Notice Board :

Call for Paper
Vol. 6 Issue 3

Submission Start Date:
March 01, 2020

Acceptence Notification Start:
March 20, 2020

Submission End:
March 25, 2020

Final ManuScript Due:
March 31, 2020

Publication Date:
March 31, 2020
                         Notice Board: Call for PaperVol. 6 Issue 3      Submission Start Date: March 01, 2020      Acceptence Notification Start: March 20, 2020      Submission End: March 25, 2020      Final ManuScript Due: March 31, 2020      Publication Date: March 31, 2020



Volume II Issue XII

Author Name
Sarita Bansod
Year Of Publication
2016
Volume and Issue
Volume 2 Issue 12
Abstract
Cloud is an emerging technology that stores the necessary data and electronic form of data is produced in enormous quantity. It is vital to maintain the efficacy of this data and the need of data recovery services is highly essential. Cloud computing is anticipated as the absolute foundation for the creation of IT enterprise and it is an faultless solution to move databases and application software to big data centers where managing data and services is not completely reliable. Our focus will be on the cloud data storage security which is a vital feature when it comes to giving quality services. It should also be noted that cloud environment comprises of extremely dynamic and heterogeneous environment and because of high scale physical data and resources, the failure of data centre nodes is completely normal. Therefore, cloud environment needs effective adaptive management of data replication to handle the indispensable characteristic of the cloud environment. Disaster recovery u
PaperID
IJETAS/DEC/2016/21010

Author Name
Mustafa Raja Khan
Year Of Publication
2016
Volume and Issue
Volume 2 Issue 12
Abstract
The computational grid efficiency depends on the proper allocation of resource and jobs. For the proper allocation of resource and jobs used various scheduling algorithm. the scheduling algorithm follow the principle of static method and failure of job is occurred. In this paper used dynamic resource allocation technique using teacher learning based optimization algorithm. the teacher learning based optimization algorithm increase the capacity of computational grid. For the evaluation of the performance of computational grid used MatLab software and different set of jobs.
PaperID
IJETAS/DEC/2016/21012