SOA based Adaptive Reliable Service Delivery Framework using QoS: A Fuzzy-Bayesian Network Approach

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 5, August 2010

SOA based Adaptive Reliable Service Delivery Framework
using QoS: A Fuzzy-Bayesian Network Approach

R.Sivaraman Dr.RM.Chandrasekeran
Dy.Director-CCT Registrar
Anna University Tiruchirappalli Anna University Tiruchirappalli
Email: rsiva.raman@yahoo.com Email: aurmc@sify.com

Abstract associated with that particular category and the run-time
Service Oriented Architecture has gained a universal evaluation of QoS metrics is not available To bridge the gap
acceptance as a strategy for developing new applications in between the Web Services layer and the underlying QoS
dynamic environments through self contained, reusable and aware transport methodologies, a suitable framework may
configurable services. By adherence to the web services be developed which enables the efficient service selection.
standards stack, these services can interoperate with other
services and can also be transformed into a composition of This framework should allow both the clients and
services. The behavior and operation of a service has to be service providers to specify their requests and service offers
closely monitored for an efficient delivery to the client. with QoS properties. It should also map the QoS
Ranking and selection of the services among various service requirements from the Web service and application layer
providers have become an important factor for a successful onto the underlying networking layer thereby achieving an
business solution. Quality of Service (QoS) determines the overall boost in the QoS across different layers as that of the
quality and usability of a service and is determined after Internet model. For this a well defined set of QoS indices
analyzing the QoS attributes collected from various sources. are used which includes non-runtime data like cost and run-
Hence it has become our research motivation for transfer time data as availability, execution time, mean response
and reception of QoS data. In this paper, we propose an time, trustworthy, performance, capacity. The cost is a
adaptive and reliable service delivery framework, which measure of the price involved in requesting the service,
adopts a Fuzzy-Bayesian Network (FBN) approach through which is based on volume of data and execution time is the
which the web services can be ranked and selected guaranteed max (or average or min) time taken between the
automatically. Also this framework offers reliability and arrival of service request and catering of the request. The
availability aspects of QoS demand. capacity represents the limit of concurrent requests for
guaranteed performance. For some of the QoS metrics it is
Keywords : Web service selection, QoS, Fuzzy-Bayesian not dependable to rely on the service provider’s
Network (FBN), Web Services ranking advertisement as it may have a biased version favoring their
own service. So the QoS data has to be adjudged in a neutral
1. Introduction way and this leads to the development of an independent
framework which not only relies on service provider but
The QoS specification in Web Services has become the also considers other indices.
need of the day. The service consumers aim to have a good
service performance with relatively low waiting time, high The rest of the paper is organized as follows, the section
availability and reliability to successfully use the service. 2 discusses the survey of literature, section 3 describes the
Also the service providers are formulating their efforts to design and architecture of the proposed framework and
achieve high throughput guarantees with low response time section 4 deals with the Fuzzy-Bayesian Network. In section
using dynamic capacity allocation, resource allocation and 5 we provide the implementation and experimental results.
load balancing in order to cater to a large volume of clients Section 6 discusses the possible future work and it
with assured QoS. In the present architecture each of the concludes.
input requests are manually mapped to a pre-specified
category of the provider at the registry. The service
consumer has little knowledge about the QoS attributes

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2. Related Work

With the widespread use of Web Services the QoS have service composition as a fuzzy constraint satisfaction
became the significant factor in selecting a best service problem. Each QoS criteria has five fuzzy sets describing its
among the providers and it constitutes the differentiating constraint levels: Poorly Acceptable (PA), Almost
point for a group of services. QoS generally refers to Acceptable (AA), Acceptable (A), Very Acceptable (VA),
nonfunctional properties of Web services such as price, and Extremely Acceptable (EA). The overall QoS
performance, reliability, integrity, availability, accessibility, preference of a user is then represented by a fuzzy
interoperability, security, etc. [1]. Certain traditional expression which is composed of a group of fuzzy sets
approaches [2] make use of QoS policies and a weighing connected by the and logical operator. They have introduced
system to judge through the attributes. This approach is necessary definitions for mapping a Web service dynamic
applicable only when there is a relatively small set of composition to a fuzzy constraint satisfaction problem
services and attributes. Since all the services cannot be (FCSP) and finding its solution through NP-hard [12].
weighed through a common attribute thereby limiting this Xiong et.al. [13] modeled QoS based service selection as a
approach to a smaller set of services. Also rule based model fuzzy multiple criteria decision making problem (FMCDM).
proposed by Patel et.al [3] is too restrictive and provide a QoS criteria are evaluated by a set of linguistic expressions
condensed set of alternatives for service execution. Menasce L1 = {Very Poor (VP), Medium Poor (MP), Poor (P),
[4] in his research has highlighted the need for QoS Medium (M), Good (G), Medium Good (MG), Very Good
definition, specification and evaluation in WS from the (VG)}. Similarly, the weights of QoS criteria are expressed
perspective of both service provider and service user. W3C by a set of linguistic expression L2 = {Very Low (VL), Low
[5] has summarized the key requirements of QoS for Web (L), Medium (M), High (H), Very High (VH)}. These
services. Recently, several research works have dealt with linguistic expressions are mapped to fuzzy set membership
the definition of QoS languages for WS-based applications. functions to yield the results. Agarwal et.al [14] suggested
Ludwig [6] in his work have discussed about the QoS that user preferences can be defined as a set of fuzzy If-
languages and the service level agreements(SLA) that are to Then rules. The If part contains membership functions of
be initiated between the service consumer and service various QoS criteria of a Web service and the Then part is
providers for proper operation of the system after satisfying one of the membership functions of a special concept
the QoS requirements. Hewlett-Packard (HP) has proposed a representing the rank of the Web service. In each rule, fuzzy
Web Services Management Language (WSML) and concepts like fast, medium, slow or cheap, medium,
framework and IBM have developed a Web Service Level expensive are used to express imprecise values of QoS
Agreement (WSLA) language. properties and these fuzzy concepts are modeled as
membership functions.

In a SOA environment the QoS data of the service 3. Proposed Framework
providers continuously change during the lifetime of a
service. There is no mechanism that periodically updates the In this paper we propose to create a reliable
QoS data into the registries. Al-Masri et al.[7] have stated architectural framework that can manage faults and can
that about 53% URL’s of the Universal Business adapt itself to provide a reliable service. This framework
Registries(UBR) are invalid and they cannot be validated decouples the clients from the service providers and takes
with a valid WSDL document. This means that the metadata care of the service execution after due consideration of all
information collected at the time of registration of a service QOS issues. Presently the web service clients are typically
in the registry, has been modified and not yet been reported hardwired with the service providers and does not adapt
to the registry for proper updation. Since the information dynamically in case of service failures. Unavailability of a
contained in the UBR are not accurate, it utilized more service is handled only with a manual intervention during
resources when performing the binding of web services and failures and fixing them accordingly. This involves service
wasted a considerable time in trying to communicate with stoppage which directly hinders the performance of the
either non functional or poorly functioning web services. system. The proposed framework addresses this issue by
The clients should know the revised QoS information to acting as a layer in between the web service clients and the
choose the appropriate service accordingly. web service providers and dynamically reconfigures itself to
respond to the faults so that the business processes can
Various query based and monitoring based continue in the event of service failures. By virtue of using
methods exist [8-10] for obtaining the QoS values of the SOA and XML based web services, this framework is
services. The former periodically queries the service and multiplatform enabled along with multi protocol support
actively request for the QoS information whereas the later that can bridge various interfaces, thereby removing the
utilizes a monitoring engine that runs on the service difficulty of integrating mobile and wireless sensor network
provider side as a middleware or acts as a central proxy. Lin clients with this framework. The clients submit their
et.al.[11] have formalized the service selection for a Web requests to the framework and during runtime the

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 5, August 2010
framework will select on behalf of the clients the most regular Internet link. In case of failure of any of the link the
appropriate service available or its equivalent compatible other takes control over thereby offering the client a reliable
service depending on the QOS parameters. As shown in Fig. connection path. The route redirector acts as a cache and
1, the modules of the proposed framework include a Service redirection router in case of any link failure to re-route the
Controller (SC), reply through any of the available clients. Since all the
clients are interlinked together, the reply for Client-2 can be
redirected and pushed through Client-4 in case of failure of
connectivity between Client-2 and the server.

Service Service Service Service
Client -1 Processor (SP) Dispatcher(SD) Provider -1
Controller (SC)
FBN Agent
Service
Client -2 Connection Manager Provider -2
QoS / QoE Monitor Service
Client -3 Admission Authority Service Provider -3
Registry (SR) Service
Client -n Route Redirector
Provider -4
QoS QoE

Fig.1. Proposed Framework

Service Processor (SP), Service Registry (SR) and a The service dispatcher (SD) is responsible for the transfer of
Service dispatcher (SD) which holds a QoS and QoE request from the service consumer to the service provider. It
repository. All the requests from the clients are routed makes use of the Fuzzy-Bayesian Network algorithm for
through this framework to the services located in the efficient judging of the QoS and QoE values. These values
servers. The clients search for the available services through are collected from the repositories and continuously
this framework, get the WSDL and start communicating monitored for the dynamic changes. The services are ranked
with the desired service. The role of SP is for registering and accordingly and the service requests from the clients will be
configuring all the available and compatible services from directed to the available provider for service invocation.
various service providers. Contractual relationships are
established with the service providers and after due testing, 4. Fuzzy-Bayesian Network Approach
the services are registered and configured into this
framework. All the available services are stored in SR that Fuzzy sets were derived from the concepts proposed by
acts as a private UDDI registry. The SR also holds L.A. Zadeh. Bayesian network is a model of uncertain
additional information like the number of retries in case of a knowledge representation and reasoning based on the
service access failure and the list of equivalent services probability and graph theory and was proposed by J. Pearl
available with other service providers. [15].Fuzzy Bayesian networks (FBN) represent a machine
learning model comprising of variables which are
The SC acts as an admission authority that blocks the simultaneously fuzzy and uncertain. A FBN is a Bayesian
unauthorized SOAP messages from the clients. The clients network which has fuzzy variables [16]. In the proposed
authenticate themselves by their physical hardware address work, Y represents the services and X represents the QoS
which has been previously registered with the SC. The list metrics. By the Bayesian equation [17], if X and Y are
of all registered clients and their respective hardware events, and P(Y)>0, then the conditional probability of X ,
addresses are stored in the SC which are validated during given Y is,
service requests and only authorized clients are permitted to P ( XY )
invoke service requests. The SC also holds a connection P( X ¦ Y) =
manager which holds a multiple connection links with the P (Y )
client routed through different Internet Service Providers P ( XY ) = P (Y ) P ( X ¦ Y) = P(X)P(Y ¦ X)
(ISPs). In our experimental study each client is connected Assume that {Yi, i € n}is a countable unit of events. Let Y
with three different links, a Virtual Private Network(VPN) be another event and suppose that we know P(Yi) and
link, a Multi Protocol Labeled Switching (MPLS) link and a

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 5, August 2010
P(X│Yi) for each i ∈ n. Accordingly the total probability
formula is, 6. Conclusion
n
P(Y)= ∑ P(Y ) P( X ¦ Y )
i =1
i i The quality and eventually the performance of a SOA
system depends upon the selection of a suitable service from
Bayesian equation is the available services after giving due consideration on the
P ( X jYi ) QoS requirements. Since the components of a typical SOA
P (Yi ¦ X j ) = application are built by integration from various service
P( X j ) providers in a distributed environment, the QoS
P (Yi ) P ( X j ¦ Yi ) requirements change in runtime. In fact the quality
P (Yi ¦ X j ) = n

∑ P(Y ) P( X
i −1
i j ¦ Yi )
for i=1 to n, j=1 to m
Assuming that E1, E2, ….. En are events in the experiment,
the multiplication rule of probability is given by
P ( E1 ) P ( E2 ¦ E1 ).....P ( E N ¦ E1 , E2 ,....E N −1 ) .
If the prior probability P(Yi ) and the prior conditional
probability P(Xj │Yi) is known then, P(Yi │Xj)can be
derived from the Bayesian Equation. According to the
derivations of the fuzzy subset [18,19,20,21], any function
µ Ã which maps the domain U to the closed interval [0,1],
(µ Ã:U→[0,1]) ascertains a fuzzy subset Ã. The function µ Ã
is a membership function of the fuzzy subset and so µ Ã (u)
is the membership grade of u for Ã and its scalar probability Fig. 2. FBN Rules
is:
∼ also relies on the underlying support systems and on the
P ( A) = ∑ x ∈ xµ A ( x) P ( x) .
∼ network resources. In this paper we analyzed the Web
Using the above equation the fuzzy Bayesian equation can services selection based on the Fuzzy-Bayesian Network
be expressed as, model and the experimental results showed a considerable
∼ ∼
increase in performance and reliability. This work may open
P(Y ¦ X ) = ∑∑ µ ∼ (Yi )µ ∼ ( X j ) P( X j ¦ Yi ) P ( X i ) P( X ) up the doors towards a broader analysis which may include
i∈I j ∈J Y X
many other approaches along with FBN model to improve
the selection of Web services.
5. Implementation and Experimental Results
References
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Vol. 8, No. 5, August 2010
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