Study and Analysis of Cloud Aided Remote Sensing Multiprocessing System (CARMS) with Task Allocation

Abstract

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).

A Dynamic Task Allocation Algorithm Based on Weighted Velocity

“…Volunteer computing is computing model with the features of low-cost, simple to build, high performance. Task allocation as an important part of volunteer computing has high research value (Khan et al, 2016). 8, No.8, 2017…”