Cloud Computing is being used widely all over the world by many IT companies as it provides various benefits to the users like cost saving and ease of use. However, with the growing demands of users for computing services, cloud providers are encouraged to deploy large datacenters which consume very high amount of energy and also contribute to the increase in carbon dioxide emission in the environment. Therefore, we require to develop techniques which will help to get more environment friendly computing i.e. Green Cloud Computing. Cloud computing is an increasingly popular paradigm for accessing computing resources. This paper discusses some of the research challenges for cloud computing from an enterprise or organizational perspective, and puts them in context by reviewing the existing body of literature in cloud computing. Various research challenges relating to the following topics are discussed: the organizational changes brought about by cloud computing; the economic and organizational implications of its utility billing model; the security, legal and privacy issues that cloud computing raises. It is important to highlight these research challenges because cloud computing is not simply about a technological improvement of data centers but a fundamental change in how IT is provisioned and used. This type of research has the potential to influence wider adoption of cloud computing in enterprise, and in the consumer market too

Cloud Computing is a technology that provides a platform for sharing of resources such as software, infrastructure, application and other information. Cloud Computing is being used widely all over the world, as it provides benefits to the users like cost saving and ease of use. The research work focuses on the study of task scheduling mechanism in cloud. The main goal is to reduce the power consumption by datacenters. Energy efficient scheduling of workload help to reduce the consumption of energy in datacenters thus helps in better usage of resources. An improved power saving algorithm is proposed by combining the task classification along with VM skewness algorithm with different scaling options. Skewness is used to quantify the unevenness in utilization of multiple resources on the server. Our purposed algorithm calculate the skewness factor of all Virtual Machines and based upon its value. The proposed approach is performing and shows a decrease in response time, waiting time, processing cost and overall electrical power consumed. The study can be further extended by applying the proposed algorithm on actual Cloud Computing environment and we can also integrate various energy saving technologies into data centers to reduce energy consumption.