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As web contents grow, the importance of search engines become more critical and at the same time user satisfaction decreases. Query recommendation is a new approach to improve search results in web. In this paper a method is proposed that, given a query submitted to a search engine, suggests a list of queries that are related to the user input query. The related queries are based on previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The proposed method is based on clustering processes in which groups of semantically similar queries are detected. The clustering process uses the content of historical preferences of users registered in the query log of the search engine. This facility provides queries that are related to the ones submitted by users in order to direct them toward their required information. This method not only discovers the related queries but also ranks them according to a similarity measure. The method has been evaluated using real data sets from the search engine query log.

Cloud computing has its characteristics along with so me important issues that should be handled to improve the performance and increase the efficiency of the cloud platform. These issues are related to resources management, fault tolerance, and security. The purpose of this research is to handle the resource management problem, wh ich is to allocate and schedule virtual mach ines of cloud computing in a way that help providers to reduce makespan time of tasks. In this paper, a hybrid algorith m for dynamic tasks scheduling over cloud's virtual machines is introduced. This hybrid algorith m me rges the behaviors of three effective techniques from the swarm intelligence techniques that are used to find a near optimal solution to d ifficu lt co mb inatorial problems. It exp loits the advantages of ant colony behavior, the behavior of particle swarm and honeybee foraging behavior. Experimental results reinforce the strength of the proposed hybrid algorith m. They also prove that the proposed hybrid algorith m is the best and outperformed ant colony optimization, particle swarm optimization , artificial bee colony and other known algorithms.

Nowadays, Cloud computing is an expanding area in research and industry, which involves virtualization, distributed computing, internet, software, security, web services and etc. A cloud consists of several elements such as clients, data centers and distributed servers, internet and it includes fault tolerance, high availability, effectiveness, scalability, flexibility, reduced overhead for users, reduced cost of ownership, on demand services and etc. Now the next factor is coming, cost of Virtual machines on Data centers and response time. So this paper develop trust model in cloud computing based on fuzzy logic, to explores the coordination between Data Centers and user bound to optimize the application performance, cost of Virtual machines on Data centers and response time using Cloud Computing Analyst.

Abstract-recently, search engines become more critical for finding information over the World Wide Web where web content growing fast, the user's satisfaction of search engine results is decreased. This paper proposes a method for suggesting a list of queries that are related to the user input query. The related queries are based on previously issued queries by the users. The proposed method is based on clustering process in which groups of semantically similar queries are detected. This facility provides some queries which are related to the queries submitted by users in order direct them toward their required information. This method not only discovered the related queries but also rank them according to a similarity measure. Finally the method has been evaluated using real data sets from the search engine query log.