electricity price for data center
Journal of Systems and Software, 2015Co-Authors: Hongtao Lei, Tao Zhang, Yajie Liu, Yabing Zha, Xiaomin ZhuAbstract:Nowadays, it becomes a major trend to use the green renewable energy in the data center when considering the environment protection and the energy crisis. To improve the energy efficiency and save the system cost, the computational tasks of data center should match to the renewable energy supply. This paper aims to develop a smart green energy-efficient Scheduling Strategy to increase utilization of renewable energy, reduce system running cost and improve the task satisfaction rate in a data center partially powered by the renewable energy. We first define three mathematical models, i.e., task model, energy model and Scheduling model for the proposed problem. Then, a smart green energy-efficient Scheduling Strategy is proposed for the task Scheduling in the data center, based on the renewable energy prediction and the dynamic grid electricity price. In the experiments, three other Scheduling strategies, i.e., Green-Scheduling Strategy, Price-Scheduling Strategy and Greedy-Energy-Efficient Strategy, are provided for comparisons, a real-world trace of Google cloud trace is also tested. The experimental results confirm the superiority and effectiveness of the proposed Scheduling Strategy.
Xiaomin Zhu - One of the best experts on this subject based on the ideXlab platform.
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sgeess smart green energy efficient Scheduling Strategy with dynamic electricity price for data center
Journal of Systems and Software, 2015Co-Authors: Hongtao Lei, Tao Zhang, Yajie Liu, Yabing Zha, Xiaomin ZhuAbstract:Nowadays, it becomes a major trend to use the green renewable energy in the data center when considering the environment protection and the energy crisis. To improve the energy efficiency and save the system cost, the computational tasks of data center should match to the renewable energy supply. This paper aims to develop a smart green energy-efficient Scheduling Strategy to increase utilization of renewable energy, reduce system running cost and improve the task satisfaction rate in a data center partially powered by the renewable energy. We first define three mathematical models, i.e., task model, energy model and Scheduling model for the proposed problem. Then, a smart green energy-efficient Scheduling Strategy is proposed for the task Scheduling in the data center, based on the renewable energy prediction and the dynamic grid electricity price. In the experiments, three other Scheduling strategies, i.e., Green-Scheduling Strategy, Price-Scheduling Strategy and Greedy-Energy-Efficient Strategy, are provided for comparisons, a real-world trace of Google cloud trace is also tested. The experimental results confirm the superiority and effectiveness of the proposed Scheduling Strategy.
Urbashi Mitra - One of the best experts on this subject based on the ideXlab platform.
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optimal Scheduling Strategy for networked estimation with energy harvesting
IEEE Transactions on Control of Network Systems, 2020Co-Authors: Marcos M Vasconcelos, Mukul Gagrani, Ashutosh Nayyar, Urbashi MitraAbstract:Joint optimization of Scheduling and estimation policies is considered for a system with two sensors and two noncollocated estimators. Each sensor produces an independent and identically distributed sequence of random variables, and each estimator forms estimates of the corresponding sequence with respect to the mean-squared error sense. The data generated by the sensors are transmitted to the corresponding estimators over a bandwidth-constrained wireless network that can support a single packet per time slot. The access to the limited communication resources is determined by a scheduler that decides which sensor measurement to transmit based on both observations. The scheduler has an energy-harvesting battery of limited capacity, which couples the decision-making problem in time. Despite the overall lack of convexity of this problem, it is shown that this system admits a globally optimal Scheduling and estimation Strategy pair under the assumption that the distributions of the random variables at the sensors are symmetric and unimodal. Additionally, the optimal Scheduling policy has a structure characterized by a threshold function that depends on the time index and energy level. A recursive algorithm for threshold computation is provided.
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optimal Scheduling Strategy for networked estimation with energy harvesting
arXiv e-prints, 2019Co-Authors: Marcos M Vasconcelos, Mukul Gagrani, Ashutosh Nayyar, Urbashi MitraAbstract:Joint optimization of Scheduling and estimation policies is considered for a system with two sensors and two non-collocated estimators. Each sensor produces an independent and identically distributed sequence of random variables, and each estimator forms estimates of the corresponding sequence with respect to the mean-squared error sense. The data generated by the sensors is transmitted to the corresponding estimators, over a bandwidth-constrained wireless network that can support a single packet per time slot. The access to the limited communication resources is determined by a scheduler who decides which sensor measurement to transmit based on both observations. The scheduler has an energy-harvesting battery of limited capacity, which couples the decision-making problem in time. Despite the overall lack of convexity of the team decision problem, it is shown that this system admits globally optimal Scheduling and estimation strategies under the assumption that the distributions of the random variables at the sensors are symmetric and unimodal. Additionally, the optimal Scheduling policy has a structure characterized by a threshold function that depends on the time index and energy level. A recursive algorithm for threshold computation is provided.
