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Thematische Bibliographien / Job parallèle

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Auswahl der wissenschaftlichen Literatur zum Thema „Job parallèle“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

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Zeitschriftenartikel zum Thema "Job parallèle"

1

Brecht, Timothy, Xiaotie Deng, and Nian Gu. "Competitive Dynamic Multiprocessor Allocation for Parallel Applications." Parallel Processing Letters 07, no. 01 (1997): 89–100. http://dx.doi.org/10.1142/s0129626497000115.

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We study dynamic multiprocessor allocation policies for parallel jobs, which allow the preemption and reallocation of processors to take place at any time. The objective is to minimize the completion time of the last job to finish executing (the makespan). We characterize a parallel job using two parameter. The job's parallelism, Pi, which is the number of tasks being executed in parallel by a job, and its execution time, li, when Pi processors are allocated to the job. The only information available to the scheduler is the parallelism of jobs. The job execution time is not known to the schedu

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2

Weng, Wentao, and Weina Wang. "Achieving Zero Asymptotic Queueing Delay for Parallel Jobs." ACM SIGMETRICS Performance Evaluation Review 49, no. 1 (2022): 25–26. http://dx.doi.org/10.1145/3543516.3456268.

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Zero queueing delay is highly desirable in large-scale computing systems. Existing work has shown that it can be asymptotically achieved by using the celebrated Power-of-d-choices (Pod) policy with a probe overhead d = Ω(log N/1-λ), and it is impossible when d = O(1/1-λ), where N is the number of servers and λ is the load of the system. However, these results are based on the model where each job is an indivisible unit, which does not capture the parallel structure of jobs in today's predominant parallel computing paradigm. This paper considers a model where each job consists of a batch of par

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3

Xu, Susan H. "On a job resequencing issue in parallel processor stochastic scheduling." Advances in Applied Probability 24, no. 4 (1992): 915–33. http://dx.doi.org/10.2307/1427719.

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In flexible assembly systems, it is often necessary to coordinate jobs and materials so that specific jobs are matched with specific materials. This requires that jobs depart from upstream parallel workstations in some predetermined order. One way to satisfy this requirement is to temporarily hold the serviced jobs getting out of order at a resequencing buffer and to release them to downstream workstations as soon as all their predecessors are serviced. In this paper we consider the problem of scheduling a fixed number of non-preemptive jobs on two IHR non-identical processors with the reseque

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4

Xu, Susan H. "On a job resequencing issue in parallel processor stochastic scheduling." Advances in Applied Probability 24, no. 04 (1992): 915–33. http://dx.doi.org/10.1017/s0001867800025015.

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In flexible assembly systems, it is often necessary to coordinate jobs and materials so that specific jobs are matched with specific materials. This requires that jobs depart from upstream parallel workstations in some predetermined order. One way to satisfy this requirement is to temporarily hold the serviced jobs getting out of order at a resequencing buffer and to release them to downstream workstations as soon as all their predecessors are serviced. In this paper we consider the problem of scheduling a fixed number of non-preemptive jobs on two IHR non-identical processors with the reseque

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5

Kumar, P. R., and J. Walrand. "Individually optimal routing in parallel systems." Journal of Applied Probability 22, no. 4 (1985): 989–95. http://dx.doi.org/10.2307/3213970.

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Jobs arrive at a buffer from which there are several parallel routes to a destination. A socially optimal policy is one which minimizes the average delay of all jobs, whereas an individually optimal policy is one which, for each job, minimizes its own delay, with route preference given to jobs at the head of the buffer. If there is a socially optimal policy for a system with no arrivals, which can be implemented by each job following a policy γ in such a way that no job ever utilizes a previously declined route, then we show that such a γ is an individually optimal policy for each job. Moreove

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6

Kumar, P. R., and J. Walrand. "Individually optimal routing in parallel systems." Journal of Applied Probability 22, no. 04 (1985): 989–95. http://dx.doi.org/10.1017/s0021900200108265.

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Jobs arrive at a buffer from which there are several parallel routes to a destination. A socially optimal policy is one which minimizes the average delay of all jobs, whereas an individually optimal policy is one which, for each job, minimizes its own delay, with route preference given to jobs at the head of the buffer. If there is a socially optimal policy for a system with no arrivals, which can be implemented by each job following a policy γ in such a way that no job ever utilizes a previously declined route, then we show that such a γ is an individually optimal policy for each job. Moreove

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7

Zheng, Feifeng, Ming Liu, Chengbin Chu, and Yinfeng Xu. "Online Parallel Machine Scheduling to Maximize the Number of Early Jobs." Mathematical Problems in Engineering 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/939717.

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We study a maximization problem: online scheduling onmidentical machines to maximize the number of early jobs. The problem is online in the sense that all jobs arrive over time. Each job's characteristics, such as processing time and due date, become known at its arrival time. We consider thepreemption-restart model, in which preemption is allowed, while once a job is restarted, it loses all the progress that has been made on this job so far. If in some schedule a job is completed before or at its due date, then it is calledearly(oron time). The objective is to maximize the number of early job

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8

Shim, Sang Oh, and Seong Woo Choi. "Scheduling Jobs on Dedicated Parallel Machines." Applied Mechanics and Materials 433-435 (October 2013): 2363–66. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.2363.

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This paper considers scheduling problem on dedicated parallel machines where several types of machines are grouped into one process. The dedicated machine is that a job with a specific recipe should be processed on the dedicated machine even though the job can be produced on any other machine originally. In this process, a setup is required when different jobs are done consecutively. To minimize the completion time of the last job, a scheduling method is developed. Computational experiments are performed on a number of test problems and results show that the suggested algorithm give good solut

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9

Liu, Ming, Feifeng Zheng, Zhanguo Zhu, and Chengbin Chu. "Optimal Semi-Online Algorithm for Scheduling on Two Parallel Batch Processing Machines." Asia-Pacific Journal of Operational Research 31, no. 05 (2014): 1450038. http://dx.doi.org/10.1142/s0217595914500389.

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Batch processing machine scheduling in uncertain environment attracts more and more attention in the last decade. This paper deals with semi-online scheduling on two parallel batch processing machines with non-decreasing processing time of job. Jobs arrive over time in the online paradigm, and the processing time of any batch is equal to the length of the last arrival job in the batch. We study the unbounded model where each processing batch may contain an unlimited number of jobs, and the objective is to minimize the makespan. Given any job Jj together with its following job Jj+1, it is assum

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10

Luo, Cheng Xin. "An FPTAS for Uniform Parallel-Machine Scheduling Problem with Deteriorating Jobs and Rejection." Applied Mechanics and Materials 433-435 (October 2013): 2335–38. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.2335.

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This paper studies uniform parallel-machine scheduling problem with deteriorating jobs and rejection. The processing time of each job is a linear nondecreasing function of its starting time. A job can be rejected by paying a penalty cost. The objective is to minimize the sum of the total load of the accepted jobs on all machines and the total rejection penalties of the rejected jobs. We propose a fully polynomial-time approximation scheme (FPTAS) for this problem.

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