A computational model to support in-network data analysis in federated ecosystems

Zamani, Ali Reza, Zou, Mengsong, Diaz-Montes, Javier, Petri, Ioan, Rana, Omer and Parashar, Manish 2018. A computational model to support in-network data analysis in federated ecosystems. Future Generation Computer Systems 80 , pp. 342-354. 10.1016/j.future.2017.05.032

Preview

PDF - Accepted Post-Print Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview

Abstract

Software-defined networks (SDNs) have proven to be an efficacious tool for undertaking complex data analysis and manipulation within data intensive applications. SDN technology allows us to separate the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage software-defined networking (SDN) to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. This strategy allows us to minimize waiting times at the destination data center and to cope with spikes in demand for computational capability. We validate our approach using a smart building application in a multi-cloud infrastructure. Results show how the in-transit processing strategy increases the computational capabilities of the infrastructure and influences the percentage of job completion without significantly impacting costs and overheads.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	Software-defined networks; In-transit; Smart buildings; Cloud federation; CometCloud
Publisher:	Elsevier
ISSN:	0167-739X
Date of First Compliant Deposit:	13 June 2017
Date of Acceptance:	21 May 2017
Last Modified:	11 Jan 2021 11:00
URI:	http://orca.cf.ac.uk/id/eprint/101393

Citation Data

Cited 7 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item