FSBD: A framework for scheduling of big data mining in cloud computing

Leila Ismail; Mohammad M. Masud; Latifur Khan

doi:10.1109/BigData.Congress.2014.81

FSBD: A framework for scheduling of big data mining in cloud computing

Leila Ismail, Mohammad M. Masud, Latifur Khan

Department of Information System and Security

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Citations (Scopus)

Abstract

Cloud computing is seen as an emerging technology for big data mining and analytics. Cloud computing can provide data mining results in the form of a Software As a Service (SAS). Both performance and quality of mining are fundamentals criteria for the use of a data mining application provided by a Cloud computing environment. In this paper, we propose a Cloud computing framework, which is responsible to distribute and schedule a Cluster-Based data mining application and its data set. The main goal of our proposed framework for scheduling of Big Data Mining (FSBD) is to decrease the overall execution time of the application with minimum loss in mining quality. We consider the Cluster-based data mining technique as a pilot application for our framework. The results show an important speedup with a minimum loss in quality of mining. We obtained a ratio of 2 of the normalized actual makespan vis-a-vis the ideal makespan. The quality of mining scales well with the number of clusters and the increasing size of the dataset. The results are promising, encouraging the adoption of the framework by Cloud providers.

Original language	English
Title of host publication	Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014
Editors	Peter Chen, Peter Chen, Hemant Jain
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	514-521
Number of pages	8
ISBN (Electronic)	9781479950577
DOIs	https://doi.org/10.1109/BigData.Congress.2014.81
Publication status	Published - Sept 22 2014
Event	3rd IEEE International Congress on Big Data, BigData Congress 2014 - Anchorage, United States Duration: Jun 27 2014 → Jul 2 2014

Publication series

Name	Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014

Other

Other	3rd IEEE International Congress on Big Data, BigData Congress 2014
Country/Territory	United States
City	Anchorage
Period	6/27/14 → 7/2/14

Keywords

Autonomous Computing
Cloud Computing
Data Mining
Distributed Systems
Divisible Load Application
High Performance Computing
Scheduling

ASJC Scopus subject areas

Computer Science Applications

Access to Document

10.1109/BigData.Congress.2014.81

Cite this

Ismail, L., Masud, M. M., & Khan, L. (2014). FSBD: A framework for scheduling of big data mining in cloud computing. In P. Chen, P. Chen, & H. Jain (Eds.), Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014 (pp. 514-521). Article 6906823 (Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.Congress.2014.81

FSBD: A framework for scheduling of big data mining in cloud computing. / Ismail, Leila ; Masud, Mohammad M.; Khan, Latifur.
Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014. ed. / Peter Chen; Peter Chen; Hemant Jain. Institute of Electrical and Electronics Engineers Inc., 2014. p. 514-521 6906823 (Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ismail, L , Masud, MM & Khan, L 2014, FSBD: A framework for scheduling of big data mining in cloud computing. in P Chen, P Chen & H Jain (eds), Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014., 6906823, Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014, Institute of Electrical and Electronics Engineers Inc., pp. 514-521, 3rd IEEE International Congress on Big Data, BigData Congress 2014, Anchorage, United States, 6/27/14. https://doi.org/10.1109/BigData.Congress.2014.81

Ismail L , Masud MM, Khan L. FSBD: A framework for scheduling of big data mining in cloud computing. In Chen P, Chen P, Jain H, editors, Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 514-521. 6906823. (Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014). doi: 10.1109/BigData.Congress.2014.81

Ismail, Leila ; Masud, Mohammad M. ; Khan, Latifur. / FSBD : A framework for scheduling of big data mining in cloud computing. Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014. editor / Peter Chen ; Peter Chen ; Hemant Jain. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 514-521 (Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014).

@inproceedings{68424c89416c4a229689d7266ee86ea6,

title = "FSBD: A framework for scheduling of big data mining in cloud computing",

abstract = "Cloud computing is seen as an emerging technology for big data mining and analytics. Cloud computing can provide data mining results in the form of a Software As a Service (SAS). Both performance and quality of mining are fundamentals criteria for the use of a data mining application provided by a Cloud computing environment. In this paper, we propose a Cloud computing framework, which is responsible to distribute and schedule a Cluster-Based data mining application and its data set. The main goal of our proposed framework for scheduling of Big Data Mining (FSBD) is to decrease the overall execution time of the application with minimum loss in mining quality. We consider the Cluster-based data mining technique as a pilot application for our framework. The results show an important speedup with a minimum loss in quality of mining. We obtained a ratio of 2 of the normalized actual makespan vis-a-vis the ideal makespan. The quality of mining scales well with the number of clusters and the increasing size of the dataset. The results are promising, encouraging the adoption of the framework by Cloud providers.",

keywords = "Autonomous Computing, Cloud Computing, Data Mining, Distributed Systems, Divisible Load Application, High Performance Computing, Scheduling",

author = "Leila Ismail and Masud, {Mohammad M.} and Latifur Khan",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 3rd IEEE International Congress on Big Data, BigData Congress 2014 ; Conference date: 27-06-2014 Through 02-07-2014",

year = "2014",

month = sep,

day = "22",

doi = "10.1109/BigData.Congress.2014.81",

language = "English",

series = "Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "514--521",

editor = "Peter Chen and Peter Chen and Hemant Jain",

booktitle = "Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014",

}

TY - GEN

T1 - FSBD

T2 - 3rd IEEE International Congress on Big Data, BigData Congress 2014

AU - Ismail, Leila

AU - Masud, Mohammad M.

AU - Khan, Latifur

PY - 2014/9/22

Y1 - 2014/9/22

N2 - Cloud computing is seen as an emerging technology for big data mining and analytics. Cloud computing can provide data mining results in the form of a Software As a Service (SAS). Both performance and quality of mining are fundamentals criteria for the use of a data mining application provided by a Cloud computing environment. In this paper, we propose a Cloud computing framework, which is responsible to distribute and schedule a Cluster-Based data mining application and its data set. The main goal of our proposed framework for scheduling of Big Data Mining (FSBD) is to decrease the overall execution time of the application with minimum loss in mining quality. We consider the Cluster-based data mining technique as a pilot application for our framework. The results show an important speedup with a minimum loss in quality of mining. We obtained a ratio of 2 of the normalized actual makespan vis-a-vis the ideal makespan. The quality of mining scales well with the number of clusters and the increasing size of the dataset. The results are promising, encouraging the adoption of the framework by Cloud providers.

AB - Cloud computing is seen as an emerging technology for big data mining and analytics. Cloud computing can provide data mining results in the form of a Software As a Service (SAS). Both performance and quality of mining are fundamentals criteria for the use of a data mining application provided by a Cloud computing environment. In this paper, we propose a Cloud computing framework, which is responsible to distribute and schedule a Cluster-Based data mining application and its data set. The main goal of our proposed framework for scheduling of Big Data Mining (FSBD) is to decrease the overall execution time of the application with minimum loss in mining quality. We consider the Cluster-based data mining technique as a pilot application for our framework. The results show an important speedup with a minimum loss in quality of mining. We obtained a ratio of 2 of the normalized actual makespan vis-a-vis the ideal makespan. The quality of mining scales well with the number of clusters and the increasing size of the dataset. The results are promising, encouraging the adoption of the framework by Cloud providers.

KW - Autonomous Computing

KW - Cloud Computing

KW - Data Mining

KW - Distributed Systems

KW - Divisible Load Application

KW - High Performance Computing

KW - Scheduling

UR - http://www.scopus.com/inward/record.url?scp=84923916916&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84923916916&partnerID=8YFLogxK

U2 - 10.1109/BigData.Congress.2014.81

DO - 10.1109/BigData.Congress.2014.81

M3 - Conference contribution

AN - SCOPUS:84923916916

T3 - Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014

SP - 514

EP - 521

BT - Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014

A2 - Chen, Peter

A2 - Jain, Hemant

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 27 June 2014 through 2 July 2014

ER -

FSBD: A framework for scheduling of big data mining in cloud computing

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this