Notice Board :

Call for Paper
Vol. 5 Issue 7

Submission Start Date:
July 01, 2019

Acceptence Notification Start:
July 10, 2019

Submission End:
July 15, 2019

Final MenuScript Due:
July 25, 2019

Publication Date:
July 31, 2019


                         Notice Board: Call for PaperVol. 5 Issue 7      Submission Start Date: July 01, 2019      Acceptence Notification Start: July 10, 2019      Submission End: July 15, 2019      Final MenuScript Due: July 25, 2019      Publication Date: July 31, 2019




Volume V Issue II

Author Name
Madhuri Kaushal, Mohit Jain
Year Of Publication
2019
Volume and Issue
Volume 5 Issue 2
Abstract
Cloud computing is an interesting era of research, where motivation is to find out the best outcome and productive data security and sharing approach. Load balancing in public impair by way of division of cloud just right geographical position. Load balancing is frequently a strategy of controlling the visitors in a cloud atmosphere. Cloud requests hunt for assets for performance. The resources are quite often storage, processing, bandwidth, and many others. Allocation these belongings efficaciously to the entire competing jobs are named as load balancing. This paper will provide a comprehensive survey of cloud load balancing techniques. This paper also proposes an updated load balancing technique. This proposed technique uses the concept of hash map for effective allocation of virtual machines to user requests.
PaperID
2019/IJRRETAS/2/2019/38679

Author Name
Alok Aamle, Mohit Jain
Year Of Publication
2019
Volume and Issue
Volume 5 Issue 2
Abstract
The destination image branding is the domain of tourism industry where the facts and information is collected and evaluated for finding the credibility of a target tourist destination. Manual collection and processing of collected information accurately is a complicated and time consuming task therefore a data mining model is suggested in this presented work that collect and evaluate the destination image accurately and based on evaluation can make the recommendations about visits of tourist. In order to perform this task data mining techniques are applied on text data source. In first the data is extracted from the Google search engine and it is preprocessed for make it impure.
PaperID
2019/IJRRETAS/2/2019/38680