DocumentCode
1665540
Title
CEPSim: A Simulator for Cloud-Based Complex Event Processing
Author
Higashino, Wilson A. ; Capretz, Miriam A. M. ; Bittencourt, Luiz F.
Author_Institution
Dept. of Electr. & Comput. Eng., Western Univ., London, ON, Canada
fYear
2015
Firstpage
182
Lastpage
190
Abstract
As one of the Vs defining Big Data, data velocity brings many new challenges to traditional data processing approaches. The adoption of cloud environments in complex event processing (CEP) systems is a recent architectural style that aims to overcome these challenges. Validating cloud-based CEP systems at the required Big Data scale, however, is often a laborious, error-prone, and expensive task. This article presents CEPSim, a new simulator that has been developed to facilitate this validation process. CEPSim extends CloudSim, an existing cloud simulator, with an application model based on directed acyclic graphs that is used to represent continuous CEP queries. Once defined, the queries can be simulated in different cloud environments under diverse load conditions. Moreover, CEPSim is also customizable with different operator placement and scheduling strategies. These features enable researchers and system architects to experiment with different configurations and strategies and to promote research in this field. Experimental results show that CEPSim can successfully simulate existing cloud-based CEP systems.
Keywords
Big Data; cloud computing; directed graphs; Big Data; CEP queries; CEPSim; CloudSim; application model; cloud environments; cloud simulator; cloud-based CEP systems; cloud-based complex event processing; data processing; data velocity; directed acyclic graphs; load conditions; operator placement; scheduling strategies; validation process; Big data; Cloud computing; Computational modeling; Digital signal processing; Generators; Terminology; Virtual machining; Big Data; cloud computing; complex event processing; simulation;
fLanguage
English
Publisher
ieee
Conference_Titel
Big Data (BigData Congress), 2015 IEEE International Congress on
Conference_Location
New York, NY
Print_ISBN
978-1-4673-7277-0
Type
conf
DOI
10.1109/BigDataCongress.2015.34
Filename
7207218
Link To Document