27-06-2013, 01:03 PM
Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization
Collaborative Web.doc (Size: 95 KB / Downloads: 14)
Abstract:
With the increasing presence and adoption of Web services on the World Wide Web, the demand of efficient Web service quality evaluation approaches is becoming unprecedentedly strong. To avoid the expensive and time-consuming Web service invocations, this paper proposes a collaborative Quality-of-Service (QoS) prediction approach for Web services by taking advantages of the past Web service usage experiences of service users. We first apply the concept of user-collaboration for the Web service QoS information sharing. Then, based on the collected QoS data, a neighborhood-integrated approach is designed for personalized Web service QoS value prediction. To validate our approach, large-scale real-world experiments are conducted, which include 1,974,675 Web service invocations from 339 service users on 5,825 real-world Web services. The comprehensive experimental studies show that our proposed approach achieves higher prediction accuracy than other approaches. The public release of our Web service QoS dataset provides valuable real-world data for future research.
Introduction
Web services are self-described software applications designed to support interoperable machine to machine interaction over a network via standard interfaces and communication protocols [1]. Strongly promoted by the leading industrial companies, Web services have been widely employed in a lot of domains. Qualityof- Service (QoS) is usually employed to describe the
non-functional characteristics of Web services. With the growing presence and adoption of Web services on the World Wide Web, QoS has become an important selling and differentiating point of the functionally equivalent services. In the recent literature, a number of QoS-based approaches have been proposed for Web service composition Web service selection fault-tolerant Web services and so on. Accurate QoS values of Web services are required for these QoS-based approaches to work well. To address the fundamental problem of how to obtain the Web service QoS values, effective and efficient Web service QoS value obtaining approaches are urgently needed. The QoS values of Web services can be measured either at the server-side or at the client-side. QoS values measured at the server-side(e.g., price, popularity, etc.) are usually advertised by the service providers and identical for different users, while QoS values measured at the client-side (e.g., responsetime, throughput, availability, etc.) can vary widely among users influenced by the unpredictable Internet connections and the heterogeneous user environments.
OPERATING SYSTEM
An operating system (commonly abbreviated to either OS or O/S) is an interface between hardware and user; it is responsible for the management and coordination of activities and the sharing of the limited resources of the computer.
The operating system acts as a host for applications that are run on the machine. As a host, one of the purposes of an operating system is to handle the details of the operation of the hardware.
This relieves application programs from having to manage these details and makes it easier to write applications.
Almost all computers, including handheld computers, desktop computers, supercomputers, and even video game consoles, use an operating system of some type.
OVERVIEW OF WINDOWS XP PROFESSIONAL
Windows XP is a line of operating systems produced by Microsoft for use on personal computers running x86 and IA-64 processors, including home and business desktops, notebook computers, and media centers.
The name "XP" is short for "experience". Windows XP is the successor to both Windows 2000 Professional and Windows Me, and is the first consumer-oriented operating system produced by Microsoft to be built on the Windows NT kernel and architecture.
Windows XP was first released on 25 October 2001, and over 400 million copies were in use in January 2006, according to an estimate in that month by an IDC analyst. It was succeeded by Windows Vista, which was released to volume license customers on 8 November 2006, and worldwide to the general public on 30 January 2007.
Direct OEM and retail sales of Windows XP ceased on 30 June 2008, although it is still possible to obtain Windows XP from System Builders.(smaller OEMs who sell assembled computers) until 31 July 2009 or by purchasing Windows Vista Ultimate or Business and then downgrading to Windows XP.
COMPONENTS OF . NET FRAMEWORK
THE COMMON LANGUAGE RUNTIME (CLR):
The common language runtime is the foundation of the .NET Framework. It manages code at execution time, providing important services such as memory management, thread management, and removing and also ensures more security and robustness. The concept of code management is a fundamental principle of the runtime. Code that targets the runtime is known as managed code, while code that does not target the runtime is known as unmanaged code.
THE .NET FRAME WORK CLASS LIBRARY:
It is a comprehensive, object-oriented collection of reusable types used to develop applications ranging from traditional command-line or graphical user interface (GUI) applications to applications based on the latest innovations provided by ASP.NET, such as Web Forms and XML Web services.
The .NET Framework can be hosted by unmanaged components that load the common language runtime into their processes and initiate the execution of managed code, thereby creating a software environment that can exploit both managed and unmanaged features. The .NET Framework not only provides several runtime hosts, but also supports the development of third-party runtime hosts.
CONCLUSION AND FUTURE WORK
Based on the intuition that a user’s Web service QoS usage experiences can be predicted by both the user’s own characteristics and the past usage experiences of other similar users, we propose a neighborhood-integrated matrix factorization approach for making personalized QoS value prediction. Our NIMF approach systematically fuses the neighborhood-based and model-based collaborative filtering approaches to achieve higher prediction accuracy. The extensive experimental analysis shows the effectiveness of our approach. After obtaining the predicted QoS values on the unused Web services, most service users will make invocations to the selected Web services. The QoS values of these Web service invocations contain valuable information for improving the QoS prediction accuracy. We plan to design better incentive mechanisms and automatic approaches to enable the real-time sharing of these Web service usage experiences among service users.