A case for understanding end-to-end performance of topic detection and tracking based big data applications in the cloud

Wang, M; Ranjan, R; Jayaraman, PP; Strazdins, P; Burnap, P; Rana, O; Georgakopulos, D

doi:10.1007/978-3-319-47063-4_33

A case for understanding end-to-end performance of topic detection and tracking based big data applications in the cloud

Lookup NU author(s): Professor Raj Ranjan

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Abstract

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016. 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.