ACKNOWLEDGEMENT

ACKNOWLEDGEMENT

I am grateful to GOD Almighty for giving me the courage and strength to complete my seminar successfully. I am thankful to our beloved principal Prof. Shahir V K and our respected Head of the Department of Computer Science and Engineering Mr. Gireesh T K for their parental guidance and support.

I would like to thank our seminar co-ordinators Ms. Divya M and Ms.Anjana T K for giving me innovative suggestions and assisting in times of need. I am thankful for valuable guidance and enduring encouragement throughout this study.

I also remember with thanks the timely help and constant encouragements induced by other faculties of AWH Engineering College, my friends and parents. I express my sense of gratitude to Department of Computer Science & Engineering, AWH Engineering College, for providing me with facilities to complete my work.

ARUNIMA VREG NO:EWALECS008ABSTRACT

The hindrances to the adoption of public cloud computing services include service reliability, data security and privacy, regulation compliant requirements, and so on. To address those concerns, we propose a hybrid cloud computing model which users may adopt as a viable and cost-saving methodology to make the best use of public cloud services along with their privately-owned (legacy) data centers. As the core of this hybrid cloud computing model, an intelligent workload factoring service is designed for proactive workload management. It enables federation between on- and off-premise infrastructures for hosting Internet-based applications, and the intelligence lies in the explicit segregation of base workload and flash crowd workload, the two naturally different components composing the application workload. The core technology of the intelligent workload factoring service is a fast frequent data item detection algorithm, which enables factoring incoming requests not only on volume but also on data content, upon changing application data popularity. CONTENTS1. INTRODUCTION

1

2. LITERACTURE SURVEY

3

3. HYBRID CLOUD COMPUTING MODEL

5 3.1 Design Rationale

5 3.2 Architecture

7 3.3 Discussion

84. INTELLIGENT WORKLOAD FACTORING

10 4.1 Problem Model

10 4.2 Logic View

11 4.3 Fast Frequent Data Item Detection Algorithm

125. ANALYSIS

14 5.1 Request Rate Estimation

14

6. EVALUATION

177. CONCLUSION

22 REFERENCES

23

GLOSSARY

24

LIST OF FIGURES

Figure 3.1 Video stream workload evolution on Yahoo! Video Site.the load

5Figure 3.2 Application hosting platform architecture in the hybrid Cloud Computing Model 6.Figure 3.3. Application hosting platform architecture in the hybrid Cloud Computing Model. 7Figure 4.1 Logic view of Workload Factoring Componen

10Figure 4.2. Schematic description of fast Top K algorithm.

11 Figure 6.1 The video streaming service testbed with a hybrid platform

19.LIST OF ABBREVIATIONS

1. PCI-DSS : Payment Card Industry Data Security Standard

2. ARIMA : Auto- Regressive Integrated Moving Average

3. IWF :Integrated Workload Factoring