Modeling performances of concurrent big data applications. (5th May 2014)
- Record Type:
- Journal Article
- Title:
- Modeling performances of concurrent big data applications. (5th May 2014)
- Main Title:
- Modeling performances of concurrent big data applications
- Authors:
- Castiglione, Aniello
Gribaudo, Marco
Iacono, Mauro
Palmieri, Francesco
You, Ilsun
Ogiela, Marek R.
Hwang, Myunggwon - Abstract:
- <abstract abstract-type="main" id="spe2269-abs-0001"> <title>Summary</title> <p id="spe2269-para-0001">Big Data applications are characterized by a non‐negligible number of complex parallel transactions on a huge amount of data that continuously varies, generally increasing over time. Because of the amount of needed resources, the ideal runtime scenario for these applications is based on complex cloud computing and storage infrastructures, providing a scalable degree of parallelism together with isolation between different applications and resource abstraction. However, such additional abstraction degree also introduces significant complexity in performance modeling and decision making. Potential concurrency of many applications on the same cloud infrastructure has to be evaluated, and, simultaneously, scalability of applications over time has to be studied through proper modeling practices, in order to predict the system behavior as the usage patterns evolve and the load increases. For this purpose, in this paper, we propose an analytic modeling technique based on the use of Markovian Agents and Mean Field Analysis that allows the effective description of different concurrent Big Data applications on a same, multi‐site cloud infrastructure, accounting for mutual interactions, in order to support the careful evaluation of several elements in terms of real costs/risks/benefits for correctly dimensioning and allocating the resources and verifying the existing service level<abstract abstract-type="main" id="spe2269-abs-0001"> <title>Summary</title> <p id="spe2269-para-0001">Big Data applications are characterized by a non‐negligible number of complex parallel transactions on a huge amount of data that continuously varies, generally increasing over time. Because of the amount of needed resources, the ideal runtime scenario for these applications is based on complex cloud computing and storage infrastructures, providing a scalable degree of parallelism together with isolation between different applications and resource abstraction. However, such additional abstraction degree also introduces significant complexity in performance modeling and decision making. Potential concurrency of many applications on the same cloud infrastructure has to be evaluated, and, simultaneously, scalability of applications over time has to be studied through proper modeling practices, in order to predict the system behavior as the usage patterns evolve and the load increases. For this purpose, in this paper, we propose an analytic modeling technique based on the use of Markovian Agents and Mean Field Analysis that allows the effective description of different concurrent Big Data applications on a same, multi‐site cloud infrastructure, accounting for mutual interactions, in order to support the careful evaluation of several elements in terms of real costs/risks/benefits for correctly dimensioning and allocating the resources and verifying the existing service level agreements. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract> … (more)
- Is Part Of:
- Software, practice & experience. Volume 45:Number 8(2015)
- Journal:
- Software, practice & experience
- Issue:
- Volume 45:Number 8(2015)
- Issue Display:
- Volume 45, Issue 8 (2015)
- Year:
- 2015
- Volume:
- 45
- Issue:
- 8
- Issue Sort Value:
- 2015-0045-0008-0000
- Page Start:
- 1127
- Page End:
- 1144
- Publication Date:
- 2014-05-05
- Subjects:
- Computer software -- Periodicals
Computer programming -- Periodicals
Computer programs -- Periodicals
005.3 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/spe.2269 ↗
- Languages:
- English
- ISSNs:
- 0038-0644
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8321.453000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 3070.xml