|An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
|Foundation of Computer Science USA
|ICCTAC2015 - Number 1
|Authors: A. Stanislas, L. Arockiam
A. Stanislas, L. Arockiam . An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds. An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds. ICCTAC2015, 1 (May 2015), 5-9.
Cloud computing is a wonderful paradigm which assures the customers of providing computing resources instantly whenever they are in need. It is the virtualization technology that makes this paradigm a reality. But the present technology which is used for provisioning virtual machines is not adequate. Thus, there is latency in service provisioning and the long waiting time of virtual machine provisioning hampers the future popularity of cloud computing. So, high scalability which is the key factor of cloud computing is not easily possible. Therefore, there is a need for a mechanism to enable the service provisioning effectively with high scalability. In view of that, this paper presents a system which predicts the workload demands of the service requests automatically so as to prepare the virtual machines in advance in order to ensure the customers with instant services efficiently without much delay. Trend value analysis using various methods is carried out in the prediction system.