Benchmarking and performance modelling of MapReduce communication pattern
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Understanding and predicting the performance of big data applications running in the cloud or on-premises could help minimise the overall cost of operations and provide opportunities in efforts to identify performance bottlenecks. The complexity of the low-level internals of big data frameworks and the ubiquity of application and workload configuration parameters makes it challenging and expensive to come up with comprehensive performance modelling solutions. In this paper, instead of focusing on a wide range of configurable parameters, we studied the low-level internals of the MapReduce communication pattern and used a minimal set of performance drivers to develop a set of phase level parametric models for approximating the execution time of a given application on a given cluster. Models can be used to infer the performance of unseen applications and approximate their performance when an arbitrary dataset is used as input. Our approach is validated by running empirical experiments in two setups. On average, the error rate in both setups is ±10% from the measured values.
Ceesay , S , Barker , A D & Lin , Y 2020 , Benchmarking and performance modelling of MapReduce communication pattern . in J Chen & L T Yang (eds) , Proceedings 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2019) . , 8968864 , IEEE International Conference on Cloud Computing Technology and Science , IEEE Computer Society , pp. 127-134 , 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) , Sydney , New South Wales , Australia , 11/12/19 . https://doi.org/10.1109/CloudCom.2019.00029conference
Proceedings 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2019)
Copyright © 2019 IEEE. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/CloudCom.2019.00029
DescriptionFunding: UK EPSRC EP/R010528/1 and IsDB
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