One of the powerful platform to assimilate, dissimilate and retrieve information as well as extract useful information is World Wide Web. This Web data is massive, dynamic in nature and is complex in nature. Extraction of potential Value from web is carried out through Data mining, but traditional data mining has a bottleneck in storage and computing when the data is too complex. Due to increasing number of providers a new technology- Cloud Computing is used which offers various web services which help to overcome this bottleneck. Meanwhile user's behavior and demands are changing sharply, in order to maintain a balance and maximize the revenue there is a desperate need for new principle. Now a day's most of the service providers are using a static method which neglects if there is a dynamic in the user behaviors. To solve the above problem we use cloud computing technology wherein we design a massive web log data which can be analyzed on a platform bases on cloud - Hadoop Framework. Along with it in order to improve the efficiency of the existing mining methods a parallel algorithm for web log mining is needed. The proposed algorithm can be used to parallel systems wherein the data is usually stored on cloud and also helps to identify the users using methods of cloud computing and Map Reduce techniques. © Research India Publications.