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It has been proved that there is no optimal online scheduler for uniform parallel machines. Despite its nonoptimality, EDF is an appropriate algorithm to use in such environments. However, its performance significantly drops in overloaded situations. Moreover, EDF produces a relatively large number of migrations which may prove unacceptable for use on some parallel machines. In this paper a new algorithm based on fuzzy logic for scheduling soft real-time tasks on uniform multiprocessors is presented. The performance of this algorithm is then compared with that of EDF algorithm. It is shown that our proposed approach not only demonstrates a performance close to that of EDF in nonoverloaded conditions but also has supremacy over EDF in overloaded situations in many aspects. Furthermore, it imposes much less overhead on the system.

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This paper presents an alternative product recommendation system for Business-to-customer ecommerce purposes. The system recommends the products to a new user. It depends on the purchase pattern of previous users whose purchase pattern are close to that of new user. The system is based on vector space model to find out the closest user profile among the profiles of all users in database. It also implements Association rule mining based recommendation system, taking into consideration the order of purchase, in recommending more than one product. To make the association rule memoryefficient, cellular automata is used.