Abstract
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.
TopE-Marketing And Green Cloud Computing
With the growth of high speed networks, there is an alarming rise in their usage. This ever-increasing demand is handled through large-scale datacenters, which consolidate hundreds and thousands of servers with other infrastructure such as cooling, storage and network systems. The commercialization of these developments is defined currently as Cloud computing (Buyya et al., 2008). High growing demand of Cloud infrastructure by Google, Amazon, etc has drastically increased the energy consumption of data centers (Kumar & Buyya, 2012).
Cloud computing, through the use of large shared virtualized datacenters can offer large energy savings. However, Cloud services further increase the Internet traffic, hence growing information database which in contrast increase energy consumption. Energy-efficient solutions are required to minimize the impact of Cloud computing on the environment.
According to a study by GfK (Gesellschaft für Konsumforschung) Custom Research (Murugesan, 2011), cloud computing is being used on large scale in these emerging markets. Multinational companies are taking measures on regional level to penetrate the market for good service provision and are also taking care of localization (Murugesan, 2011).
Key Terms in this Chapter
CB-Rough Sets: An extension of the notion of basic rough sets where the classification generated by an equivalence relation is replaced by a cover generated by a less strict relation like the proximity relation.
NB-Rough Sets: An extension of the notion of basic rough sets used in order to model information systems having hybrid features, that is a combination of numeric as well as categorical ones.
MapReduce: A concept which is an abstraction of the primitives ‘map’ and ‘reduce’. Most of the computations are carried by applying a ‘map’ operation to each global record in order to generate key/value pairs and then apply the reduce operation in order to combine the derived data appropriately.
Cloud Computing: A computing paradigm which represents a transition from computing-as-a product to computing-as-a-service.
Distributed Systems: A collection of computers that act, work, and appear as one large computer.
E-Commerce: A type of industry where the buying and selling of products or services is conducted over electronic systems such as the Internet and other computer networks.
BigData: A collection of data sets so large and complex that it is difficult to process it using on-hand database management tools or traditional data processing applications.
Rough Set: An uncertainty based model introduced by Z.Pawlak in the year 1982 which captures uncertainty through boundary region concept, the model introduced by G. Frege, the father of modern logic.