Motoi Iwashita

Risk Management on the Security Problem in Cloud Computing

ICT systems have been investigated for flexible systems configuration, systems operation cost reduction, environmental impact reduction, etc. Cloud computing has attracted attention as technology that solves these. In the U.S., business Cloud services, such as Amazon EC2/S3, Google Apps, Force.com, and Windows Azure, are gaining more and more users. Additionally, study of Cloud computing, such as a governmental i-Japan strategy and a start of the smart Cloud study group of the Ministry of Internal Affairs and Communications, is progressing rapidly in Japan. However, the investigation on the present Cloud computing is mainly focused on the service side, while the security side has not been sufficiently looked at. The security perception by the social viewpoints of a users vague uneasiness has especially been insufficiently investigated. This paper looks into the various risks when a company uses Cloud computing. That is, the security from a users viewpoint in Cloud computing is investigated. Concretely, the risk factor from a users viewpoint in such Cloud computing is comprehensively extracted with the risk breakdown structure (RBS) method. Furthermore, the risk factors that were extracted are analyzed and evaluated. A detailed countermeasure and proposal are produced on the basis of these results. These in turn will be used to promote public Cloud use, strengthen competitiveness by cost reduction, and increase the efficiency of corporate management.

Approaches to analyze requirements of billing management for cloud computing services

Business management using information-communication (info-communication) infrastructures such as broadband networks and data centres will become more and more important these days. Moreover, cloud computing service induces a lot of corporate alliances and it makes customer-service management complicated. Especially, billing management is very critical for both providers and customers. It is expected to establish the billing management method adjusting environment change quickly. Therefore, this paper proposes the methodology of acquiring required functions for billing management in complicated future cloud computing services. The approaches are based on the Soft Systems Methodology (SSM) with Unified Modeling Language (UML). Then the requirement functions of billing management are expressed by Use Case Diagram for future cloud computing services.

Mobile Carrier Choice Modeling under Competitive Conditions

This paper presents a mobile-carrier choice modeling to analyze customer preference and understand customer choice behavior in the mobile phone market. Due to severe competitive conditions, there are a few differences among mobile phone services provided by mobile-carriers. We propose a new mobile-carrier choice modeling considering incentive factors and restrictive factors as decision-making factors. Web survey is carried out to obtain the sample data for this model. We show the model estimated by using the survey data to analyze mobile-carrier choice behavior.

Mobile-carrier Choice Modeling Framework Under Competitive Conditions

This paper presents a mobile-carrier choice modeling framework to analyze customer preference and understand customer choice behavior in the mobile phone market. Due to severe competitive conditions, there are few differences between the mobile phone services provided by mobile-carriers. We propose a new mobile-carrier choice modeling that takes into account incentive factors and restrictive factors as decision-making factors. A Web survey was carried out to obtain the sample data for this model. We show the model estimated from the survey data to analyze mobile-carrier choice behavior.

Customer Segmentation in Mobile Carrier Choice Modeling

Service choice behavior of customers is becoming complicated and diversified in the mobile phone market. The aim of this paper is to analyze mobile carrier choice behavior and clarify the customer segments and their decision-making factors. We classify mobile phone customers into two groups: churning customers and stable customers. Customer segmentation is carried out based on survey data for each customer group in order to clarify the decision-making factors. We propose a new service choice modeling with regard to mobile carriers based on the customer segmentation results.

Internet Traffic Classification Using Score Level Fusion of Multiple Classifier

Internet traffic is continuously growing fast due to the rapid spread of the internet and the speed-up of the internet connection. Also, the applications provided on the internet have become more diversified. To support the QoS requirements for these internet applications, it would be better to measure the traffic volume according to the applications. Therefore, we are engaged in the application classification method, which is an offline technique for identifying the applications in units of flow. In some application classification methods, the applications of the target flows are analyzed according to their statistics on traffic metric, or features. We focus on these feature based classification methods, since the methods have the advantage that the port number and the packet payload need not be checked for classification. In the field of the machine learning, the classification methods that consist of multiple classifiers have been discussed. This is why the classification methods are improved in performance. However, the conventional feature based classification methods consists of single classifier. Also, the design of multiple classifiers has hardly been discussed. The design includes the way of combining some classifiers. Here, we introduce the fusion of multiple classifiers and propose applying the score level fusion using feature vectors to concatenate each classifier score to classify the flow into applications.

Mobile Internet-Access Behavior Analysis

The rapid growth of the mobile Internet market has caused various changes in the mobile business. The purpose of this study is to construct mobile-user behavior models which are effective to plan business strategies in the mobile Internet market. We analyzed mobile Internet-access behavior based on the original survey data collected from mobile Internet users. This paper showed the results in the following points: mobile-Internet usages patterns, purchase behavior in the Mobile-Commerce market, etc. We clarified the basic characteristics of mobile Internet users.

Demand Analysis by Modeling Choice of Internet Access and IP Telephony

In Japan, demands for broadband Internet access and IP telephony have increased dramatically in recent years. According to official sources, as of September 2009, there are 30.9 million users of broadband Internet access and 20.9 million of IP telephony. This study evaluates and estimates the market share of fiber-optic Internet connection, which is becoming the major player in broadband services, paying specific attention to IP telephony. A comprehensive combined choice model of Internet access line, IP telephony, and awareness of IP telephony is presented. The most suitable parameters for the model were determined by using an original market research survey conducted in Japan during 2004 with stated-preference choice experiments of both Internet access and IP telephony. The results indicate that increasing awareness has the potential to dramatically increase the penetration of IP telephony. The results also indicate a great variability in price sensitivity across income groups for the choices of Internet access line and IP telephony. The fiber optic share is shown to change with a change in its own monthly usage charge, indicating that market share gains are possible through reduced usage fees.

Evaluation of HTTP video classification method using flow group information

Recently, the traffic volume of HTTP video applications, such as YouTube, is rapidly growing on the Internet. To support the quality of service requirements of HTTP video applications, network carriers need to design bandwidth by taking into account the traffic volume of HTTP video applications. However, since most HTTP video applications are provided in a web browser, it is difficult to classify HTTP video applications with other web applications by the port number. We propose an HTTP video application classification method by using a machine learning method with traffic-flow features such as packet size. We propose a new feature that is useful in classifying HTTP video applications. We can improve the accuracy of traffic classification of HTTP video applications by 12%. Furthermore, we compare the accuracy and calculation time among three machine learning methods.

Semantic analysis and classification method for customer enquiries in telecommunication services

A variety of services have recently been provided that depend on highly developed networks and personal equipment. With these advances, connecting this equipment has become increasingly more complicated. Customer enquiries about problems such as no-connection to an Internet/phone service will increase, and telecom operators will require the ability to understand such situations and act as quickly as possible. Therefore, it is important to analyze failure trends and establish, in advance, effective coping processes for complex problems conveyed in customer enquiries. We present a method for analyzing and classifying customer enquiries efficiently and precisely from the structural type of description in ontologies. Moreover, our method can reconstruct semantic content efficiently by extracting related terms through analysis and classification. This method is based on a dependency parsing and co-occurrence technique to enable classification of a large amount of unstructured data into patterns because customer enquiries are generally stored as unstructured textual data. The validity of the method is evaluated and the method to determine threshold values is developed by using a large amount of customer enquiries in the real operational field.