Cloud computing is a fastest growing technology in the research and industry field.It provides the on demand resources to the customers on the rent basis. These resources are provided through the virtual machines. Resources required by the virtual machines can change dynamically. So load balancing in the cloud is more challenging task as compared to the traditional computing, where the resource requirements are not changed with time. Overall performance of the cloud system can be increased by the efficient load balancing approach. Three steps are involved in the load balancing method i.e., physical machine selection, virtual machine selection and destination physical machine selection. In the past few years a number of load balancing approaches have been proposed to increase the resource utilization and minimize the energy consumption. This paper has proposed a load balancing approach which uses the lower and upper threshold to select the physical machine (PM) for migrating the virtual machine (VM). Then place the selected VM to the PM which consumes minimum power to minimize the energy consumption.
To create the cloud environment, CloudSim simulator is used which provides the interface to deal with the physical and virtual machines. To evaluate the performance, the proposed method is compared with already present load balancing approaches. Simulation result shows that proposed approach minimize the energy consumption, migrations and total simulation time.

Breathless flow in data collection and storage mechanism has enabled Firms to heap up a massive amount of data. In many cases, these huge volumes of data can be mined for fascinating and applicable information in a wide range of applications. When the arrival of data is fast as well in a large bunches in term of amount, this lead major problem to go through this data in both the circumstances in store it and in extracting the useful information from it. To taking under these issues continues mining or stream mining is a best way. Data steam mining allows to not storing the entire data for future prediction which lead to overcome the e-vestige and unnecessary storage overhead. But there is no such way in literature to mine continues data direct, so first one make it feasible accordingly and then mine it. Here in this paper we present an algorithm which handle stream data in very effective manner.