321526

Elastic and transparent scaling for stream processing applications

 Coordinatore  

 Organization address address: ESKISEHIR YOLU 8 KM
city: ANKARA
postcode: TR-06800

contact info
Titolo: Prof.
Nome: Cevdet
Cognome: Aykanat
Email: send email
Telefono: 903123000000

 Nazionalità Coordinatore Non specificata
 Totale costo 100˙000 €
 EC contributo 100˙000 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-
 Anno di inizio 2013
 Periodo (anno-mese-giorno) 2013-05-01   -   2017-04-30

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    Nome Ente NON disponibile

 Organization address address: ESKISEHIR YOLU 8 KM
city: ANKARA
postcode: TR-06800

contact info
Titolo: Prof.
Nome: Cevdet
Cognome: Aykanat
Email: send email
Telefono: 903123000000

TR (ANKARA) coordinator 100˙000.00

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

developers    data    multiple    techniques    dynamically    nature    scaling    elastic    live    volume    feeds    stream    resource    parallelism    specify    parallelize    sources    workload    safe    transparent   

 Obiettivo del progetto (Objective)

'Stream processing applications process high-volume, continuous feeds from live data sources, employ data-in-motion analytics to analyze these feeds, and produce near real-time results with low latency. With the explosion in the amount of data available as live feeds, stream computing has found wide applications in areas ranging from telecommunications to health-care to cyber-security. The high volume of the data to be processed, the velocity at which the results need to be produced, and the variety of the data sources involved make stream processing applications unique and challenging. One of the major limitations of existing stream processing middleware is their inability to take advantage of parallelism opportunities that exist in stream processing applications, in order to scale in the presence of additional resources and workload. This often requires manual intervention by the application developers to specify what is 'safe' to parallelize and/or how much parallelization is 'profitable'. Yet another challenge is to dynamically adapt to the changes in workload and resource availability. We propose system-level techniques that address these challenges via 'transparent' and 'elastic' scaling. The transparent nature of the scaling means that the application developers do not need to specify what parts of the application is safe to parallelize, and instead this information will be derived through automatic analysis.The elastic nature of the scaling means that the level of parallelism is dynamically adjusted as resource and workload availability changes. Furthermore, in the presence of multiple kinds of parallelism as well as multiple instances of application segments that can benefit from these, we propose techniques to effectively manage resources in order to maximize application performance.'

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