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

Meisong Wang; Rajiv Ranjan; Prem Prakash Jayaraman; Peter Strazdins; Pete Burnap; Omer Rana; Dimitrios Georgakopulos

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

Meisong Wang, Rajiv Ranjan^*, Prem Prakash Jayaraman, Peter Strazdins, Pete Burnap, Omer Rana, Dimitrios Georgakopulos

^*Corresponding author for this work

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

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Internet of Things
Subtitle of host publication	IoT Infrastructures - 2nd International Summit, IoT 360° 2015, Revised Selected Papers
Editors	Miguel Elias Mitre Campista, Andrey Somov, Benny Mandler, Hakima Chaouchi, Maria Fazio, Dagmar Caganova, Stefano Giordano, Johann Marquez-Barja, Sherali Zeadally, Mohamad Badra, Radu-Laurentiu Vieriu
Publisher	Springer Verlag
Pages	315-325
Number of pages	11
ISBN (Print)	9783319470627
DOIs	https://doi.org/10.1007/978-3-319-47063-4_33
Publication status	Published - 2016
Event	2nd International Summit on Internet of Things, IoT 360° 2015 - Rome, Italy Duration: 27 Oct 2015 → 29 Oct 2015

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	169
ISSN (Print)	1867-8211

Conference

Conference	2nd International Summit on Internet of Things, IoT 360° 2015
Country/Territory	Italy
City	Rome
Period	27/10/15 → 29/10/15

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

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Wang, M., Ranjan, R., Jayaraman, P. P., Strazdins, P., Burnap, P., Rana, O., & Georgakopulos, D. (2016). A case for understanding end-to-end performance of topic detection and tracking based big data applications in the cloud. In M. E. M. Campista, A. Somov, B. Mandler, H. Chaouchi, M. Fazio, D. Caganova, S. Giordano, J. Marquez-Barja, S. Zeadally, M. Badra, & R.-L. Vieriu (Eds.), Internet of Things: IoT Infrastructures - 2nd International Summit, IoT 360° 2015, Revised Selected Papers (pp. 315-325). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 169). Springer Verlag. https://doi.org/10.1007/978-3-319-47063-4_33

Wang, Meisong ; Ranjan, Rajiv ; Jayaraman, Prem Prakash et al. / A case for understanding end-to-end performance of topic detection and tracking based big data applications in the cloud. Internet of Things: IoT Infrastructures - 2nd International Summit, IoT 360° 2015, Revised Selected Papers. editor / Miguel Elias Mitre Campista ; Andrey Somov ; Benny Mandler ; Hakima Chaouchi ; Maria Fazio ; Dagmar Caganova ; Stefano Giordano ; Johann Marquez-Barja ; Sherali Zeadally ; Mohamad Badra ; Radu-Laurentiu Vieriu. Springer Verlag, 2016. pp. 315-325 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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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.",

keywords = "Big data, Cloud computing, Hadoop map reduce",

author = "Meisong Wang and Rajiv Ranjan and Jayaraman, {Prem Prakash} and Peter Strazdins and Pete Burnap and Omer Rana and Dimitrios Georgakopulos",

note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016.; 2nd International Summit on Internet of Things, IoT 360° 2015 ; Conference date: 27-10-2015 Through 29-10-2015",

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editor = "Campista, {Miguel Elias Mitre} and Andrey Somov and Benny Mandler and Hakima Chaouchi and Maria Fazio and Dagmar Caganova and Stefano Giordano and Johann Marquez-Barja and Sherali Zeadally and Mohamad Badra and Radu-Laurentiu Vieriu",

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Wang, M, Ranjan, R, Jayaraman, PP, Strazdins, P, Burnap, P, Rana, O & Georgakopulos, D 2016, A case for understanding end-to-end performance of topic detection and tracking based big data applications in the cloud. in MEM Campista, A Somov, B Mandler, H Chaouchi, M Fazio, D Caganova, S Giordano, J Marquez-Barja, S Zeadally, M Badra & R-L Vieriu (eds), Internet of Things: IoT Infrastructures - 2nd International Summit, IoT 360° 2015, Revised Selected Papers. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 169, Springer Verlag, pp. 315-325, 2nd International Summit on Internet of Things, IoT 360° 2015, Rome, Italy, 27/10/15. https://doi.org/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. / Wang, Meisong; Ranjan, Rajiv; Jayaraman, Prem Prakash et al.
Internet of Things: IoT Infrastructures - 2nd International Summit, IoT 360° 2015, Revised Selected Papers. ed. / Miguel Elias Mitre Campista; Andrey Somov; Benny Mandler; Hakima Chaouchi; Maria Fazio; Dagmar Caganova; Stefano Giordano; Johann Marquez-Barja; Sherali Zeadally; Mohamad Badra; Radu-Laurentiu Vieriu. Springer Verlag, 2016. p. 315-325 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 169).

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

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T1 - A case for understanding end-to-end performance of topic detection and tracking based big data applications in the cloud

AU - Wang, Meisong

AU - Ranjan, Rajiv

AU - Jayaraman, Prem Prakash

AU - Strazdins, Peter

AU - Burnap, Pete

AU - Rana, Omer

AU - Georgakopulos, Dimitrios

N1 - Publisher Copyright: © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016.

PY - 2016

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N2 - 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.

AB - 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.

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KW - Cloud computing

KW - Hadoop map reduce

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M3 - Conference contribution

SN - 9783319470627

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 315

EP - 325

BT - Internet of Things

A2 - Campista, Miguel Elias Mitre

A2 - Somov, Andrey

A2 - Mandler, Benny

A2 - Chaouchi, Hakima

A2 - Fazio, Maria

A2 - Caganova, Dagmar

A2 - Giordano, Stefano

A2 - Marquez-Barja, Johann

A2 - Zeadally, Sherali

A2 - Badra, Mohamad

A2 - Vieriu, Radu-Laurentiu

PB - Springer Verlag

T2 - 2nd International Summit on Internet of Things, IoT 360° 2015

Y2 - 27 October 2015 through 29 October 2015

ER -

Wang M, Ranjan R, Jayaraman PP, Strazdins P, Burnap P, Rana O et al. A case for understanding end-to-end performance of topic detection and tracking based big data applications in the cloud. In Campista MEM, Somov A, Mandler B, Chaouchi H, Fazio M, Caganova D, Giordano S, Marquez-Barja J, Zeadally S, Badra M, Vieriu RL, editors, Internet of Things: IoT Infrastructures - 2nd International Summit, IoT 360° 2015, Revised Selected Papers. Springer Verlag. 2016. p. 315-325. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). 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

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