Titlebook: ComputerPerformance Engineering; 18th European Worksh Katja Gilly,Nigel Thomas Conference proceedings 2023 The Editor(s) (if applicable) an

ABYSS

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.

微塵

Cervical-Spine

https://doi.org/10.1007/978-3-031-25049-1artificial intelligence; computer hardware; computer networks; computer programming; computer science; co

的闡明

ARK

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

Schlemms-Canal

Devastate

混合,攙雜

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

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