World has become a global village. With the advent of technology, the concept of Cloud Computing has evolved a lot. Cloud computing offers various benefits in terms of storage, computation, cost and flexibility. It focuses on delivering combination of technological components such as applications, platforms, infrastructure, security and web hosted services over the internet. One of the major elements of Cloud Computing infrastructure is Data centre. Companies host there applications and services online through Data centres, whose probability of downtime is expected to be very low. Since, data centre consists of number of servers; the rate of service failure is usually high. In this paper we have analysed service failure rate of a conventional data centre. The Fault Trend of network failure by assuming there occurrence as a Poisson Process was made. The accurate prediction of fault rate helps in managing the up gradation, replacement and other administrative issues of data centre components.

Vehicle Make and Model Recognition (VMMR) has emerged as a significant element of vision based systems because of its application in access control systems, traffic control and monitoring systems, security systems and surveillance systems, etc. So far a number of techniques have been developed for vehicle recognition. Each technique follows different methodology and classification approaches. The evaluation results highlight the recognition technique with highest accuracy level. In this paper we have pointed out the working of various vehicle make and model recognition techniques and compare these techniques on the basis of methodology, principles, classification approach, classifier and level of recognition After comparing these factors we concluded that Locally Normalized Harris Corner Strengths (LHNS) performs best as compared to other techniques. LHNS uses Bayes and K-NN classification approaches for vehicle classification. It extracts information from frontal view of vehicles for vehicle make and model recognition.