2013 Fifth Conference on Measuring Technology and Mechatronics Automation
Chord-Based Semantic Service Discovery with QoS Ying Zhanga ,Hui Heb and Jing Tengc School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, P.R.China
[email protected],
[email protected],
[email protected]
a
computing. In order to achieve the high scalability, we focus on developing a decentralized discovery approach [6-8].
Abstract—Large
quantities of services emerging in the internet have put forward higher requirement to service discovery, as a result, the service discovery mechanism based on keywords and simplified classification of UDDI can no longer work well. Service description language, metadata storage and service matching algorithm are three critical factors influencing the efficiency and quality of service discovery. This paper presents a prototype system of Chord-based semantic service discovery with QoS(Quality of Service). It puts QoS into OWL-S(Web ontology language for services) to describe services, and adopts Chord-based distributed storage. OWL-QoS based matching algorithm is also used to discover services. Experiment results show that the approach presented in this paper can improve the efficiency and accuracy of service discovery commendably.
II.
The Prototype System As figure 1 shows, it has three layers in the Chord-based semantic web service discovery system. From bottom to up, they are physical network layer, friend-group layer and location network layer. In addition, there are five elements in the system as following: Service requesters live in the physical network layer. Service providers register their services to the system, then the discovery system publishes the services to the corresponding groups. Friend-group members are service providers who provide similar services. Friend-group leaders come from different friend groups. Service parsers are able to parse a complicated service into several simple services.
Keywords-Service discovery; Chord; QoS; Ontology
I.
INTRODUCTION
Both of WSDL and UDDI are basic descriptions of the Web Services [1], but this kind of UDDI discovery is simply based on the keyword matching, and produces low success rates. It stays at a grammer level. In order to describe services more properly, and supply a much more accurate matching between user needs and service descripitons, researchers focused on the Semantic Web, which is machine-readable. With combination of semantic Web and Web service, semantic web services accomplish service discovery at the semantic level. We adopt OWL-S[2] to describe services and supply a common semantic base for both service providers and requesters. How to provide services with QoS produces more and more attention [3-4]. From users' view, each user has an unique requirement. It is very important to provide a service discovery system with QoS for users. In this paper, we appended QoS measurements to OWL-S for satisfying users' high-class requirements, and called it OWL-QoS [5] . Centralized approach introduces single points of failure and exposes vulnerability to malicious attacks and doesn't suit a large number of services. This disadvantage is fatal for evolving trend to ubiquitous and pervasive
978-0-7695-4932-3/12 $26.00 © 2012 IEEE DOI 10.1109/ICMTMA.2013.93
CHORD-BASED SEMANTIC WEB SERVICE DISCOVERY SYSTEM
The Architecture of Location Network Layer In order to deal with the single points of failures, and adapt to the scalability and mobility of services, the location network layer takes Chord as a basic network. Chord is a structured routing protocol, and maps Data and peers to key values through Distributed Hash Table (DHT). A peer's identifier is chosen by hashing the peer's IP address, while a data identifier is produced by hashing the data. The hash function can be either SHA-1(M=160) or MD5(M=128).
Ontological Data In order to accomplish semantic web service discovery with QoS, we design three kinds of ontological data, namely, domain ontology, QoS ontology, and service description ontology with QoS. Domain ontolgoy describes the correlation among various services. QoS ontology describes properties and gradings of service quality. Service description ontology with QoS, called OWL-QoS, is based on the OWL-S plusing QoS information. These ontologies are given in our former work [5]. 365
Figure 1. Chord based semantic web service discovery system
III.
group leaders, then we can get those friend groups whose similarities are greater than threshold. In case that there is no such a group can be found, we select the most similar one whose similarity is the maximum. The selection function is MapTos(g)={g| sim(g, s)> sim(f, s) } , g
SERVICE REGISTRATION AND DISCOVERY
Service Registration As figure 5 shows, the process of service registration consisted of four steps. At first, a service provider can choose any friend group to join. Then the selected group leader maps service to the related friend groups. Next, with Chord, we can find out the addresses of the related friend groups. Finally, OWL-QoS can be published in the friend groups. We will give a detail of step 2. Mapping service to the related groups Taking advantage of domain ontology to compute the similarity degree between a registering service and various
s im (n 1 , n 2 )
f Leaders,f z g
and f are friend group leaders, s is service, Leaders represent a set of friend group leaders, sim(g,s) means the similarity between g and s. Formula 1 and 2 computed the similarity. Along with the increase of two nodes' distance, the similarity degree between them becomes smaller. In contrast, with the same distance, the similarity degree of the deep layer nodes is greater than that of the low layer nodes.
G u ( l 1 l 2 ) u m a x (| l 1 l 2 |,1) ° d is ( n 1 , n 2 ) G ° ® G u (l1 l 2) u T 2 ° °¯ (d is ( n 1, n 2 ) G ) u m a x (| l 1 l 2 |,1)
n1 and n2 are two nodes in the domain ontology (as figure 3 illustrates). l1 and l2 are two layers where n1 and n2 exist respectively. dis(n1,n2) means the shortest distance between n1 and n2. is an adjustable parameter. T is the depth of the domain ontology.
d is ( n 1, n 2 )
| l 1 l 2 | (1 )
d is ( n 1, n 2 ) | l 1 l 2 |
(2 )
In our prototype system, we have created 1500 service profiles with QoS measurement and divided them into sixteen groups according to DMOZ [9]. For the study, we observed the following three metrics: efficiency, recall and precision.
Service Discovery Service discovery process includes five steps. Firstly, a service requester can choose any friend group to refer requirement. Then the selected group leader maps requirement to the related friend groups. Next, with the help of Chord in the location network layer, requester can find the specific locations of the related friend groups. Fourthly, based on the OWL-QoS, service matching process will be executed within the related friend groups. At last, the matching results will be returned. The first three steps are similar to that of service registration. Service matching process has been discussed in our former work [5]. If no service could be found in the related friend groups, service requirements will be sent to service parsers, which divides service into several sub-services to relocate. IV.
Figure 2. Comparision of efficiency
In the first test, we compared the efficiency between centralized and Chord-based discovery. In figure 2, we can see that time used by the centralized service discovery is much more than that spent by the Chord-based approach.
EXPERIMENT AND COMPARISON
The Prototype System
366
machine-readable, and we can get service discovery on the semantic layer. Distributed approach had good scalability and solved the problem of single points of failure. Experiment results show that the approach presented in this paper can improve the efficiency and accuracy of service discovery commendably.
Services are divided into different friend groups according to the semantic relations among them, and the service matching can be restricted in those related groups. Therefore, the Chord-based approach is more effective. It can decrease much query time. We do test two to compare recall and precision between centralized and Chord-based approach. Recall () is defined as
T
S T S
VI.
This paper is supported by the fundamental research funds for the central universities (09QG27), the national grand fundamental research 973 program of China (2007CB307100, No.2007CB307106), the national natural science fund project (61001197).
. S denotes the set of all relevant
services of one query in the system. T is a set of the returned services. Precision () can be gained by
K
S T T
. As figure 3 shows, the recall of centralized
REFERENCES [1] F.Curbera, R.Khalaf, N.Mukhi, S.Tai, et.al:The next step in web services. Communications of the ACM, Vol. 46(10), (2003), p.29-34. [2] D.Martin. OWL-S: Semantic Markup for web services. http://www.daml.org/services/owl-s/ OWL-S: Semantic Markup for Web Services.htm. (2003). [3] S.Ran. A model for Web services discovery with QoS. ACM SIGCOM Exchanges, Vol.14(1), (2003), p.1-10. [4] J.Gennari, M. A.Musen, R.W.Fergerson, et.al. Evolution of protg: An environment for knowledge based systems development, Technical report, Stanford University, (2002). [5] Y.Zhang, H. Huang, Y. Qu, et.al. Semantic Service Discovery with QoS Measurement in universal network. In: Rough Sets and Emerging Intelligent Systems’ 07 International Conference, Warszawa, Poland, Jun.(2007), p. 707-715. [6] H. Zhuge, X. Li. Peer-to-Peer in Metric Space and Semantic Space. IEEE Transactions on Knowledge and Data Engineering, Vol.19(6), (2007), p.759-771. [7] J. Eberhard, A. Tripathi, Semantics-Based Object Caching in Distributed Systems, IEEE Transactions on Knowledge and Data Engineering, Vol.21(12), (2010), p.1750-1764. [8] I. Stoica, R. Morris, D. Karger, M.F. Kaashoek, and H. Balakrishnan, Chord: A Scalable Peer-to-Peer Lookup Service for Internet Applications, Proc. ACM SIGCOMM'01, (2001), p.149-160, Aug. 2001. [9] DMOZ website. http://www.dmoz.org.
approach is higher than that of the Chord-based approach, while the precision of the former is lower than that of the latter. That's because the former do matching among all the services, but the latter only do such things in the related friend groups.
Figure 3. Comparision of precision and recall
Figure 4. Comparision of precision
According to figure 4, we can see that average matching success rate with QoS grows faster than that without QoS, because QoS can specify the matching results. The average matching success rate increased by 6.2%. V.
ACKNOWLEDGMENT
CONCLUSIONS
In order to slove three main problems about efficiency and quality of service discovery, we adopted OWL-QoS to describe services and Chord-based structure to store meta data. With OWL-QoS, service description became
367
