Predicting hot-spots in distributed cloud databases using association rule mining

Joarder Mohammad Mustafa Kamal; Manzur Murshed; Mohamed Medhat Gaber

doi:10.1109/UCC.2014.130

Predicting hot-spots in distributed cloud databases using association rule mining

Joarder Mohammad Mustafa Kamal, Manzur Murshed, Mohamed Medhat Gaber

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

1 Citation (Scopus)

Abstract

Data partitioning is a popular technique to horizontally or vertically split table attributes of a Cloud database cluster to evenly distribute increasing workloads. However, hot-spots can be created due to inappropriate partitioning scheme and static partition management without considering the dynamic workload characteristics. In this paper, an automatic database partition management scheme - APM - is proposed which periodically analyses workload logs to predict the formation of any potential hot-spot using association rule mining. A detailed illustration of the proposed scheme is presented with examples along with a cost model following by experimental observations from running a HBase cluster with YCSB workloads in AWS.

Original language	English
Title of host publication	Proceedings - 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014
Place of Publication	Danvers MA USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	800-805
Number of pages	6
ISBN (Electronic)	9781479978816
DOIs	https://doi.org/10.1109/UCC.2014.130
Publication status	Published - 29 Jan 2014
Event	IEEE/ACM International Conference on Utility and Cloud Computing 2014 - London, United Kingdom Duration: 8 Dec 2014 → 11 Dec 2014 Conference number: 7th https://ieeexplore.ieee.org/xpl/conhome/7027114/proceeding (Proceedings)

Conference

Conference	IEEE/ACM International Conference on Utility and Cloud Computing 2014
Abbreviated title	UCC 2014
Country/Territory	United Kingdom
City	London
Period	8/12/14 → 11/12/14
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7027114/proceeding (Proceedings)

Keywords

Association rule mining
Distributed database
Hot-spots
Partitioning
Workload

Access to Document

10.1109/UCC.2014.130

Cite this

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title = "Predicting hot-spots in distributed cloud databases using association rule mining",

abstract = "Data partitioning is a popular technique to horizontally or vertically split table attributes of a Cloud database cluster to evenly distribute increasing workloads. However, hot-spots can be created due to inappropriate partitioning scheme and static partition management without considering the dynamic workload characteristics. In this paper, an automatic database partition management scheme - APM - is proposed which periodically analyses workload logs to predict the formation of any potential hot-spot using association rule mining. A detailed illustration of the proposed scheme is presented with examples along with a cost model following by experimental observations from running a HBase cluster with YCSB workloads in AWS.",

keywords = "Association rule mining, Distributed database, Hot-spots, Partitioning, Workload",

author = "Kamal, \{Joarder Mohammad Mustafa\} and Manzur Murshed and Gaber, \{Mohamed Medhat\}",

year = "2014",

month = jan,

day = "29",

doi = "10.1109/UCC.2014.130",

language = "English",

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booktitle = "Proceedings - 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE/ACM International Conference on Utility and Cloud Computing 2014, UCC 2014 ; Conference date: 08-12-2014 Through 11-12-2014",

url = "https://ieeexplore.ieee.org/xpl/conhome/7027114/proceeding",

}

Kamal, JMM, Murshed, M & Gaber, MM 2014, Predicting hot-spots in distributed cloud databases using association rule mining. in Proceedings - 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014., 7027597, IEEE, Institute of Electrical and Electronics Engineers, Danvers MA USA, pp. 800-805, IEEE/ACM International Conference on Utility and Cloud Computing 2014, London, United Kingdom, 8/12/14. https://doi.org/10.1109/UCC.2014.130

Predicting hot-spots in distributed cloud databases using association rule mining. / Kamal, Joarder Mohammad Mustafa; Murshed, Manzur; Gaber, Mohamed Medhat.
Proceedings - 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014. Danvers MA USA: IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 800-805 7027597.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Gaber, Mohamed Medhat

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PY - 2014/1/29

Y1 - 2014/1/29

N2 - Data partitioning is a popular technique to horizontally or vertically split table attributes of a Cloud database cluster to evenly distribute increasing workloads. However, hot-spots can be created due to inappropriate partitioning scheme and static partition management without considering the dynamic workload characteristics. In this paper, an automatic database partition management scheme - APM - is proposed which periodically analyses workload logs to predict the formation of any potential hot-spot using association rule mining. A detailed illustration of the proposed scheme is presented with examples along with a cost model following by experimental observations from running a HBase cluster with YCSB workloads in AWS.

AB - Data partitioning is a popular technique to horizontally or vertically split table attributes of a Cloud database cluster to evenly distribute increasing workloads. However, hot-spots can be created due to inappropriate partitioning scheme and static partition management without considering the dynamic workload characteristics. In this paper, an automatic database partition management scheme - APM - is proposed which periodically analyses workload logs to predict the formation of any potential hot-spot using association rule mining. A detailed illustration of the proposed scheme is presented with examples along with a cost model following by experimental observations from running a HBase cluster with YCSB workloads in AWS.

KW - Association rule mining

KW - Distributed database

KW - Hot-spots

KW - Partitioning

KW - Workload

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DO - 10.1109/UCC.2014.130

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BT - Proceedings - 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014

PB - IEEE, Institute of Electrical and Electronics Engineers

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T2 - IEEE/ACM International Conference on Utility and Cloud Computing 2014

Y2 - 8 December 2014 through 11 December 2014

ER -

Predicting hot-spots in distributed cloud databases using association rule mining

Abstract

Conference

Keywords

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