Load Balancing in Cloud with MNLR algorithm

The reduction of energy consumption and carbon footprint in Cloud services and knowledge centres has been the focus in the ICT industry. The need to delve deep into the study of energy consumption in data centers is so because data in cloud services is processed and stored in these data centers. Energy consumption cannot be just attributed only to data centres. It also includes the consumption of energy at the transport network when end-users are connected to the cloud and also when cloud is accessed by the devices of end-users. Earlier, energy consumption at data centres alone was viewed upon while consumption of energy at transport network and end-user level was not considered. In order to cast light on the ignored parts of consumption of energy, three well-known and familiar Cloud applications - Facebook, Google Drive and Microsoft OneDrive are taken into account. Studies on these applicaions have been done using various measures and models. End results prove that in order to achieve efficient energy management in cloud services, improvement in energy efficiency at transport network and end-user devices must go hand in hand along with the improvement of energy efficiency at the data centres. With the arrival of Internet of Things (IoT) and Fog Computing concept, there has been an increase in the hosting and distribution of both content and applications from nano data centres which are tiny servers at end-user premises. Different views have been established with respect to the energy consumed by these nano centres. It is so because different energy consumption models have been used ignoring the energy consumption at transport network level and end-user devices. In order to reduce the short of knowledge in this field, conventional and measurement based models have been proposed for network topology and energy consumption in nano data centers. These nano data centres may be more or less energy-efficient than centralized data centers to a certain extent. A number of conclusions have been found from this study. it includes the causes that could facilitate consumption of less energy by nano data centers in comparison to its centralized counterpart. The type of access network linked to nano servers, the ratio of idle time of the nano server to its active time and, the kind of applications that also takes into account the number of downloads, updates and pre-loading of data all influence the factors of efficient energy consumption at the non centres.