Notice Board :

Call for Paper
Vol. 4 Issue 11

Submission Start Date:
November 01, 2018

Acceptence Notification Start:
November 10, 2018

Submission End:
November 15, 2018

Final MenuScript Due:
November 25, 2018

Publication Date:
November 30, 2018


                         Notice Board: Call for PaperVol. 4 Issue 11      Submission Start Date: November 01, 2018      Acceptence Notification Start: November 10, 2018      Submission End: November 15, 2018      Final MenuScript Due: November 25, 2018      Publication Date: November 30, 2018




Volume III Issue II

Author Name
Pragya Giri, Prof. Abhishek Raghuvanshi
Year Of Publication
2017
Volume and Issue
Volume 3 Issue 2
Abstract
with growth of cloud computing load balancing is important impact on performance. Cloud computing efficiency depends on good load balancer. Many type of situation occur that time cloud partitioning is done by load balancer. Different type of situation needed different type of strategies for public cloud portioning using load balancer.in this paper we work on, partition of public cloud using two type of situation first is load status evaluation and second is cloud division rules. Load status evaluation is measure in number of cloudlets arrives at datacenter and cloud divisions rules are based on cloudlet come from which geographical location. On the basis of geographical location we partition public cloud and improve performance of load balancing in cloud computing. We implement proposed system with help of cloudsim3.0 simulator.
PaperID
2017/IJRRETAS/3/2017/19610

Author Name
Neha Chouhan, Dr. Pankaj Dashore
Year Of Publication
2017
Volume and Issue
Volume 3 Issue 2
Abstract
As a result of the rapid development in cloud computing, it's fundamental to investigate the performance of extraordinary Hadoop MapReduce purposes and to realize the performance bottleneck in a cloud cluster that contributes to higher or diminish performance. It is usually primary to research the underlying hardware in cloud cluster servers to permit the optimization of program and hardware to achieve the highest performance feasible. Hadoop is founded on MapReduce, which is among the most popular programming items for huge knowledge analysis in a parallel computing environment. In this paper, we reward a particular efficiency analysis, characterization, and evaluation of Hadoop MapReduce WordCount utility.
PaperID
2017/IJRRETAS/3/2017/19611

Author Name
Monika Labana, Mohit Jain
Year Of Publication
2017
Volume and Issue
Volume 3 Issue 2
Abstract
Security is very essential in both wired and wireless network communication. Network security is an important criterion for wired and wireless communication. The advancement in wireless technologies and the high availability of wireless equipment in everyday devices is a factor in the success of infrastructure-less networks. MANETs are becoming more and more common due to their ease of deployment. The high availability of such networks and the lack in security measures of their routing protocols are alluring a number of attackers to interrupt. An ad hoc network is a collection of number of wireless computers having dynamically changing topology due to which the security issues are more in case of wireless networks. In recent year with the widespread use of mobile device, Mobile Ad hoc networks (MANETs) technology has been attracted attention day by day. In this paper, our aims to propose a trust based model for defending network from the severe types of network attack i.e. black-hole a
PaperID
2017/IJRRETAS/2/2017/17610

Author Name
Purva Upadhyay, Dr. Rekha Rathore
Year Of Publication
2017
Volume and Issue
Volume 3 Issue 2
Abstract
Distributed computing and Big data classification are these days about ubiquitous. Authors propose technique of distributed data mining by merge restricted analytical models (build in similar in nodes of a circulated computer system) into a comprehensive one without requirement to build disseminated version of data mining algorithm. In this research, to propose an associative multi level based data clustering with multi-dimensional data. employed to multi level based data clustering process in this research. as well, genetic algorithm is used to find optimal clustering results. To assess the proposed algorithm on two real-worlds multi-dimensional data provide by Machine Learning Repository. To focus on resourceful implementation of proposed Associative multi level Based Optimal Clustering algorithm.
PaperID
2017/IJRRETAS/3/2017/19614