Titlebook: Seminar on Stochastic Processes, 1981; E. Çinlar,K. L. Chung,R. K. Getoor Book 1981 Birkhäuser Boston 1981 ALS.Markov process.Martingale.S

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书目名称Seminar on Stochastic Processes, 1981影响因子(影响力)




书目名称Seminar on Stochastic Processes, 1981影响因子(影响力)学科排名




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书目名称Seminar on Stochastic Processes, 1981网络公开度学科排名




书目名称Seminar on Stochastic Processes, 1981被引频次




书目名称Seminar on Stochastic Processes, 1981被引频次学科排名




书目名称Seminar on Stochastic Processes, 1981年度引用




书目名称Seminar on Stochastic Processes, 1981年度引用学科排名




书目名称Seminar on Stochastic Processes, 1981读者反馈




书目名称Seminar on Stochastic Processes, 1981读者反馈学科排名

租约

Two Results on Dual Excursions,g time T. In the first case, one considers the stopping times defined by Maisonneuve [8]. See §3 for a formal description. Roughly speaking, such stopping times correspond to rules where the decision to stop during an excursion may be based only on information available before the excursion began, a

闲荡

Book 1981pers having been already committed to otherpublications. The seminar was made possible through the enlightened support of the Air Force Office of Scientific Research, Grant No. 80-0252. We are grateful to them as well as the publisher, Birkhauser Boston, for their support and encouragement.

AER

ween the components of parallel jobs. Message trapping is carried out at the user-level, eliminating the need for unsupported hardware or device-level programming. A sending process attaches its status to outgoing messages so local schedulers on remote nodes can make more intelligent scheduling deci

菊花

K. L. Chung,K. M. Raooss the intercluster connections. In addition to a synthetic workload, we also consider a workload derived from measurements of a real application on the DAS. We find that the communication speed difference has a severe impact on response times, that a relatively small amount of capacity is lost due

obstinate

Concerto

wangle

J. W. Pitman novel user-aware model and a metric that can overcome the presented issues by evaluating the quality on a user level. The model assigns an expected end time (.) to each job based on a fair partitioning of the system resources, modeling users expectations. Using this calculated . we can then compare

Emmenagogue

A. O. Pittengerfly describe scheduling objectives from the viewpoint of a data center offering IaaS. After presenting our algorithmic concept, we provide an evaluation with theoretical means to demonstrate that this approach can also yield meaningful results in practice.

Flinch