Paper Info
Title
A Case for Understanding End-to-End Performance of Topic Detection and Tracking Based Big Data Applications in the Cloud.
Abstract
Big Data is revolutionizing nearly every aspect of our lives ranging from enterprises to consumers, from science to government. On the other hand, cloud computing recently has emerged as the platform that can provide an effective and economical infrastructure for collection and analysis of big data produced by applications such as topic detection and tracking (TDT). The fundamental challenge is how to cost-effectively orchestrate these big data applications such as TDT over existing cloud computing platforms for accomplishing big data analytic tasks while meeting performance Service Level Agreements (SLAs). In this paper a layered performance model for TDT big data analytic applications that take into account big data characteristics, the data and event flow across myriad cloud software and hardware resources. We present some preliminary results of the proposed systems that show its effectiveness as regards to understanding the complex performance dependencies across multiple layers of TDT applications.
Year
DOI
Venue
2015
10.1007/978-3-319-47063-4_33
Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering
Keywords
Field
DocType
Cloud computing,Big data,Hadoop map reduce
Data science,Service level,End-to-end principle,Computer science,Software,Ranging,Performance model,Big data,Government,Cloud computing
Conference
Volume
ISSN
Citations 
169
1867-8211
3
PageRank 
References 
Authors
0.48
14
7
Name
Order
Citations
PageRank
Meisong Wang1221.92
Rajiv Ranjan24747267.72
Prem Prakash Jayaraman337844.66
Peter E. Strazdins411017.83
peter burnap528437.02
Omer F. Rana62181229.52
Dimitrios Georgakopoulos72554580.54