服务质量(QoS)驱动的服务排名与选择方法

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"这篇文章‘Yau2011-QoS-based Service Ranking and Selection for Service-based Systems’由Stephen S. Yau和Yin Yin撰写，来自亚利桑那州立大学的信息保障中心和计算、信息与决策系统工程学院。本文探讨了在服务导向系统（SBS）中，基于服务质量（QoS）的服务排名和选择方法，以解决用户在功能需求之外对非功能性品质（如吞吐量、延迟、可靠性和安全性）的需求。" 在服务导向架构（SOA）中，服务发现和匹配技术已经广泛用于根据用户的功能需求找到合适的服务。然而，服务质量（QoS）作为决定服务成功与否的重要因素，常常被用户同时考虑。QoS不仅包括功能特性，还包括响应时间、可靠性、安全性和其他性能指标。该论文提出了一种新的QoS基础的服务排名和选择方法，以帮助用户在满足功能需求的服务集合中，找出最符合其QoS要求的服务。为了评估服务如何满足用户的QoS需求，作者引入了一组函数，用于在不同度量和尺度上规范化服务的QoS各个方面。这些函数能够计算出每个服务在特定QoS方面的满意度分数。这种方法可能包括对服务性能的加权评估，以及对不同QoS属性的相对重要性的考量。服务排名是通过比较和排序这些满意度分数来完成的，从而生成一个满足用户需求的最佳服务列表。服务选择则是在这个排名基础上，根据用户的优先级和特定场景的约束，确定最合适的服务实例。此外，考虑到服务的动态性，如性能波动和服务可用性变化，论文可能会讨论如何实时更新服务的QoS信息，并相应地调整排名。此研究对于构建高效、用户友好的SBS至关重要，因为它提供了处理多维度QoS要求的工具，使服务消费者能够做出更明智的决策。同时，这种方法也为服务提供商提供指导，以优化他们的服务，以满足市场上的QoS标准和用户期望。这篇论文深入探讨了在服务导向环境中，如何有效地平衡功能需求和非功能需求，以提升服务质量和用户体验。

QoS-based Service Ranking and Selection for Servic-based Systems

Stephen S. Yau and Yin Yin

Information Assurance Center, and

School of Computing, Informatics, and Decision Systems Engineering

Arizona State University

Tempe, AZ, USA

{yau, yin.yin}@asu.edu

Abstract—To facilitate rapid development of service-based

systems (SBS), many service discovering and matching

techniques have been developed to find services according to

users’ functionality requirements. However, users usually also

have requirements on non-functional qualities of services

(QoS), such as throughput, delay, reliability and security,

which are also critical for the success of SBS. In this paper, a

QoS-based service ranking and selection approach is presented

to help users to select the service that best satisfies users’ QoS

requirements from a set of services having already satisfied

users’ functionality requirements. To determine how well a

service satisfies users’ concerned QoS requirements, a set of

functions is presented to normalize services’ QoS on various

QoS aspects with different metrics and scales, compute

services’ satisfaction scores on each QoS aspect, and combine

each services’ satisfaction scores on all QoS aspects together as

an overall satisfaction scores. Compared with existing service

ranking and selection techniques, our approach has the

following advantages: 1) selects the service that best satisfies

users QoS requirements instead of the service with the best

QoS which may be much overqualified for the users’ QoS

requirements, 2) improves the flexibility in users’ QoS

requirement specification, and 3) uses the prospect theory to

more accurately model the relation between services’ QoS and

their satisfaction scores.

Keywords- Service ranking and selection; QoS requirements,

QoS-based;satisfaction score; service-based systems; prospect

theory.

I. INTRODUCTION

Web services and service-oriented architecture (SOA)

have emerged as effective tools in improving distributed

application systems’ flexibility, interoperability, scalability

and robustness, which have been widely used in many

domains, such as e-commerce, e-government, health care,

and homeland security [1, 2]. In SOA, service providers

specify their services' functionalities as contracts through

standardized service description language (WSDL) [3] and

developers of service-based systems (SBS) who are looking

for services providing specific functionalities, can search for

services in service directories, and integrate the found

services into their SBSs instead of developing systems from

scratch. This process certainly accelerates and facilitates the

development of large-scale of service-based systems.

Besides the functionalities provided by services, the

services’ QoS, such as throughput, delay, reliability and

security, are also important [4]. However, existing

service discovering and matching approaches, such as

UDDI, Service Location Protocol (SLP), Jini, and

Bluetooth, only search for services with functionalities, and

do not provide any guarantee on services' QoS. Hence, the

users have no idea about whether the discovered services'

QoS can satisfy their QoS requirements. Furthermore, if

there are several services providing equivalent

functionalities, users do not know which service should be

selected.

To incorporate service QoS information in service

discovery and matching, there are two major challenges.

The first is to specify, measure, and monitor services’ QoS.

Several QoS quality models and ontologies have been

presented to formally organize and specify various QoS

aspects with their metrics and scales [5-9]. These QoS

specifications are then integrated with extended web service

standards [10-16]. Furthermore, services’ QoS can be either

monitored and updated through a runtime QoS monitor [17],

users’ feedbacks and reputations [18, 19], or data mining

with historic data [20, 21].

The second challenge is to match services’ QoS with

users’ QoS requirements. While users have requirements on

multiple concerned QoS aspects, it is difficult to find the

best service by comparing multiple concerned QoS aspects

simultaneously. For example, it is not clear how to compare

a service with high throughput and high price to another

service with low throughput and low price. Furthermore,

services’ QoS on various QoS aspects cannot be simply

combined because different users’ preferences on concerned

QoS aspects may be different, and various QoS aspects have

different scales and value ranges.

While the first challenge has been well addressed by

existing techniques [5-21], the second challenge has not

been well studied. In this paper, we will present a service

ranking and selection approach to meet the second

challenge. This approach will include 1) normalizing

services’ QoS on various aspects to a unique range, 2)

measuring how well services’ QoS satisfies users’ QoS

requirements on each concerned QoS aspect with a

satisfaction score, and 3) combining services’ satisfaction

scores on all concerned aspects together as an overall

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服务质量(QoS)驱动的服务排名与选择方法

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