Ang Tan Fong

Keat Keong

Phang Ling Teck Chaw

Por Lip

CiteSeerX

Automatic Web Services Deployment 
 
 
Ang Tan Fong, Ling Teck Chaw, Phang Keat Keong, Por Lip Yee 
Department of Computer Systems and Technology 
Faculty of Computer Science and Information Technology 
University of Malaya, Malaysia 
{angtf, tchaw, kkphang, porlip}@um.edu.my 
 
 
Abstract 
 
As web service has become the emerging paradigm, 
the area of web service research has received a lot of 
attention in recent years. Most of the web services are 
deployed by site administrators. As the number of 
request of web services increase tremendously, there is 
a need to replicate the services to multiple resources. 
Although manual deployment allow the services to be 
deployed safely, it is impossible to scale. To solve this 
problem, we propose in this paper a new architecture 
for automatic web services deployment. Through the 
experiment, we proved that automatic deployment 
strategy able to handle the increasing number of users’ 
request in an effective manner.  
 
 
1. Introduction 
 
Web services technology is playing a major role in 
today’s distributed computing. Web services can be 
advertised, located and used across the Internet using 
the standard protocol like SOAP, WSDL and UDDI. 
Due to its interoperability, Web services have been 
recognized as the next generation framework for 
building distributed applications over the Internet. As 
the number of services increase, an architecture called 
Service Oriented Architecture (SOA) has been 
proposed to organize and manage the services. SOA is 
a set of principles that define an architecture that is 
loosely coupled and comprised of service providers 
and service consumers that interact according to a 
negotiated contract or interface [1]. The primary goal 
of SOA is to expose application functions in a 
standardized way so that they can be leveraged across 
multiple domains. 
Although SOA has enabled the merging of Grid 
and Web services, it provides poor support for resource 
matching. SOA provides a powerful framework for 
matching services functionalities but lack of QoS 
support. SOA selects the resources based on the web 
services functionalities without consider the non-
functional properties, such as availability, reliability, 
accessibility, cost and accuracy. Another drawback of 
SOA is that, searching and mapping is done according 
to the registered services. If the number of requests is 
more than the primary resources, the request is send to 
the waiting queue. There is no solution to deploy the 
web services dynamically to the secondary resources. 
Further more, whenever users want to deploy a new 
service, they need to ask the site administrator to 
configure the web server and setup the web service. No 
auto deployment is available and it is impossible to 
scale. 
In order to address the above problem, we propose 
in this paper a new architecture for automatic web 
services deployment. The architecture will first group 
all the relevant web services based on their 
functionalities. Then, it will select the most suitable 
resources according to the users’ QoS constraints. If all 
the resources are fully utilized, the architecture will 
automatic deploy the services to the secondary 
resources. The adaptive mechanism is used for the 
service deployment. This strategy will reduce the 
number of tasks waiting in the queue and able to 
handle the increasing number of requests efficiently. 
Besides automatic deployment, the architecture allows 
the services to be stored in repository for future uses. 
Experimental results show that the automatic 
deployment strategy able to handle the increasing 
number of users’ requests in an effective manner. 
The remainder of this paper is organized as follows. 
Section 2 provides an overview of the related work. In 
Section 3, we discuss the deployment architecture. 
Section 4 details the prototype implementation and 
describes the experiments we have conducted. Finally, 
we conclude our work in Section 5. 
 
2. Related Works 
 
2009 World Congress on Computer Science and Information Engineering
978-0-7695-3507-4/08 $25.00 © 2008 IEEE
DOI 10.1109/CSIE.2009.417
315
Recently, QoS issues of Web services have 
obtained great interest in the research community. In 
[2], method to support QoS of web service in the 
distributed domain is proposed. The paper describes 
various supporting QoS attributes for Web services. By 
using agent based approach, the QoS information is 
gathered. Then, the service is selected dynamically 
based on the consumers’ preferences and providers’ 
capabilities. In [3], the author defines a new discovery 
process that combines the functionality and QoS of 
services. The author again uses agent to handle the 
QoS measurement, evaluation and QoS information 
exchange. 
In [4], the issue of QoS driven service selection in 
dynamic Web service composition is investigated. The 
author extends OWL-S with a lightweight QoS model 
to better facilitate service selection process. The results 
show that the algorithm is time efficient, while still 
achieving satisfying optimal rate. Jingya Zhou et al.  
[5] propose an efficient algorithm in selecting the best 
resource according to users’ performance 
requirements. The algorithm can select the least cost 
composite service while satisfying end-to-end QoS 
requirements and has greater success rate. 
Besides QoS issues, various dynamic service 
deployment mechanism have been explored in the area 
of distributed systems. Daniel et al. [6] propose 
architecture for a system which allows the deployment 
of services in a group of computers, connected in a 
peer-to-peer fashion. The architecture uses publish and 
subscribe approach to detect events and perform self 
actions. Another similar approach, Snap [7] deploys 
web services over a Distributed Hash Table and creates 
replicas of a service on demand. When the demand 
reduces, the replicas are deleted. However, the research 
assumes all compute nodes are equal and able to 
execute any services. 
In [8], the authors present architecture to enable on 
demand resource provisioning. The project provides a 
Universal Factory Service (UFS) that provides a 
dynamic Grid service deployment mechanism. The 
results show that dynamic deployment can use resource 
more efficiently compared to static partitioning. Other 
similar approaches are stated in SODA [9], OGSI.NET 
[10] and DistAnt [11]. 
Gabor Kecskemeti et al. [12] propose automatic 
service deployment using virtualization. The paper 
describes an extension to the Globus Workspace 
Service [13]. Service deployment with virtualization 
can support both the on-demand deployment and the 
self-healing services. The author defines an 
infrastructure on which an automated service 
deployment solution can build on. However, the 
virtualization middleware introduces a visible overhead 
in the performance of services. Other similar works in 
virtualization include VMPlants [14] and Virtualized 
Clustering project [15]. 
Vanish et al. [16] quantitatively compare manual, 
script-based, language-based, and model-based 
deployment solutions as a function of scale, 
complexity, and susceptibility to change. The research 
concludes that if the number of deployed systems is 
small or the systems’ configurations rarely change, a 
manual solution is the most reasonable approach. 
However, in larger environments, the script-based 
solutions are well matched for large scale deployments, 
language-based for services of large complexity, and 
model-based for dynamic changes to the design. 
From the findings, we propose to use agent based 
approach for QoS collector. The resources will be 
ranked based on QoS attributes and the resources are 
dynamically selected based on users’ constraint and 
requirements. When the number of requests more than 
the registered resources, automatic service deployment 
will be executed based on modify version of Snap [7] 
that consider resources as heterogeneous nodes. As 
pilot phase, the research will mainly focus on script-
based solutions. Most of the running task will be the 
parametric services. Our work primarily differs from 
previous researches in that we will focus on adaptive 
mechanism on service selection and service 
deployment. 
 
3. Automatic Web Services Deployment 
Architecture 
 
We propose automatic web service deployment 
architecture to handle the increasing number of users’ 
request. Figure 1 depicts the architecture of the 
automatic web services deployment. As shown in the 
figure, the user first logon to the User Interface (UI). 
The user can submit a new service or request an 
existing service. If it is a new service, UI will register 
the service to UDDI server. After that, the user’s 
constraints and preferences are submitted to the UDDI 
server as XML file according to the OASIS Standard 
(WS-BPEL 2.0) [17]. 
Apart from handling user request, the UI will 
monitor the completion of the service. When the 
service is completed, the output is stored in the UI 
server and the user is notified. UI support both the 
synchronous and asynchronous submission from user. 
Another feature provided by UI is the security module. 
The UI provides an interface for authorization and 
authentication. Users need to provide their identity in 
order to use the services. 
 
316
 
Figure 1. Automatic Web Services 
Deployment Architecture 
 
The main functionality of UDDI server is to enable 
the publishing and sharing of information about web 
services. We modify the UDDI server to select the 
most suitable services based on not only the services 
functionalities but also the users’ constraints and 
preferences. A QoS registry is developed to integrate 
with the UDDI server. The QoS registry contains the 
QoS attributes for the services and resources. The 
service properties will include memory and storage 
requirements, subtask dependency in a service, 
completion time and communication volumes. When a 
new service is registered to the UDDI server, service 
profiling module is executed from time to time to 
retrieve the service properties. Besides, analogical 
benchmarking is performed to measure the service 
performances on dynamic resources. The behavior of 
the services such as static, linear and exponential can 
be analyzed from the benchmark. 
Agent is installed in all the compute resources that 
available in the architecture. The main feature of agent 
is to collect the resources performance information 
such as processing speed, number of core and number 
of concurrent request, throughput and latency, 
availability, reliability, accessibility, accuracy. The 
agent collects the information periodically from the 
resources, summarize it and store it in the UDDI 
server. 
By using the information above, the UDDI server 
can perform the selection operation. The operation 
consists of three phases namely matching, numbering 
and ranking. During the matching process, all the 
services that satisfy the functionalities and users 
constraints and preferences are selected. Then, the 
value of QoS is normalized to calculate the numerical 
result. Finally, the ranking process will rank the 
resources using different weight for different QoS 
properties. UDDI will select the resource with the best 
ranking. 
The Web services repository is the main component 
that facilitates the automatic deployment. When the 
waiting queue of UDDI server reaches certain 
threshold, the UDDI server will instruct the web 
service repository to perform automatic services 
deployment. This is happening when the web server, 
cluster and super computer unable to handle the 
increasing number of request. Without the automatic 
deployment, the administrator need to setup and 
register the web service manually. The manual process 
can not scale and is time consuming. By using the 
automatic deployment, the Web services repository 
able to select the requesting service and deploy it to the 
secondary resources. The deployment is using the 
manager worker model and no web server is required 
at each compute elements. 
Apart from its main feature, Web services 
repository can be considered as the secondary server of 
the architecture. It has the latest copy of web services 
from UDDI. It can perform analogical benchmarking 
to identify the behavior of the service when the 
secondary compute elements are idle. 
 
4. Implementation and Experiments 
 
The architecture is currently implemented on .NET 
and J2EE environment. Therefore, it can be used for 
any web service technologies. Since Web services 
manage to solve the interoperability problem, we can 
easily deploy the web services on different platforms. 
Besides the basic library of .NET and J2EE, the UDDI 
.NET SDK version 2 is used to allow the integration 
with UDDI server. In addition, we develop a QoS 
registry which contains the QoS attributes for the 
services and the resources on top of the UDDI server. 
Our implementation links the QoS information with 
the services functionalities together. 
For the automatic deployment module, no web 
server is required to install on the compute elements. 
Http.sys architecture is used as the low-level HTTP 
protocol stacks for the dynamic deployment on 
compute elements. It is a kernel-mode component that 
offers HTTP services to all applications on the 
machine. When http.sys receives a request, it can 
forward it directly to the correct worker process. 
Http.sys is capable of caching responses directly within 
the kernel. This improves the overall throughput and 
performance. Figure 2 shows the Http.sys architecture. 
 
317
 
Figure 2. Http.sys Architecture 
 
After the implementation, the architecture is tested 
with the parametric service. We have conducted a set 
of experiment using the static parametric service. Table 
1 show the resources use for the experiments. The first 
three set of resources are the primary resources while 
the last two set are the secondary resources. The 
average execution time of each service on different 
resources is shown in Table 2. 
 
Table 1. Resources 
  Resource  Core Unit Type 
1 
Server with 
CPU 2.33GHz 8 5 
Primary 
resources 
2 
Server with 
CPU 1.86GHz 8 5 
Primary 
resources 
3 
Server with 
CPU 1.86GhHz 4 5 
Primary 
resources 
4 
PC with CPU 
2.33GHz 2 20 
Secondary 
resources 
5 
PC with CPU 
2.0GHz 2 20 
Secondary 
resources 
 
Table 2. Average Execution Time 
  Resource  Core 
Average Execution 
Time (s) 
1 
Server with 
CPU 2.33GHz 8 4.6 
2 
Server with 
CPU 1.86GHz 8 6.2 
3 
Server with 
CPU 1.86GHz 4 14.6 
4 
PC with CPU 
2.33GHz 2 19.1 
5 
PC with CPU 
2.0GHz 2 22.2 
 
Figure 3 illustrate the distribution of the total 
execution time of different number of request. M1 is 
the standard architecture without the automatic 
deployment while M2 is our propose architecture that 
dynamically deploy the service to the secondary 
resources. When the number of request is small, M1 
outperform our architecture since no services waiting 
in the queue. However, when the number of request 
increase, our propose method is better. Our method can 
utilize the secondary resources efficiently and at the 
same time reduce the total execution time. 
 
 
Figure 3. Total Execution Time using two 
methods 
 
5. Conclusion 
 
Web services are suitable for implementing in the 
distributed and heterogeneous platform. However, 
more research is required to discover their potential 
benefit. This paper presents an automatic web service 
deployment that able to handle the increasing number 
of users’ request in an effective manner. The major 
contribution of this paper is the efficient usage of 
secondary resources. Since most of the research 
concentrate on primary resources, our research focus 
on the secondary resources. The secondary resources 
include those computers in the students’ lab that are 
underutilized. In addition, most of these computers 
nowadays consist of multiple cores. A partial of these 
compute resources can be shared without affect the 
resources performance. Experimental results show that 
our architecture reduce the number of tasks waiting in 
the queue and reduce the overall execution time.  
Our future work consists of providing intelligent 
scheduling, interface for self healing, and more 
advanced security features. In addition, we will test the 
architecture using the linear and exponential 
parametric services. Besides, we may consider working 
on language based and model based solutions 
deployment. 
6. Acknowledgement 
This work is partly financially supported by the 
university research grant under no PJP (FS251/2008B). 
 
7. References 
 
318
[1] Manoj Mansukhani, Service Oriented Architecture White 
Paper, HP Inc, June 2005. 
[2] Seung-Hyun Lee, Dong-Ryeol Shin, Web Service QoS in 
Multi-Domain, In 10th IEEE International Conference on 
Advanced Communication Technology (ICACT), 2008. 
[3] Mossab A. Al Hunaity, Towards an Efficient Quality 
Based Web Service Discovery Framework, IEEE 
Congress on Services, 2008. 
[4] Yan Yang, et al., An Approach to QoS-aware Service 
Selection in Dynamic Web Service Composition, In 
IEEE Third International Conference on Networking and 
Services (ICNS'07), 2007. 
[5] Jingya Zhou, et al., QoS Adaption aware Algorithm for 
Grid Service Selection, In 12th IEEE International 
Conference Computer Supported Cooperative Work in 
Design, 2008.  
[6] Daniel Lazaro, et al., An Architecture for Decentralized 
Service Deployment, In IEEE International Conference 
on Complex, Intelligent and Software Intensive Systems, 
2008. 
[7] Pairot, C., et al., Deploying Wide-Area Applications Is a 
Snap. IEEE Internet Computing, 11 (2) 72-79, 2007. 
[8] Eun-Kyu Byun, et al., A Dynamic Grid Services 
Deployment Mechanism for On-Demand Resource 
Provisioning, In IEEE International Symposium on 
Cluster Computing and the Grid, 2005. 
[9] X. Jiang and D. Xu, SODA: a Service-On-Demand 
Architecture for Application Service Hosting Utility 
Platforms, In Proceedings of the IEEE International 
Symposium on High Performance Distributed 
Computing, 2003. 
[10] G. Wasson, et al., OGSI.NET: OGSI-compliance on the 
.NET Framework, In Proceedings of the IEEE 
International Symposium on Cluster Computing and the 
Grid, 2004. 
[11] W. Goscinski and D. Abramson, Distributed Ant: A 
System to Support Application Deployment in the Grid, 
IEEE/ACM International Workshop on Grid 
Computing, 2004. 
[12] Gabor Kecskemeti, et al., Automatic Service 
Deployment Using Virtualisation, In IEEE 16th 
Euromicro Conference on Parallel, Distributed and 
Network-Based Processing, 2008. 
[13] K. Keahey, et al., Virtual workspaces in the grid. 
ANL/MCS-P1231-0205, 2005. 
[14] I. Krsul, et al., Vmplants: Providing and managing 
virtual machine execution environments for grid 
computing. In International Conference on High 
Performance Computing, Networking and Storage 
(SC04), 2004. 
[15] Javier Alonso, et al., High-Available Grid Services 
through the use of Virtualized Clustering, In 8th IEEE 
Grid Computing Conference, 2007. 
[16] Vanish Talwar, et al., Comparison of Approaches to 
Service Deployment, In Proceedings of the 25th IEEE 
International Conference on Distributed Computing 
Systems (ICSCS’05), 2005. 
[17] OASIS Standard, Web Services Business Process 
Execution Language Version 2.0,  11 April 2007. 
http://www.oasis-open.org/committees/download.php 
/23964/wsbpel-v2.0-primer.htm. 
[18], Aaron Skonnard, Run ASMX Without IIS, MSDN 
Magazine, December 2004. 
 
 
 
 
319


Automatic web services deployment

http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=71EB00B9461ED8BD578BBBC595E114E8?doi=10.1.1.1073.2888&rep=rep1&type=pdf

Automatic web services deployment

Abstract

Similar works

Full text

Available Versions

CiteSeerX