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Abstract
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.
Original language | English |
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Title of host publication | Proceedings 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2019) |
Editors | Jinjun Chen, Laurence T. Yang |
Publisher | IEEE Computer Society |
Pages | 127-134 |
Number of pages | 8 |
ISBN (Electronic) | 9781728150116 |
ISBN (Print) | 9781728150123 |
DOIs | |
Publication status | Published - 27 Jan 2020 |
Event | 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) - Novotel Sydney Central, Sydney, Australia Duration: 11 Dec 2019 → 13 Feb 2020 Conference number: 11 http://2019.cloudcom.org/ |
Publication series
Name | IEEE International Conference on Cloud Computing Technology and Science |
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Publisher | IEEE |
ISSN (Electronic) | 2330-2186 |
Conference
Conference | 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) |
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Abbreviated title | CloudCom |
Country/Territory | Australia |
City | Sydney |
Period | 11/12/19 → 13/02/20 |
Internet address |
Keywords
- Communication Pattern
- Big Data
- MapReduce
- Modelling
Fingerprint
Dive into the research topics of 'Benchmarking and performance modelling of MapReduce communication pattern'. Together they form a unique fingerprint.Projects
- 1 Finished
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ABC: Adaptive Brokerage for the Cloud: ABC: Adaptive Brokerage for the Cloud
Barker, A. D. (PI) & Thomson, J. D. (CoI)
1/04/18 → 30/09/22
Project: Standard