doi: 10.18178/ijfcc.2016.5.1.442
Study and Analysis of Cloud Aided Remote Sensing Multiprocessing System (CARMS) with Task Allocation
M. Zubair khan
1
,
Geeta Devi
2
,
Yasser M. Alginahi
1
- 1 Taibah University, P.O. Box 344, Madinah, Saudi Arabia.
- 2 Invertis University Bareilly, India.
Abstract
Cloud computing is one of the promising andsuccessful technology in this technological era and because of thelimitation of remote sensing the concept called Cloud AidedRemote Sensing Multiprocessing System (CARMS) came intoexistence. It is the combination of technologies called Remotesensing and Cloud Computing, which makes Internet ofeverything enabler possible (ubiquitous computing). In CARMSscenario, system can have different types of tasks or requestsand some maybe requested at the same instance of time. In suchcases, it is important that the system should serve maximumpossible tasks gaining profit to the sensing services at clouds andproviding user satisfaction at the same time. Hence, for thesensing services of sensors in cloud, an optimal scheduling ortask allocation scheme has to be developed so that multiplerequests or tasks may get response and therefore, these tasks canbe scheduled, processed and handled properly without any delay.Thus, the proposed work provides an algorithm that allowsefficient task allocation which aims at providing efficientdistribution of tasks to the Virtual Machines in the cloud.
Keywords
- Remote sensing (RS)
- cloud computing (CC)
- internet of everything enabler (IOE)
- cloud aided remote sensingmultiprocessing system (CARMS)
- distributive sensor cloudsystem (DSCS)
How to Cite
M. Zubair khan, Geeta Devi, and Yasser M. Alginahi, "Study and Analysis of Cloud Aided Remote Sensing Multiprocessing System (CARMS) with Task Allocation," International Journal of Future Computer and Communication, vol. 5, no. 1, pp. 47-52, 2016. https://doi.org/ 10.18178/ijfcc.2016.5.1.442
Copyright & License
Copyright © 2016 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).