Cart (Loading....) | Create Account
Close category search window
 

End-to-End QoS on Shared Clouds for Highly Dynamic, Large-Scale Sensing Data Streams

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Tolosana-Calasanz, R. ; Aragon Inst. of Eng. Res. (I3A), Univ. of Zaragoza, Zaragoza, Spain ; Ángel Bañares, J. ; Pham, C. ; Rana, O.

The increasing deployment of sensor network infrastructures has led to large volumes of data becoming available, leading to new challenges in storing, processing and transmitting such data. This is especially true when data from multiple sensors is pre-processed prior to delivery to users. Where such data is processed in-transit (i.e. from data capture to delivery to a user) over a shared distributed computing infrastructure, it is necessary to provide some Quality of Service (QoS) guarantees to each user. We propose an architecture for supporting QoS for multiple concurrent scientific workflow data streams being processed (prior to delivery to a user) over a shared infrastructure. We consider such an infrastructure to be composed of a number of nodes, each of which has multiple processing units and data buffers. We utilize the ``token bucket" model for regulating, on a per workflow stream basis, the data injection rate into such a node. We subsequently demonstrate how a streaming pipeline, with intermediate data size variation (inflation/deflation), can be supported and managed using a dynamic control strategy at each node. Such a strategy supports end-to-end QoS with variations in data size between the various nodes involved in the workflow enactment process.

Published in:

Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on

Date of Conference:

13-16 May 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.