Internet has been universally recognized as a medium for network-enabled transfer of information and knowledge in various areas. Taking advantage of the continuously improving, web-based learning systems plays an important role for selflearning. Nevertheless, learning systems do not generally adapt to learners’ profiles. Learners have to spend a lot of time before reaching the learning goal that is compatible with their
knowledge background. In this paper, we propose architecture for intelligent E-Learning system architecture which helps learners taking advantage of the web massive contents through the use of an automated assistant (a recommender system). The proposed recommender system is based on a novel feature reduction technique with the goal of being able to adapt according to the learner's interest in the general domain without being specific to a single domain.

Cloud computing technology is envisioned as the next generation architecture of IT Enterprise. It is defined as a set of scalable data servers or chunk servers that provide computing and storage services to clients. The cloud storage is a relatively basic and widely applied service which can provide users with stable, massive data storage space. Our research shows that the architecture of current Cloud Computing System moves from central to distributed one; the reason for such movement is to avoid the bottle neck introduces since all data chunks must be indexed by a master index server. In this paper, we propose new cloud storage architecture based on P2P using agents. The system is based on a new architecture with better scalability, fault tolerance and enhanced performance.