Earticle

Cloud computing is a new paradigm in which dynamically scalable virtualized computing resources are provided as a service over the Internet. However, since resources are limited, it is very important that cloud providers efficiently provide their resources. This paper presents a trust model for efficient reconfiguration and allocation of computing resources satisfying various user requests. Our model collects and analyzes reliability based on historical information of servers in a Cloud data center. Then it prepares the best available resources for each service request in advance, providing the best resources to users. We also carried out experiments for reliability analysis with 4 data types, including an all data set, random data set, recent data set, and the data set within a standard deviation. As a result, using our trust model, cloud providers can utilize their resources efficiently and also provide highly trusted resources and services to many users.

In recent, service providers would like to support actual feeling services for internet users to give a satisfaction. Since web environments are changed more newly, internet users want to satisfy their needs. For offering the satisfaction to users, we suggest a middleware to give impressive media that is named Harmonized Media Service Middleware (HMSM). HMSM can support the harmonized media by using some emotional knowledge for each user. The harmonized media is constructed with media content, like videos, and emotional effects. The video content is made by individually user, so it has a just recorded data as simple media. However, because user wants to a feeling of satisfaction, it is necessary to offer multiple media which include emotional effects for user’s impression. In this paper, we suggest a way to offer a middleware for actual feeling multiple media, called HMSM. HMSM analyzes the input media and then extracts the effects which have compatibility with the input media from emotional effect database. At the result, HMSM will be offer actual feeling service to satisfy user’s needs.

In this paper, Multiple Reduced Hypercube(MRH), which is a new interconnection network based on a hypercube interconnection network, is suggested. Also, this paper demonstrates that MRH(n) proposed in this study is superior to the previously proposed hypercube interconnection networks and the hypercube transformation interconnection networks in terms of network cost(diameter x degree). In addition, several network properties (connectivity, routing algorithm, diameter, broadcasting) of MRH(n) are analyzed.

GRID environments are privileged targets for computation-intensive problem solving in areas from weather forecasting to seismic analysis. Mainly composed by commodity hardware, these environments can deliver vast computational capacity, at relatively low cost. In order to take full advantage of their power we need to have efficient task schedulers with the ability to maximize resource effectiveness, shortening execution times. GRID schedulers must not only decide taking a snapshot of the GRID status into account, but should also consider the output involved in past decisions. In this work, we intend to show how resource usage can be analyzed, through the use of data mining techniques, to predict performance availability of a GRID environment, as a preliminary work to increase scheduling efficiency as well as adequate resource provisioning.