ABYSS
发表于 2025-3-25 03:36:52
A Deterministic Model to Predict Execution Time of Spark Applicationsrunning a big data query (Query-64 of TPC-DS benchmark) that involves parallel execution of a large number of stages. The query is executed on the spark cluster of Google Cloud. Our model resulted in an execution time that is at 2.85% error in comparison to the measured execution time.
微尘
发表于 2025-3-25 10:38:30
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Cervical-Spine
发表于 2025-3-25 15:03:03
https://doi.org/10.1007/978-3-031-25049-1artificial intelligence; computer hardware; computer networks; computer programming; computer science; co
的阐明
发表于 2025-3-25 16:18:23
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ARK
发表于 2025-3-25 20:43:15
https://doi.org/10.1007/978-3-642-99389-3 failure. Streaming systems must be robust in the face of such uncertainty in order to be deemed fit for purpose. Measuring and quantifying a system’s level of robustness is a non-trivial task. We present, compare and contrast a range of non-parametric goodness-of-fit tests which can act as quantifi
faction
发表于 2025-3-26 03:55:04
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Schlemms-Canal
发表于 2025-3-26 06:21:57
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Devastate
发表于 2025-3-26 11:41:30
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混合,搀杂
发表于 2025-3-26 14:30:42
https://doi.org/10.1007/978-3-531-90887-8rgy). We derive and show features of the expected waiting times of customers at every system state. While the system can be modeled using only two variables, it is necessary to adopt one more dimension to derive the expected waiting time at a specific state. Furthermore, we propose a loop algorithm
Fibroid
发表于 2025-3-26 20:20:24
https://doi.org/10.1007/978-3-531-90887-8 protect against possibly lengthy recovery periods is in operation. The problem of how to choose a checkpointing interval in order to optimize performance is addressed by analysing a general queueing model which includes breakdowns, repairs, back-ups and recoveries. Exact solutions are obtained unde