A World Wide Web (WWW) Server is normally a single machine dedicated to process a HTTP request for a single WWW site. The number of people using the Internet has been growing at a very fast rate, while the services provided over the Internet are increasingly becoming mission critical. Hence, enabling high performance, reliability, and availability, as well as the creation of management tools, have become key issues in the development and maintenance of Internet servers. Servers based on clusters of workstations or PCs are the most popular hardware platform used to meet the growth of traffic demands in World Wide Web. A cluster based network server consists of a front-end responsible for request distribution and a number of back end nodes responsible for request processing. In content-based request distribution Front-end takes into account both the service, and content requested with current load on back-end nodes. The current approach for handling these issues from the server perspective is based on the concept of load balancing. Locality-aware request distribution (LARD) is a specific strategy for content aware request distribution that improves cluster performance by simultaneously achieving load balancing and high cache hit rates in the back ends.

Keywords:  Data Clustering, Server Load Balancing, Cache Hit Rate.

Cloud Computing is an emerging technology of sharing computational resources through a network may be LAN, MAN, WAN or the Internet. The resources which can share on Cloud are Software, Platform, Data Storage & Infrastructure. There are two parties involved in this technology – (i) Cloud Service Provider (CSP) (ii) Cloud Consumer (CC). CSP develops necessary infrastructure to facilitate the service and CC uses the services through network connection. Cloud Computing makes outsourcing of computing environment for an individual or an enterprise so that they can avoid committing large capital outlays when purchasing & managing software and hardware as well as dealing with the operational overhead therein. Although cloud computing’s benefits are tremendous, data security and protection is one of the major concern in Cloud Computing. Both the parties – CSP and CC need to understand the possible loopholes and need to strictly follow the recommended do’s and don'ts to protect data and make is secure. In this paper, we present various do’s and don’ts frame-worked by different leading organizations to become the cloud computing environment fully secure and trustworthy.

Index Terms: Cloud Computing, Data Encryption, Data at Rest, Data in motion

The existing image retrieval which classifies based on the click through logs may play a vital role for effective search results. The user satisfaction can be obtained through the user click sequence. From the click sequence, the feedback sessions are calculated and produce effective search results. Thus the results show only the related results, but the users need the related and relevant data results. For this, the novel algorithm may propose to obtain the effective relevant and related search results based on both the click through logs and personalized web search. After that the privacy protection has been implemented to avoid the data leakage when the system use protected details for searching. Our runtime generalization aims at striking a balance between two predictive metrics that evaluate the utility of personalization and the privacy risk of exposing the generalized profile. The new system presents two greedy algorithms, namely Greedy and GreedyIL for runtime generalization. The system proposes a Cluster-Based SVM (CB-SVM) classifier method to overcome the problems obtained with the SVM Classifier. We also provide an online prediction mechanism for deciding whether personalizing a query is beneficial. The experimental results show the effectiveness of the novel algorithm.

Key Terms: Image Retrieval, Text Based Image Retrieval, Click through logs, Cluster Based SVM Classifier, Personalized Web Search, Data Leakage