Due to the high availability of resources in the Cloud Computing platform, there is a tremendous increase in the underutilization of these resources. Improving the throughput and effectively utilizing these resources are two main challenges in the cloud computing scenario. This paper proposes a methodology for improving the throughput and effective utilization of resources by appropriately placing the Virtual Machine in the server that would be more productive. The proposed solution is based on VM placement algorithm and an exclusive framework is designed for this algorithm. This algorithm refers to the history of data which is available in a global provenance database. By utilizing this provenance data, the system performance is improved.

A social network is a social structure made up of a set nodes, which represents social actors (such as people, organizations), and edges or lines represents relationship between these nodes or actors. Social networks have important roles in the dispersal of information and innovation, the analysis of such networks, attracted much attention in the research area. The analysis of social network can be done as a whole, which means the representations of all of its actors and identification of structures, present in that social network, that lead to the presence of communities. In the method of community detection, the main aim is to partition the network into dense regions of the graph, and those dense regions typically correspond to entities which are closely related, and can hence be said to belong to a community. In any complex network, communities are able to exchange and offer information because members in one community have similar tastes and desires. The determination of such communities is useful in the context of a variety of applications in social-network analysis, including customer segmentation, recommendations, link inference, and vertex labeling and influence analysis. This paper presents a survey on community detection approaches, which have already been proposed, and also discussing the type of social networks on which those proposed approaches are applicable. This survey can play a significant role in the analysis and evaluation of community detection approaches in different application domains.