No abstract

Grid computing creates the illusion of a simple but large and powerful self-managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources which leads to the problem of load balance. The main goal of load balancing is to provide a distributed, low cost, scheme that balances the load across all the processors. To improve the global throughput of Grid resources, effective and efficient load balancing algorithms are fundamentally important. Focus of this paper is on analyzing Load Balancing requirements in a Grid environment and proposing an algorithm with machine learning concepts to find more efficient algorithm.

Numerous local incidents occur on road networks daily, many of which may lead to congestion and safety hazards. If vehicles can be provided with information about such incidents or traffic conditions in advance, the quality of driving can be improved significantly in terms of time, distance, and safety. Vehicular Ad Hoc Networks (VANETs) have newly emerged as an effective tool for improving road safety through the dissemination of warning messages among the vehicles in the network about potential obstacles on the road ahead. Various Approaches of data dissemination in vehicular network can be used to inform vehicles about dynamic road traffic condition so that a safe and efficient transportation system can be achieved. Here we extensively reviewed various data dissemination techniques and identify the challenges with it. However, type of VANET applications and inherent VANET characteristics such as different network density, fast movement of vehicles make data dissemination quite challenging. General TermsVehicular Ad hoc Network (VANET), Data Dissemination, Flooding, Relaying.