The popularity of cloud computing has increasing rapidly across various sectors. The model that cloud computing offered has drawn attention of the enterprise such as pay as you go model, auto scaling, etc. With those kinds of advantages, it will help enterprise to save their cost while running their business.

The benefit of cloud computing leads lots of cloud server providers offer their cloud server for rent in the internet. Each cloud server providers have their advantages and disadvantages. Enterprise needs more time to find a suitable could server provider and they also might not know the differences between cloud server providers.

In this research, we present a search model for search cloud server providers, and use enterprise location (context aware method) for recommend the cloud server providers which is nearby the enterprise in order to improve bandwidth and reduce latency problem. Furthermore, we implement the search model, recommendation system, and evaluation standard in the system for user using their requirements and location. Implementation result show cloud server which is near to enterprise will improve bandwidth and reduce latency problem.

The popularity of cloud computing has increasing rapidly across various sectors. The model that cloud computing offered has drawn attention of the enterprise such as pay as you go model, auto scaling, etc. With those kinds of advantages, it will help enterprise to save their cost while running their business.

The benefit of cloud computing leads lots of cloud server providers offer their cloud server for rent in the internet. Each cloud server providers have their advantages and disadvantages. Enterprise needs more time to find a suitable could server provider and they also might not know the differences between cloud server providers.

In this research, we present a search model for search cloud server providers, and use enterprise location (context aware method) for recommend the cloud server providers which is nearby the enterprise in order to improve bandwidth and reduce latency problem. Furthermore, we implement the search model, recommendation system, and evaluation standard in the system for user using their requirements and location. Implementation result show cloud server which is near to enterprise will improve bandwidth and reduce latency problem.

Acknowledgement I
Abstract II
Table of Contents III
List of Figures V
List of Tables VI
Chapter 1. Introduction 1
1.1 Motivation 4
1.2 Thesis outline 5
Chapter 2. Backgrounds 7
2.1 Cloud Computing 7
2.2 Location Based Services (LBS) 11
2.3 Google Maps 14
2.4 Harvesine Formula and Spherical Law of Cosines 16
2.5 Recommendation System 17
2.5.1 Content - Based Filtering 18
2.5.2 Collaborative Filtering 19
2.5.3 Context Aware Method 20
2.5.4 Hybrid Method 20
Chapter 3. Related Works 23
Chapter 4. System Framework 27
4.1 System Architecture 28
4.2 Recommendation Component 31
4.3 Search Component 33
4.3.1 Basic Model 33
4.3.2 Features Model 35
4.4 Cloud Server Evaluation Value (CSEV) 39
Chapter 5. Case Study and Implementation 47
5.1 Implementation Environment 47
5.2 Case Study and Case Analysis 48
Chapter 6. Conclusions and Future Works 61
References 63

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