Milanjit Kaur, Komal Arora


Data is present in large amount and it became quite hectic to deal with with this amount of data but data mining has made it easy .Data mining has given various concepts which has made the process of sentimental analysis easy . Sentiment analysis involves the usage of text scrutiny and processing in order to recognize the patterns plus gaining the useful information. Various techniques namely Natural Language Processing, Machine learning, Text mining are involved in this process of sentiment analysis. By collecting different opinions, polarity is find out and is categorized as positive, negative or neutral.

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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 Auto-Regressive 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 auto-regression model to predict future requirements, a PSO-SA 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.



Cloud Provisioning; Auto-regression; PSO; Simulated Annealing; Package selection

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