Abstract—Cloud computing is one of the promising and
successful technology in this technological era and because of the
limitation of remote sensing the concept called Cloud Aided
Remote Sensing Multiprocessing System (CARMS) came into
existence. It is the combination of technologies called Remote
sensing and Cloud Computing, which makes Internet of
everything enabler possible (ubiquitous computing). In CARMS
scenario, system can have different types of tasks or requests
and some maybe requested at the same instance of time. In such
cases, it is important that the system should serve maximum
possible tasks gaining profit to the sensing services at clouds and
providing user satisfaction at the same time. Hence, for the
sensing services of sensors in cloud, an optimal scheduling or
task allocation scheme has to be developed so that multiple
requests or tasks may get response and therefore, these tasks can
be scheduled, processed and handled properly without any delay.
Thus, the proposed work provides an algorithm that allows
efficient task allocation which aims at providing efficient
distribution of tasks to the Virtual Machines in the cloud.
Index Terms—Remote sensing (RS), cloud computing (CC),
internet of everything enabler (IOE), cloud aided remote sensing
multiprocessing system (CARMS), distributive sensor cloud
system (DSCS).
M. Zubair Khan is with Taibah University, P.O. Box 344, Madinah,
Saudi Arabia (e-mail: mkhanb@taibahu.edu.sa).
Geeta Devi is with Invertis University Bareilly, India (e-mail:
geetar01@gmail.com).
Yasser M. Alginahi is with Deanship of Academic Services, Taibah
University, P.O. Box. 344, Madinah, Saudi Arabia (e-mail:
yginahi@taibahu.edu.sa).
[PDF]
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.
