NDLI: Towards the Prediction of the Performance and Energy Efficiency of Distributed Data Management Systems

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Towards the Prediction of the Performance and Energy Efficiency of Distributed Data Management Systems

Content Provider	ACM Digital Library
Author	Niemann, Raik
Abstract	The ability to accurately simulate and predict the metrics (e.g. performance and energy consumption) of data management systems offers several benefits. It can save investments in both time and hardware. A prominent example is the resource planning. Given a specific use case, a datacenter operator is able to find the most performant or most energy efficient configuration without performing benchmarks or aquiring the necessary hardware. Another possibility would be to study the effects of architectural changes without having them implemented. In this paper, Queued Petri Nets were used to predict and to study the performance and energy consumption of a distributed data management system like Cassandra. The prediction accuracy was evaluated and compared to actual experimental results. On average, the predicted and experimental results differ only by 8 percent for the performance and 16 percent for the energy efficiency, respectively. In addition to this, the experimental results of the used Cassandra cluster revealed a super-linear behavior for the performance and a sub-linear one for the energy consumption.
Starting Page	23
Ending Page	28
Page Count	6
File Format	PDF
ISBN	9781450341479
DOI	10.1145/2859889.2859891
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-03-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cassandra Prediction Energy efficiency
Content Type	Text
Resource Type	Article

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