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demonstration

Demo: measuring and estimating monetary cost for cloud-based data stream processing

Published: 29 June 2013 Publication History

Abstract

In recent time due to the availability of cloud-based data streaming systems like Yahoo! S4 or Twitter Storm and virtually unlimited resources using a public cloud infrastructure it is possible to run stream processing tasks with a new dimension of computational complexity. However, the required resources in terms of CPU, memory, and network bandwidth differ depending on the use case and applied data streaming system. For the user of such a system this is directly visible in the monetary cost he has to spent for the used resources. Therefore, he would like to maximize the ratio between gained performance and his monetary cost.
In our demonstration we present an approach to measure and estimate the monetary cost for data streaming systems. We present a general scheme to model monetary cost for any combination of a cloud-based data streaming system and a major public cloud provider. Our model can be used as a starting point for optimizing the ratio between monetary cost and performance of streaming systems in general.

References

[1]
Twitter Storm: http://storm-project.net/.
[2]
M. Dayarathna, S. Takeno, and T. Suzumura. A performance study on operator-based stream processing systems. In Workload Characterization (IISWC), 2011 IEEE International Symposium on, pages 79--79, 2011.
[3]
D. Florescu and D. Kossmann. Rethinking cost and performance of database systems. ACM SIGMOD Record, 38(1):43--48, 2009.
[4]
A. Ishii and T. Suzumura. Elastic stream computing with clouds. In Cloud Computing (CLOUD), 2011 IEEE International Conference on, pages 195--202, 2011.
[5]
D. Kossmann, T. Kraska, and S. Loesing. An evaluation of alternative architectures for transaction processing in the cloud. In Proceedings of the 2010 SIGMOD International Conference on Management of Data, pages 579--590, 2010.
[6]
L. Neumeyer, B. Robbins, A. Nair, and A. Kesari. S4: Distributed stream computing platform. In Data Mining Workshops (ICDMW), 2010 IEEE International Conference on, pages 170--177, 2010.
[7]
S. D. Viglas and J. F. Naughton. Rate-based query optimization for streaming information sources. In Proceedings of the 2002 ACM SIGMOD International Conference on Management of data, pages 37--48, 2002.

Cited By

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  • (2018)Impact-Minimizing Runtime Adaptation in Cloud-Based Data Stream ProcessingAdvances in Service-Oriented and Cloud Computing10.1007/978-3-319-72125-5_22(274-285)Online publication date: 31-Jan-2018
  • (2017)From Resource Monitoring to Requirements-based AdaptationProceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion10.1145/3053600.3053617(91-96)Online publication date: 18-Apr-2017
  • (2014)Cost-Effective Resource Allocation for Deploying Pub/Sub on CloudProceedings of the 2014 IEEE 34th International Conference on Distributed Computing Systems10.1109/ICDCS.2014.63(555-566)Online publication date: 30-Jun-2014

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cover image ACM Conferences
DEBS '13: Proceedings of the 7th ACM international conference on Distributed event-based systems
June 2013
360 pages
ISBN:9781450317580
DOI:10.1145/2488222
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2013

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Author Tags

  1. cloud-based data stream processing
  2. monetary cost

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  • Demonstration

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DEBS '13

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DEBS '13 Paper Acceptance Rate 16 of 58 submissions, 28%;
Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

View all
  • (2018)Impact-Minimizing Runtime Adaptation in Cloud-Based Data Stream ProcessingAdvances in Service-Oriented and Cloud Computing10.1007/978-3-319-72125-5_22(274-285)Online publication date: 31-Jan-2018
  • (2017)From Resource Monitoring to Requirements-based AdaptationProceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion10.1145/3053600.3053617(91-96)Online publication date: 18-Apr-2017
  • (2014)Cost-Effective Resource Allocation for Deploying Pub/Sub on CloudProceedings of the 2014 IEEE 34th International Conference on Distributed Computing Systems10.1109/ICDCS.2014.63(555-566)Online publication date: 30-Jun-2014

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