TIME WINDOW BASED AUTO-REGRESSIVE HYBRID PSO FOR OPTIMAL CLOUD PACKAGE SELECTION

Rapid expansion of cloud technologies were mainly due to the increased requirements of cloud users. However, increased requests also laden with increased resource requirements especially due to the elastic nature of the cloud. This mandates the need for effective resource provisioning model. This paper presents a Time Window based AutoRegressive Hybrid PSO (TWARP) model that provides faster and more appropriate resource allocations. The TWARP model is composed of a temporal data grouping model to create training data, an autoregression model to predict future requirements, a PSOSA based optimal package selection mechanism and a final request handling mechanism that allocates the actual resource to a user. Experiments indicate low time requirements and effective allocation levels. Comparison with recent literature works also indicates highly effective performances of the proposed model.