Performance Prediction of Service-Oriented Architecture - A survey
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The paper presents a comprehensive survey on performance prediction methods for service-oriented architecture (SOA) applications, focusing on the limitations of traditional performance models and assessing various approaches based on performance metrics. Key performance metrics analyzed include response time, throughput, and resource utilization, with a categorization of existing prediction methodologies according to their effectiveness and applicable project stages. The survey aims to provide guidance for SOA architects in selecting appropriate performance evaluation methods and aids researchers in identifying areas for future improvement.
![International Journal of Computer Applications Technology and Research
Volume 3– Issue 12, 831 - 835, 2014, ISSN:- 2319–8656
www.ijcat.com 831
Performance Prediction of Service-Oriented Architecture
- A survey
Haitham A.Moniem,
College of Graduate Studies,
Sudan University of Science and Technology,
Khartoum, Sudan
Hany H Ammar,
Lane Department of Computer Science and
Electrical Engineering,
College of Engineering and Mineral Resources,
West Virginia University
Morgantown, USA
Abstract: Performance prediction and evaluation for SOA based applications assist software consumers to estimate their applications
based on service specifications created by service developers. Incorporating traditional performance models such as Stochastic Petri
Nets, Queuing Networks, and Simulation present drawbacks of SOA based applications due to special characteristics of SOA such as
lose coupling, self-contained and interoperability. Although, researchers have suggested many methods in this area during last decade,
none of them has obtained popular industrial use. Based on this, we have conducted a comprehensive survey on these methods to
estimate their applicability. This survey classified these approaches according to their performance metrics analyzed, performance
models used, and applicable project stage. Our survey helps SOA architects to select the appropriate approach based on target
performance metric and researchers to identify the SOA state-of-art performance prediction.
Keywords: Service; Service-Oriented Architecture; Performance; Prediction; Evaluation
1. INTRODUCTION
Service-Oriented Architecture (SOA) is an architectural style
as well as technology of delivering services to either users or
other services through a network. SOA architecture created in
order to satisfy business goals that include easy and flexible
integration with other systems. SOA has many advantages
such as reducing development costs, creative services to
customers, and agile deployment [1].
There are many definitions for SOA, but they are all point to
the same core idea that SOA is simply a collection of
application services. The service defined as “a function or
some processing logic or business processing that well-
defined, self-contained, and does not depend on the context or
state of other services” [2]. It also states that “Generally SOA
can be classified into two terms: Services and Connectors.”
Open Management Group (OMG) defines SOA as: “an
architectural style that supports service orientation”. It goes
further to define service orientation, “service orientation is a
way of thinking in terms of services and services-based
development and the outcomes of services”. Moreover, SOA
is communication between services and applications which
sometimes involves data transfer. But the communication
between applications does not happen as a point-to-point
interaction; instead it happens through a platform-
independent, general purpose middle-ware that handles all
communications by the use of web services [2].
The main goal of this paper is to report a detail survey in
performance prediction of SOA. Section 2 lays the important
concepts of SOA. Section 3 explains by a diagram an example
of SOA base application and how the system exchanges the
messages. Section 4 presents the performance metrics of SOA
based applications. We considered three important metrics
which are response time, throughput, and resource utilization.
Section 5 summarizes the previous work in table 1 mentioning
the date of published paper, the name of authors, objectives,
performance metrics, performance model, and applicability
stage. Section 5 concludes the paper.
2. SERVICE-ORIENTED
ARCHITECTURE CONCEPTS
This part briefly tries to describe some important concepts
related to SOA.
2.1 Enterprise Service Bus (ESB)
An ESB is a standard infrastructure that combines messaging,
web services, data transformation, and intelligent routing in a
highly distributed and different environment [7] [9].
2.2 Business Process Execution Language
(BPEL)
BPEL is a language for designing SOA based systems. It
contains a lot of facilities such as web services composition,
publishing available services, organizing service execution,
and handling exceptions.
2.3 ACME
ACME is a generic language for describing software
architecture. It presents constructs for describing systems as
graphs of components interacting through connectors [11].
3. EXAMPLE
Figure 1, present an example of SOA architecture. The
example will explain in steps the requests and responses flow
between service provider, service consumer, and the directory.
Step 1 Service provider publishes its service description on a
directory, step 2 Consumer performs queries to the directory
to locate a service and find out to communicate with the
provider, step 3 Service description is written in a special
language called Web Service Description Language (WSDL),
step 4 Messages are sent and received from the directory in a
special language called Simple Object Access Protocol
(SOAP), step 5 Consumer formulate its message to the
provider using tag based language called Extensible Markup
Language (XML). The message is generated in XML but it is](https://image.slidesharecdn.com/ijcatr03121016-150111091912-conversion-gate01/85/Performance-Prediction-of-Service-Oriented-Architecture-A-survey-1-320.jpg)
![International Journal of Computer Applications Technology and Research
Volume 3– Issue 12, 831 - 835, 2014, ISSN:- 2319–8656
www.ijcat.com 832
based on specifications defined in WSDL, step 6 the response
generated by the provider is also in tag based XML format.
Figure. 1 Example of SOA Architecture
Based on ISO 9126 performance metrics are response time,
throughput, and resource utilization [12]. Therefore, accurate
measuring of SOA application plays an important role to
business success. If the application has an efficient
performance, this will lead to high productivity, well
hardware utilization, and customer satisfaction. Otherwise,
SOA based application capability will have limited benefits,
resource wasting, low productivity, and unsatisfied customer.
Applying performance to SOA applications one of the
challenging non-functional quality attribute, this because of
the physical geographic distribution of services,
communication overhead, use of standard message format,
and varying service workload [3]. Performance evaluation and
analysis differs in each situation of SOA based application.
However, previous works on service performance are not
accurate and practical enough to effectively understand and
diagnose the reasons behind performance degradation.
4. SERVICE-ORIENTED
ARCHITECTURE PERFORMANCE
METRICS
4.1 Service Response Time
Service Response Time is the measure of the time between the
end of a request to a service and the beginning of the time
service provider response. There are many considerations to
measure service response time [4] as Figure 2 stated. The
main reasons that cause low performance of SOA based
applications are:
o Services provider and service requester are
positioned at different geographical areas, mostly
at different machines.
o The potential problems of XML which is the
standard message format increases the time needed
to process a request.
o The time needed to discover the services through
the directory either in design time or run time.
o Rules that govern services contain a business
process by business process’s need.
o Adaptation of service composition by adding new
service or adapting existing services.
o Think time is an elapsed time between the end of a
response time generated by a service and the
beginning of an end user’s request [4].
4.2 Throughput
Throughput defined as the number of requests SOA
application can process at a given period of time. There are
two metrics for throughput; throughput of a service and
throughput of a business process [4] as Figure 3 stated.
The value range of these two metrics service throughput and
business process throughput must be greater than zero. The
higher the values indicate a better SOA application
performance.
4.3 Resource Utilization
To analyze the performance of SOA based applications in
terms of resource utilization, there are three basics
information needed: firstly, workload information, which
consists of concurrent users, and request arrival rates.
Secondly, software specification, which consists of execution
path, components to be executed, and the protocol of
contention used by the software [5]. Finally, environmental
information, this information consists of system specification
such as configuration and device service rates, and scheduling
policies.
Directory
Service Description
Service
Provider
Service
Consumer
WSDL
SOAP SOAP
(XML) Service Request
(XML) Service Response
(Register)
(Publish)
(Find)
(Locate)
(Bind)
(Execute)](https://image.slidesharecdn.com/ijcatr03121016-150111091912-conversion-gate01/85/Performance-Prediction-of-Service-Oriented-Architecture-A-survey-2-320.jpg)
![International Journal of Computer Applications Technology and Research
Volume 3– Issue 12, 831 - 835, 2014, ISSN:- 2319–8656
www.ijcat.com 834
5. SOA PREDICTION AND
EVALUATION APPROACHES
Several approaches have been created to evaluate and predict
SOA based application performance. In the following we
provide summaries of SOA performance prediction
approaches in the scope of the survey. We have divided the
approaches on seven columns such as author name and year of
publication, main objective, prediction approach, analyzed
metrics, performance model, validation method, and
applicable project stage.
Table 1. Comparison of Several Prediction Approaches
Author Name/
Year
Main Objective Approach used Metrics Analyzed Performance
Model
Method’s
Validation
Applicable
Project Stage
Kounev,
Samuel, et al.
[6], 2010
Designing systems
with build-in self-
aware performance
and resource
management
capabilities
Use dynamic
architecture-level
performance
model at run-time
for online
performance and
resource
management
Response time and
Resource
utilization
Queuing Petri net
Model
Compared with
PCM model result
Runtime
Liu, et al. [7],
2007
Develop a
performance
model for
predicting runtime
performance based
on COTS ESB
(Enterprise Service
Bus)
Measure primitive
performance
overheads of
service routing
activities in the
ESB
Throughput and
response time
Queuing Network
Model
Compared with the
results of Microsoft
Web Stress Tool
Runtime
Tribastone, et al.
[8], 2010
Present a method
for performance
predication of
SOA at early stage
of development.
Modeling the
system using
UML and two
profiles,
UML4SOA, and
MARTE
Response time,
Processor
utilization
Layered Queuing
Network
Model
Compared with
Mobile Payment
case study
performance result
Design time
Teixeira, et al.
[9], 2009
Propose approach
to estimate
performance of
SOA
The model uses
Petri Net
formalism to
represent the
process and
estimate its
performance
using simulation.
Resource
consumption,
Service levels
degradation
Stochastic Petri
Nets
Model
Compared with
(Rud et al)
Analytical
Method and values
from real
applications
Design time
Punitha, et al.
[11], 2008
Developing an
architectural
performance
model for SOA
Building and
measuring the
performance
model using
ACME language
Response time,
throughput, load
capacity, heavily
loaded
components.
Queuing Network
Model
Prototype SOA
Application has
been implemented
and measured
Design time
Brüseke, et al.
[12], 2014
Developing
PBlaman
(Performance
Blame Analysis )
Comparing the
observed response
time of each
component in a
failed test case to
expected response
time from the
contract
Response time Palladio
Component Model
(PCM)
Applied on two case
studies
Design time
Reddy, et al.
[13], 2011
Modeling Web
Service using
UML
Simulate the
model using
Simulation of
Multi-tiered
Queuing
Applications
(SMTQA)
Response time and
Server utilization
SMTQA Model Applied on case
study
Design time](https://image.slidesharecdn.com/ijcatr03121016-150111091912-conversion-gate01/85/Performance-Prediction-of-Service-Oriented-Architecture-A-survey-4-320.jpg)
![International Journal of Computer Applications Technology and Research
Volume 3– Issue 12, 831 - 835, 2014, ISSN:- 2319–8656
www.ijcat.com 835
Marzolla, et al.
[14],
2007
Present a multi-
view approach for
performance
prediction of SOA
based applications
for users and
providers
Approach for
performance
assessment of
Web Service
workflows
described using
annotated BPEL
and WSDL
specification
Response time and
throughput
Queuing Network
Model
Prototype tool called
bpe12qnbound
Both Design
time and Run
time
6. CONCLUSION
We have surveyed the state-of-art in the research of
performance prediction methods for service-oriented
architecture based applications. The survey categorized the
approaches according to the performance metrics analyzed,
performance model, method validation, and approach
applicable stage.
The field of performance evaluation and prediction for
service-oriented architecture based application has been
developed and matured over the last decade. Many tools and
ideas have been implemented as good software engineering
practice and should lead the creation of new approaches.
Our survey helps both architects and researchers. Architects
can obtain a complete view of the performance evaluation and
prediction approaches proposed to transfer them to industry,
on the other hand researchers can align themselves with the
proposed approaches and add more features in the future to
enhance and enrich the area.
7. REFERENCES
[1] Bianco, P., Kotermanski, R., & Merson, P. F. (2007).
Evaluating a service-oriented architecture.
[2] Krafzig, D., Banke, K., & Slama, D. (2005). Enterprise
SOA: service-oriented architecture best practices.
Prentice Hall Professional.
[3] Erl, T. (2004). Service-Oriented Architecture. Concepts,
Technology, and Design. Tavel, P. 2007 Modeling and
Simulation Design. AK Peters Ltd.
[4] Her, J. S., Choi, S. W., Oh, S. H., & Kim, S. D. (2007,
October). A framework for measuring performance in
service-oriented architecture. In Next Generation Web
Services Practices, 2007. NWeSP 2007. Third
International Conference on (pp. 55-60). IEEE.
[5] Abowd, G., Bass, L., Clements, P., Kazman, R., &
Northrop, L. (1997).Recommended Best Industrial
Practice for Software Architecture Evaluation (No.
CMU/SEI-96-TR-025). CARNEGIE-MELLON UNIV
PITTSBURGH PA SOFTWARE ENGINEERING
INST.
[6] Kounev, S., Brosig, F., Huber, N., & Reussner, R. (2010,
July). Towards self-aware performance and resource
management in modern service-oriented systems.
In Services Computing (SCC), 2010 IEEE International
Conference on(pp. 621-624). IEEE.
[7] Liu, Y., Gorton, I., & Zhu, L. (2007, July). Performance
prediction of service-oriented applications based on an
enterprise service bus. In Computer Software and
Applications Conference, 2007. COMPSAC 2007. 31st
Annual International(Vol. 1, pp. 327-334). IEEE.
[8] Tribastone, M., Mayer, P., & Wirsing, M. (2010).
Performance prediction of service-oriented systems with
layered queueing networks. In Leveraging Applications
of Formal Methods, Verification, and Validation (pp. 51-
65). Springer Berlin Heidelberg.
[9] Teixeira, M., Lima, R., Oliveira, C., & Maciel, P. (2009,
October). Performance evaluation of service-oriented
architecture through stochastic Petri nets. InSystems,
Man and Cybernetics, 2009. SMC 2009. IEEE
International Conference on (pp. 2831-2836). IEEE.
[10] Balsamo, S., Mamprin, R., & Marzolla, M. (2004).
Performance evaluation of software architectures with
queuing network models. Proc. ESMc, 4.
[11] Punitha, S., & Babu, C. (2008, September). Performance
prediction model for service oriented applications.
In High Performance Computing and Communications,
2008. HPCC'08. 10th IEEE International Conference
on (pp. 995-1000). IEEE.
[12] Brüseke, F., Wachsmuth, H., Engels, G., & Becker, S.
(2014). PBlaman: performance blame analysis based on
Palladio contracts. Concurrency and Computation:
Practice and Experience.
[13] Reddy, C. R. M., Geetha, D. E., Srinivasa, K. G., Kumar,
T. S., & Kanth, K. R. (2011). Predicting performance of
web services using SMTQA. International Journal of
Computer Science Information Technology, 1(2), 58-66.
[14] Marzolla, M., & Mirandola, R. (2007). Performance
prediction of web service workflows. In Software
Architectures, Components, and Applications (pp. 127-
144). Springer Berlin Heidelberg.](https://image.slidesharecdn.com/ijcatr03121016-150111091912-conversion-gate01/85/Performance-Prediction-of-Service-Oriented-Architecture-A-survey-5-320.jpg)
Performance Prediction of Service-Oriented Architecture - A survey
- 1. International Journal of Computer Applications Technology and Research Volume 3– Issue 12, 831 - 835, 2014, ISSN:- 2319–8656 www.ijcat.com 831 Performance Prediction of Service-Oriented Architecture - A survey Haitham A.Moniem, College of Graduate Studies, Sudan University of Science and Technology, Khartoum, Sudan Hany H Ammar, Lane Department of Computer Science and Electrical Engineering, College of Engineering and Mineral Resources, West Virginia University Morgantown, USA Abstract: Performance prediction and evaluation for SOA based applications assist software consumers to estimate their applications based on service specifications created by service developers. Incorporating traditional performance models such as Stochastic Petri Nets, Queuing Networks, and Simulation present drawbacks of SOA based applications due to special characteristics of SOA such as lose coupling, self-contained and interoperability. Although, researchers have suggested many methods in this area during last decade, none of them has obtained popular industrial use. Based on this, we have conducted a comprehensive survey on these methods to estimate their applicability. This survey classified these approaches according to their performance metrics analyzed, performance models used, and applicable project stage. Our survey helps SOA architects to select the appropriate approach based on target performance metric and researchers to identify the SOA state-of-art performance prediction. Keywords: Service; Service-Oriented Architecture; Performance; Prediction; Evaluation 1. INTRODUCTION Service-Oriented Architecture (SOA) is an architectural style as well as technology of delivering services to either users or other services through a network. SOA architecture created in order to satisfy business goals that include easy and flexible integration with other systems. SOA has many advantages such as reducing development costs, creative services to customers, and agile deployment [1]. There are many definitions for SOA, but they are all point to the same core idea that SOA is simply a collection of application services. The service defined as “a function or some processing logic or business processing that well- defined, self-contained, and does not depend on the context or state of other services” [2]. It also states that “Generally SOA can be classified into two terms: Services and Connectors.” Open Management Group (OMG) defines SOA as: “an architectural style that supports service orientation”. It goes further to define service orientation, “service orientation is a way of thinking in terms of services and services-based development and the outcomes of services”. Moreover, SOA is communication between services and applications which sometimes involves data transfer. But the communication between applications does not happen as a point-to-point interaction; instead it happens through a platform- independent, general purpose middle-ware that handles all communications by the use of web services [2]. The main goal of this paper is to report a detail survey in performance prediction of SOA. Section 2 lays the important concepts of SOA. Section 3 explains by a diagram an example of SOA base application and how the system exchanges the messages. Section 4 presents the performance metrics of SOA based applications. We considered three important metrics which are response time, throughput, and resource utilization. Section 5 summarizes the previous work in table 1 mentioning the date of published paper, the name of authors, objectives, performance metrics, performance model, and applicability stage. Section 5 concludes the paper. 2. SERVICE-ORIENTED ARCHITECTURE CONCEPTS This part briefly tries to describe some important concepts related to SOA. 2.1 Enterprise Service Bus (ESB) An ESB is a standard infrastructure that combines messaging, web services, data transformation, and intelligent routing in a highly distributed and different environment [7] [9]. 2.2 Business Process Execution Language (BPEL) BPEL is a language for designing SOA based systems. It contains a lot of facilities such as web services composition, publishing available services, organizing service execution, and handling exceptions. 2.3 ACME ACME is a generic language for describing software architecture. It presents constructs for describing systems as graphs of components interacting through connectors [11]. 3. EXAMPLE Figure 1, present an example of SOA architecture. The example will explain in steps the requests and responses flow between service provider, service consumer, and the directory. Step 1 Service provider publishes its service description on a directory, step 2 Consumer performs queries to the directory to locate a service and find out to communicate with the provider, step 3 Service description is written in a special language called Web Service Description Language (WSDL), step 4 Messages are sent and received from the directory in a special language called Simple Object Access Protocol (SOAP), step 5 Consumer formulate its message to the provider using tag based language called Extensible Markup Language (XML). The message is generated in XML but it is
- 2. International Journal of Computer Applications Technology and Research Volume 3– Issue 12, 831 - 835, 2014, ISSN:- 2319–8656 www.ijcat.com 832 based on specifications defined in WSDL, step 6 the response generated by the provider is also in tag based XML format. Figure. 1 Example of SOA Architecture Based on ISO 9126 performance metrics are response time, throughput, and resource utilization [12]. Therefore, accurate measuring of SOA application plays an important role to business success. If the application has an efficient performance, this will lead to high productivity, well hardware utilization, and customer satisfaction. Otherwise, SOA based application capability will have limited benefits, resource wasting, low productivity, and unsatisfied customer. Applying performance to SOA applications one of the challenging non-functional quality attribute, this because of the physical geographic distribution of services, communication overhead, use of standard message format, and varying service workload [3]. Performance evaluation and analysis differs in each situation of SOA based application. However, previous works on service performance are not accurate and practical enough to effectively understand and diagnose the reasons behind performance degradation. 4. SERVICE-ORIENTED ARCHITECTURE PERFORMANCE METRICS 4.1 Service Response Time Service Response Time is the measure of the time between the end of a request to a service and the beginning of the time service provider response. There are many considerations to measure service response time [4] as Figure 2 stated. The main reasons that cause low performance of SOA based applications are: o Services provider and service requester are positioned at different geographical areas, mostly at different machines. o The potential problems of XML which is the standard message format increases the time needed to process a request. o The time needed to discover the services through the directory either in design time or run time. o Rules that govern services contain a business process by business process’s need. o Adaptation of service composition by adding new service or adapting existing services. o Think time is an elapsed time between the end of a response time generated by a service and the beginning of an end user’s request [4]. 4.2 Throughput Throughput defined as the number of requests SOA application can process at a given period of time. There are two metrics for throughput; throughput of a service and throughput of a business process [4] as Figure 3 stated. The value range of these two metrics service throughput and business process throughput must be greater than zero. The higher the values indicate a better SOA application performance. 4.3 Resource Utilization To analyze the performance of SOA based applications in terms of resource utilization, there are three basics information needed: firstly, workload information, which consists of concurrent users, and request arrival rates. Secondly, software specification, which consists of execution path, components to be executed, and the protocol of contention used by the software [5]. Finally, environmental information, this information consists of system specification such as configuration and device service rates, and scheduling policies. Directory Service Description Service Provider Service Consumer WSDL SOAP SOAP (XML) Service Request (XML) Service Response (Register) (Publish) (Find) (Locate) (Bind) (Execute)
- 3. International Journal of Computer Applications Technology and Research Volume 3– Issue 12, 831 - 835, 2014, ISSN:- 2319–8656 www.ijcat.com 833 Figure. 2 Sub-metrics of SOA Response Times Figure. 3 Sub-metrics of SOA Throughput Figure. 4 Sub-metrics of SOA Resource Utilization Service Response Time Business Process Transmissio n Time Business Process Processing Time Business Process Request Message Processing Time Business Process Execution Waiting Time Service Discovery Time Service Adaptation Time Service Composition Time Business Process Logic Execution Time Service Request Transmission Time Service Processing Time Service Request Message Processing Time Service Execuation Waiting Time Service Logic Execuation Time Secondary Storage Requst Transmission Time Secondary Stoarge Processing Time Secondary Stoarge Response Transmission Time Service Response Message Processing Time Service Response Transmission Time Business Process Response Message Processing Time Business Process Response Time Throughput Service Throughput Business Process Throughput Resorce Utlization CPU Usage Input/Output Activity Communication Devices Memeory Uage Number of Database Calls
- 4. International Journal of Computer Applications Technology and Research Volume 3– Issue 12, 831 - 835, 2014, ISSN:- 2319–8656 www.ijcat.com 834 5. SOA PREDICTION AND EVALUATION APPROACHES Several approaches have been created to evaluate and predict SOA based application performance. In the following we provide summaries of SOA performance prediction approaches in the scope of the survey. We have divided the approaches on seven columns such as author name and year of publication, main objective, prediction approach, analyzed metrics, performance model, validation method, and applicable project stage. Table 1. Comparison of Several Prediction Approaches Author Name/ Year Main Objective Approach used Metrics Analyzed Performance Model Method’s Validation Applicable Project Stage Kounev, Samuel, et al. [6], 2010 Designing systems with build-in self- aware performance and resource management capabilities Use dynamic architecture-level performance model at run-time for online performance and resource management Response time and Resource utilization Queuing Petri net Model Compared with PCM model result Runtime Liu, et al. [7], 2007 Develop a performance model for predicting runtime performance based on COTS ESB (Enterprise Service Bus) Measure primitive performance overheads of service routing activities in the ESB Throughput and response time Queuing Network Model Compared with the results of Microsoft Web Stress Tool Runtime Tribastone, et al. [8], 2010 Present a method for performance predication of SOA at early stage of development. Modeling the system using UML and two profiles, UML4SOA, and MARTE Response time, Processor utilization Layered Queuing Network Model Compared with Mobile Payment case study performance result Design time Teixeira, et al. [9], 2009 Propose approach to estimate performance of SOA The model uses Petri Net formalism to represent the process and estimate its performance using simulation. Resource consumption, Service levels degradation Stochastic Petri Nets Model Compared with (Rud et al) Analytical Method and values from real applications Design time Punitha, et al. [11], 2008 Developing an architectural performance model for SOA Building and measuring the performance model using ACME language Response time, throughput, load capacity, heavily loaded components. Queuing Network Model Prototype SOA Application has been implemented and measured Design time Brüseke, et al. [12], 2014 Developing PBlaman (Performance Blame Analysis ) Comparing the observed response time of each component in a failed test case to expected response time from the contract Response time Palladio Component Model (PCM) Applied on two case studies Design time Reddy, et al. [13], 2011 Modeling Web Service using UML Simulate the model using Simulation of Multi-tiered Queuing Applications (SMTQA) Response time and Server utilization SMTQA Model Applied on case study Design time
- 5. International Journal of Computer Applications Technology and Research Volume 3– Issue 12, 831 - 835, 2014, ISSN:- 2319–8656 www.ijcat.com 835 Marzolla, et al. [14], 2007 Present a multi- view approach for performance prediction of SOA based applications for users and providers Approach for performance assessment of Web Service workflows described using annotated BPEL and WSDL specification Response time and throughput Queuing Network Model Prototype tool called bpe12qnbound Both Design time and Run time 6. CONCLUSION We have surveyed the state-of-art in the research of performance prediction methods for service-oriented architecture based applications. The survey categorized the approaches according to the performance metrics analyzed, performance model, method validation, and approach applicable stage. The field of performance evaluation and prediction for service-oriented architecture based application has been developed and matured over the last decade. Many tools and ideas have been implemented as good software engineering practice and should lead the creation of new approaches. Our survey helps both architects and researchers. Architects can obtain a complete view of the performance evaluation and prediction approaches proposed to transfer them to industry, on the other hand researchers can align themselves with the proposed approaches and add more features in the future to enhance and enrich the area. 7. REFERENCES [1] Bianco, P., Kotermanski, R., & Merson, P. F. (2007). Evaluating a service-oriented architecture. [2] Krafzig, D., Banke, K., & Slama, D. (2005). Enterprise SOA: service-oriented architecture best practices. Prentice Hall Professional. [3] Erl, T. (2004). Service-Oriented Architecture. Concepts, Technology, and Design. Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd. [4] Her, J. S., Choi, S. W., Oh, S. H., & Kim, S. D. (2007, October). A framework for measuring performance in service-oriented architecture. In Next Generation Web Services Practices, 2007. NWeSP 2007. Third International Conference on (pp. 55-60). IEEE. [5] Abowd, G., Bass, L., Clements, P., Kazman, R., & Northrop, L. (1997).Recommended Best Industrial Practice for Software Architecture Evaluation (No. CMU/SEI-96-TR-025). CARNEGIE-MELLON UNIV PITTSBURGH PA SOFTWARE ENGINEERING INST. [6] Kounev, S., Brosig, F., Huber, N., & Reussner, R. (2010, July). Towards self-aware performance and resource management in modern service-oriented systems. In Services Computing (SCC), 2010 IEEE International Conference on(pp. 621-624). IEEE. [7] Liu, Y., Gorton, I., & Zhu, L. (2007, July). Performance prediction of service-oriented applications based on an enterprise service bus. In Computer Software and Applications Conference, 2007. COMPSAC 2007. 31st Annual International(Vol. 1, pp. 327-334). IEEE. [8] Tribastone, M., Mayer, P., & Wirsing, M. (2010). Performance prediction of service-oriented systems with layered queueing networks. In Leveraging Applications of Formal Methods, Verification, and Validation (pp. 51- 65). Springer Berlin Heidelberg. [9] Teixeira, M., Lima, R., Oliveira, C., & Maciel, P. (2009, October). Performance evaluation of service-oriented architecture through stochastic Petri nets. InSystems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on (pp. 2831-2836). IEEE. [10] Balsamo, S., Mamprin, R., & Marzolla, M. (2004). Performance evaluation of software architectures with queuing network models. Proc. ESMc, 4. [11] Punitha, S., & Babu, C. (2008, September). Performance prediction model for service oriented applications. In High Performance Computing and Communications, 2008. HPCC'08. 10th IEEE International Conference on (pp. 995-1000). IEEE. [12] Brüseke, F., Wachsmuth, H., Engels, G., & Becker, S. (2014). PBlaman: performance blame analysis based on Palladio contracts. Concurrency and Computation: Practice and Experience. [13] Reddy, C. R. M., Geetha, D. E., Srinivasa, K. G., Kumar, T. S., & Kanth, K. R. (2011). Predicting performance of web services using SMTQA. International Journal of Computer Science Information Technology, 1(2), 58-66. [14] Marzolla, M., & Mirandola, R. (2007). Performance prediction of web service workflows. In Software Architectures, Components, and Applications (pp. 127- 144). Springer Berlin Heidelberg.