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Cpartition: A correlation-based space partitioning for content-based publish/subscribe systems with skewed workload
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Publication Year
2020-02-01
Journal
Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020, pp.377-384
Keyword
Content space partitioningLoad balancePartitioning granularityPublish/subscribe system
Mesh Keyword
Content-basedContent-based publish/subscribe systemsLoad imbalanceLow correlationMessaging systemPub/sub systemsPublish/subscribeSpace partitioning
All Science Classification Codes (ASJC)
Artificial IntelligenceInformation Systems and ManagementControl and Optimization
Abstract
It is difficult to develop a messaging system that processes live content efficiently. Real-world workload is skewed because users of a publish/subscribe (pub/sub) system use only a small portion of the entire contents. For this reason, the distribution of subscriptions within the content space is seriously imbalanced, and events are not processed efficiently. In this study, we propose CPartition, a correlation-based content space partitioning technique for alleviating load imbalance caused by skewed subscription workload in a content-based pub/sub system. This work aims to assign attributes that have low correlation to the same dimension group. By doing so, the balance between the number of subscriptions among brokers can be improved by scattering the linearly distributed subscriptions to many more subspaces. We have implemented the content-based pub/sub system for evaluation. The evaluation demonstrates a load balance comparison between CPartition and existing methods. Under various configurations of partitioning granularity, the experimental results reveal that CPartition outperforms the existing methods on both skewed subscription workload and balanced subscription workload.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36561
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85084357239&origin=inward
DOI
https://doi.org/10.1109/bigcomp48618.2020.00-46
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9050588
Type
Conference
Funding
ACKNOWLEDGMENT This research is supported by C2 integrating and interfacing technologies laboratory of Agency for Defense Development (UD180010ED).
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Oh, Sangyoon Image
Oh, Sangyoon오상윤
Department of Software and Computer Engineering
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