Cloud computing represents a paradigm shift and it can be applied to a wide range of areas, including e-commerce, health, education, communities, etc which are emerging as the important sectors in today's market. Day-by-day more knowledge is added to the Internet and is shared amongst the users over the cloud resulting in increase of energy consumption which needs to be managed. This usage can be brought into account for measuring and hence conserving the energy. The consumption is all together considered for the processing, storage and transport of the knowledge granules over the cloud. Since the data accessed in the cloud is “on-demand,” the prediction techniques like those using rough sets can be used to minimize the transfer of data over the cloud networks. The data over the cloud can be procured with the help of rough set based methods efficiently which can help in conserving the energy. In this chapter, we propose a neighbourhood based rough set approach, which is efficient in handling heterogeneous features for knowledge acquisition using MapReduce from BigData. Also, we discuss how green cloud computing can be helpful in increasing the efficiency of emerging markets. Some future trends researches have also been proposed.