Exploratory Study of Performance Evaluation Models for Distributed Software Architecture S.O. Olabiyisi; E.O. Omidiora Department of Computer Science & Engineering Ladoke Akintola University of Technology, Ogbomoso Oyo State, Nigeria Faith-Michael Uzoka Department of Computer Science & Information Systems Mount Royal University Calgary, Canada Boluwaji A. Akinnuwesi Department of Information Technology Bells University of Technology, Ota, Ogun State Nigeria Victor W. Mbarika Mathieu Kourouma Hyacinthe Aboudja International Centre for Information Technology and Development Southern University, Baton Rouge, Louisiana, USA Department of Computer Science College of Sciences Southern University, Baton Rouge, Louisiana, USA Department of Computer Science School of Business Oklahoma City University Several models have been developed to evaluate the performance of Distributed Software Architecture (DSA) in order to avoid problems that may arise during system implementation. This paper presents a review of DSA performance evaluation models with the view of identifying the common properties of the models. It was established in this study that the existing models evaluate DSA performance using machine parameters such as processor speed, buffer size, cache size, server response time, server execution time, bus and network bandwidth size and lots of others. The models are thus classified to be machinecentric. Moreover the involvement of end users in the evaluation process is not emphasized. Software is developed in order to satisfy specific requirements of the client organization (end-users); therefore, involving users in evaluating DSA performance should not be underestimated. This study suggests future works on establishing contextual organizational variables that can be used to evaluate DSA. Also to complement the existing models, works should be done on development of user-centric performance evaluation model which will directly involve the end-users in the evaluation of DSA using the identified contextual organizational variables as parameters for evaluation. Keywords: Distributed software, Performance, Performance evaluation model, Software system architecture, Client organization, machinecentric, user-centric INTRODUCTION Today, distributed computing applications are used by many people in real time operations such as electronic commerce, electronic banking, online payment, et cetera [22]. Distributed computing is used as enabling technology for modern enterprise applications; thus in the face of globalization and ever increasing competition, Quality of Service (QoS) attributes like performance, security, reliability, scalability, and robustness are of crucial importance [29]. Companies must ensure that the distributed software (DS) they operate does not only provide all relevant functional services, but also meet the performance expectation of their customers. Therefore it becomes imperative to analyze and predict the expected performance of distributed software systems at the level of the architectural design in order to avoid the pitfalls of poor QoS during system implementation. Software architecture (SA) is a phase of software design which describes how a system is decomposed into components, how these components are interconnected, and how they communicate and interact with each other. This phase of software design is a major source of errors if the organizational structure of the different components is not carefully defined and designed. There are two parts to SA [6, 33]. The first part is the micro-architecture which covers the internal structure of the software system such as conceptual architecture, module interconnection architecture, execution architecture, and code architecture. The second part of SA is the macro-architecture that focuses on external factors that could influence the design and implementation of the software system. Examples of the external factors are: culture and belief of people (users), government policies and regulations, and disposition of people towards the use of computer. SA is an important phase in the software life cycle as it is the earliest point and highest level of abstraction at which useful analysis of a software system is possible [35]. Hence, performance analysis at this level can be useful to establish whether a proposed architecture satisfies the end users’ requirements and also meets the desired performance specifications. It also helps to identify eventual errors and verify that the quality requirements have been addressed in the design and thus saving major potential modifications later in the software development life cycle or tuning the system after deployment.. SA is considered the first product in an architecture-based development process and evaluation at this level should reveal requirement conflicts and incomplete design descriptions from stakeholders’ perspective [6]. Performance of software is a quality attribute that is measured in any of the following metrics: system throughput, responsiveness, resource utilization, turnaround time, latency, failure rate, and fault tolerance. Thus, assessing and optimizing system performance is essential for the smooth and efficient operation of the software system. There are several approaches for evaluating the performance of system architecture. One of the earliest approaches is the fix-it-later approach [3] which advocates software correctness and deferring performance considerations to the integration testing phase. If performance problems are detected, then, additional hardware may be needed; otherwise, the software will be tuned to correct the problems. This approach has some limitations, such as,: it takes time to acquire and install new hardware; also tuning the software takes time and could be costly. Tuning may distort the original software design and testing must be repeated after code changes. This gives a negative impression to users after it is corrected. The rational for the fix-it-later approach is to save development time and cost. This however will not be realized, if initial performance is unsatisfactory because of additional time and cost of tuning and maintenance. Also, Connie [3] proposed a DesignBased Evaluation and Prediction Technique (ADEPT), an analysis technique used in conjunction with the performance engineering discipline. ADEPT was the strategy used to combat the fix-it-later principle and supported the performance engineering process. ADEPT evaluates the performance of information system early in the life cycle using specifications for both expected resources requirement and upper bounds. The system design is likely to be stable if the performance goal is satisfied for the upper bound. ADEPT had the following challenges: lack of automatic feedback component, not robust enough to evaluate large and complex systems, inability to eliminate unwanted argument in the course of evaluation, and inability to work in concurrent processing environments. In recent years, several models have been developed to constantly evaluate the performance of DSA. The survey done in this paper provides the developments over about a decade (1999 – 2010) with the aim of identifying the parameters used by each model for evaluating DSA performance and also deduces the properties that are common to the models. Further research direction is proposed as a consequence. RELATED WORKS Many studies have been carried out on the survey of system performance evaluation models with the ultimate goal of providing recommendations for future research activities. Those activities could significantly improve the performance evaluation and prediction of software system architecture. A survey of the approaches to evaluate software performance from 1960 to 1986 was done in [4]. The study pointed out the breakthroughs leading to the software performance engineering approach (SPE) and a comprehensive methodology for constructing software to meet performance goals. The concepts, methods, tools, and use of SPE were summarized and future trends in each area were suggested. In [6] eight architecture analysis methods were reviewed with the view of discovering similarities and differences between these methods by making classifications, comparisons, and appropriateness studies. The eight methods considered are: SAAM (Scenario-Based Architecture Analysis Method), SAAMCS (SAAM Founded on Complex Scenarios), ESAAMI (Extended SAAM by Integration in the Domain), SAAMER (Software Architecture Analysis Method for Evolution and Reusability), ATAM (Architecture Trade-Off Analysis Method), SBAR (Scenario-Based Architecture Reengineering), ALPSM (Architecture Level Prediction of Software Maintenance), and SAEM (Software Architecture Evaluation Model). The authors discovered at that time that SAAM was used for different quality attributes like modifiability, performance, availability, and security. In addition SAAM was applied in several domains unlike the other methods that were undergoing refinement and improvement as at that time. As a result, some future works were proposed to evaluate the effects of their various usages and create a repeatable method based on repositories of scenarios, screening and elicitation questions. Three indications that concern software design specifications, performance models, and analysis of processes were highlighted in [31]. The following recommendations were made in the paper: the use of standard software artifacts like Unified Modeling Language (UML) diagrams for software design specifications; the existence of strong semantic mapping between software artifacts and the performance models as strategy to reduce the performance model complexity and still maintaining a meaningful semantic correspondence; use of simulation in addition to analytical simulations to address performance model complexity and provision of feedback which is a key success factor for a widespread use of performance analysis models. In [1] a review of performance prediction techniques for component-based software systems was carried out and the following recommendations were made: (1) integration of quantitative prediction techniques in software development process; (2) design of component models allowing quality prediction and building of component technologies supporting quality prediction; (3) inclusion of quality attributes such as reliability, safety or security in the software development process; and (4) study of interdependencies among the different quality attributes to determine, for example, how the introduction of performance predictability can affect other attributes such as reliability or maintainability. In [7], three foundational formal software analyses were described. The authors reviewed emerging trends in software model and identified future directions that promise to significantly improve the cost-effectiveness. CLASSIFICATION OF DSA PERFORMANCE EVALUATION MODELS This paper classifies existing performance models based on the technique used to develop the models. The techniques are: (1) Factor Analysis; (2) Queuing Network; (3) Petri net; (4) Pattern-Based; (5) Hierarchical Modelling; (6) Performance Analysis and Characterization Environment (PACE) Based; (7) Component-Based Modelling; (8) ScenarioBased; (9) Soft computing approach; (10) Relational Approach; (11) Software Architecture Analysis Methods (SAAM); (12) Aspectual Software Architecture Analysis Methods (ASAAM); (13) Hybrid Approaches such as UML-Petri net, UML-Stochastic Petri net, Queue Petri Nets Approach and Soft Computing Approach. The models are reviewed in order to establish the kind of parameters used in them to evaluate DSA. Factor Analysis (FA) Based Approach FA approach was used in [2] to develop a model for analysing Information Technology (IT) software projects with the aim of establishing the success or failure of the project before it takes off. FA as contained in SPSS and Statview software was used. Fifty performance indices of IT projects planning, execution, management, and control were formulated. Eleven factors were extracted and subjected to further analysis with a view to estimating and ranking their contribution to the success of IT projects. The model was tested using sample life data gotten using questionnaires that were administered to the principal actors of the popular IT software projects in Nigeria. The significant contribution of the research is the provision of a working model that utilized both quantitative and qualitative decision variables in assessing the success or failure of IT projects. This serves as template for evaluating IT projects prior to its implementation. This model was not used to evaluate performance of software system architecture. Queuing Network Based Models This is a conventional modelling paradigm which consists of a set of interconnected queues [28]. The models based on Queuing Networks are categorized in Table 1. Table 1 Queuing Network Based Performance Models Description of Model [30] designed and implemented objectoriented queuing network model – a reusable performance models for software artifacts. Parameters Considered Buffer size, processor speed of server, queue size, number of incoming request, request arrival time, request departure time. Class of Parameter Machine centric parameter [31] integrated performance and specification model to provide a tool for quantitative evaluation of software architecture at the design phase. [35] modeled layered software system as a closed Product Form Queuing Network (PFQN) and solve it for finding performance attributes of the system [31] proposed an approach based on queuing networks models for performance prediction of software systems at the software architecture level, specified by UML. [12] developed Software Architecture and Model Extraction (SAME) technique that extract communication patterns from executable designs or prototype that use message passing, to develop a Layered Queuing Network Performance Model in an automated fashion. Number of service centers, service rate of service center, arrival rate of requests at service centre, number of servers in service centers, routing procedure of requests, Number of request circulating in the system, physical resources available system workloads, network topology. Software process centric and machine centric parameters Range of number of clients accessing the system, average think time of each client, number of layers in the software system, relationship between the machines and software components, number of CPUs and disks on each of the machine and thread limitation (if any), uplink and downlink capacities of the connectors connecting machines running adjacent layers of the system, size of packets of the links, service time required to service one request by a software layer, forward transition probability, rating factors of the CPU and the disks of each machines in the system Same as in [35] Software and Machine centric parameters Same as in [35] Software and Machine centric parameter Petri Net Based Approach Petri nets were introduced in 1962 by Dr. Carl Adam Petri [27]. A Petri net is a graphical and mathematical modelling tool [26]. It is a directed bipartite graph with an initial state called the initial Table 2 Software and Machine centric parameters marking. Petri Nets consist of four basic elements: places, transitions, tokens, and arcs. System performance models based on Petri net approach are categorized in Table 2. Petri Net Based Performance Models Description of model [18] developed performance evaluation model for Agent-based system using petri net approach [20] did performance analysis of Internet based software retrieval systems using petri nets [13] developed stochastic petri nets model from UML activity diagrams Parameters Considered System load, system delays, system routing rate, latency of process, CPU time. Class of Parameter Machine centric parameters Network time. Machine centric parameters Routing rate, action duration, system response time. Machine centric parameters [14] translated UML activity diagram into stochastic Petri net model that allows to compute performance indices. [23] derived performance parameters from Generalized Stochastic Petri Net (GSPN) using Markov chain theory. Routing rate, action duration, system response time. Machine centric parameters Routing rate, action duration, system response time. Machine centric parameters Queuing Petri Net (QPN) Based Models The hybrid of Petri Net and Queuing Networks is Queuing Petri Nets (QPNs) which facilitates the integration of hardware and software aspects of system behaviour into the same model. In addition to hardware contention and scheduling strategies, using QPNs, one can easily model simultaneous resource possession, synchronization, blocking, and contention for software resources. Thus QPNs Table 3 combines Queuing Networks and Petri Nets into a single formalism in order to eliminating their disadvantages. QPNs allow queues to be integrated into places of Petri Nets and this enables the modeller to easily represent scheduling strategies and to bring the benefits of Queuing Networks into the world of Petri Nets [28]. System performance models based on Queuing Petri net approach are categorized in Table 3. Queuing Petri Net Based Performance Models Description of Model [28] applied QPN formalism to analyse the performance of distributed e-business system. [29] presented a novel case study of a realistic state-ofthe-art distributed component-based system, showing how the QPN modelling formalism can be exploited as software system performance prediction tool. Parameters Considered Service demand of queue, service rate of queue, token population of queue, queue size, buffer size, processor speed of server, routing rate. Same as in [28]. Performance Analysis and Characterization Environment Based Approach The motivation to develop Performance Analysis and Characterization Environment (PACE) based approach in [15] was to provide quantitative data concerning the performance of sophisticated applications running on high performance systems. The framework of PACE is a methodology based on a layered approach that separates out the software and hardware system components through the use of a parallelization template. This is a modular approach that leads to readily reusable models, which can be interchanged for experimental analysis. Each of the modules in PACE can be described at multiple levels of details thus providing a range of result accuracies, but at varying costs in terms of prediction evaluation time. PACE is aimed to be used for pre-implementation analysis, such as design or code porting activities, as well as, for onthe-fly use in scheduling systems. The core component of PACE is a performance specification 3 language, CHIP S (Characterization Instrumentation for Performance Prediction of Parallel Systems). 3 CHIP S provides a syntax that allows the description Class of Parameters Machine centric parameters Machine centric parameters of the performance aspects of an application and its parallelization to be expressed. This includes control flow information, resource usage information (for example number of operations), communication structures, and mapping information for a parallel or distributed system. The software object in the PACE system were created using the Application Characterization Tool (ACT). ACT aids the conversion of sequential or parallel source code into 3 the CHIP S language via the Stanford Intermediate Format (SUIF). ACT performs a static analysis of the code to produce the control flow of the application, count the number of operations in terms of high-level language implemented, and also the communication structure. The hardware objects of the model are created using a Hardware Model Configuration Language (HMCL) by specifying system-dependent parameters. On evaluation, the relevant sets of parameters are used and supplied to the evaluation methods for each of the component models. Hierarchical Performance Modeling Approach In [32] a Hierarchical Performance Modelling (HPM) technique for distributed systems, which incorporated different level of modelling abstraction, was presented. HPM is a technique to model performance for different layers of abstraction. It includes several layers of organization from primitive operation to software architecture, therefore, providing a degree of accuracy that cannot be achieved with single layer models. The application is developed in a top-down fashion from general to more specific, but performance information is generated in bottom-up method, thus linking the different levels of analytic models into a composite model. This approach support specification and performance model generation that incorporates computation and communication delays along with hardware profile characteristics to assist in the evaluation of performance alternatives. HPM models Table 4 provide a quantitative performance assessment of an entire system comprising of hardware, software, and communication. The HPM provided a well-defined methodology to allow system designers to evaluate the application based on the system requirements of their application and fine tune the values of performance parameters. Pattern Based Approach Design patterns are defined as description of communicating objects and classes that are customized to solve a general design problem in a particular context. The components of design pattern are: Pattern name, Intent, Motivation, Applicability, Structure, Participants, Collaborations, Consequences, Implementation, Sample code, Known uses and Related pattern. Performance models based on pattern based approach are presented in Table 4. Pattern Based Performance Models Description of Model [19] presented an approach based on patterns to develop performance models for object oriented software system in the early stages of the software development process. This complement the approach given in [18] [21] presented a pattern-based approach to model the performance of software system and used it to evaluate the performance of mobile agent system [9] presented a pattern-based performance completion for message-oriented middleware Parameters Considered Event load, time to perform an action, request arrival time, request service time, number of concurrent users Class of Parameter Software process centric parameters Same as in [19] Software process centric parameters System configuration (hardware & network components), message size (incoming & outgoing), delivery time for message, number of message sent, size of message sent, number of message delivered, size of message delivered, transaction/request size, buffer/pool size Software process centric parameters and machine centric parameters Soft Computing Approach Soft computing is an approach to computing which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision [8]. It is a consortium of methodologies centering in fuzzy logic (FL), artificial neural networks (ANN) and evolutionary computation (EC). These methodologies are complementary and synergistic, rather than competitive. They provide in one form or another flexible information processing capability for handling real life ambiguous situations. Soft computing aims to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low-cost solutions. The attributes of these models are often measured in terms linguistic values, such as very low, low, high, and very high. The imprecise nature of the attributes constitutes uncertainty and vagueness in their (subsequent) interpretation. Performance models based on soft computing approach are presented in Table 5. The advantage of Soft computing models particularly fuzzy logic and ANN are [10]: they are more general and they mimic the way in which humans interpret linguistic values and the transition from one linguistic value to a contiguous linguistic value is gradual rather than abrupt. Table 5 Performance Models Based Soft Computing Approach Description of Models [10] applied fuzzy logic to measure similarity of software projects when their attributes are described by categorical values (linguistic values in fuzzy logic) [11] presented a new technique based on fuzzy logic, linguistic quantifiers and analogy-based reasoning to estimate the cost of or effort of software projects when they are described by either numerical data or linguistic values. [17] showed how fuzzy logic can be applied to computer performance work to simplify and speed analysis and reporting. [25] Developed a fuzzy model for evaluating information system projects based on their present value using fuzzy modelling technique. Parameters Considered Seventeen parameters: software size, project mode plus 15 cost drivers. Class of Parameter Software process centric and machine centric parameters Same as in [10] Software process centric and machine centric parameters CPU Queue length, memory (RAM) available, pages input per second, read time, write time, I/Os per second. Machine parameters Three parameters representing three possible values of project costs, benefits, evaluation periods, and discount rate. Software process centric parameters Other Performance Models In [5], multivariate Adaptive Regression Splines (MARS) was used for software performance analysis. A resource function was designed and automated, having the following parameters - size of data objects, number of disk blocks to be read, size of messages to be processed, memory and cache size, processor speed, bus and network bandwidth. In [16], PASA, a method for performance assessment of software architectures, was developed and it was scenario-based. It identifies potential areas of risk within the architecture with respect to performance and other quality objectives. It identifies strategies for reducing or eliminating the risks if a problem is found. Scenario for important workloads are identified and documented. The scenarios provide means of reasoning about the performance of the software as well as other qualities and they serve as starting point for constructing performance models of the architecture. ASAAM (Aspectual Software Architecture Analysis Method) is scenario-based proposed in [34]. It introduces a set of heuristic rules that help to derive architectural aspects and the corresponding tangled architectural components from scenarios. It takes as input the architecture design and measures the impact of predefined scenarios on it in order to identify the potential risks and the sensitive points of the architecture. This helps to predict the quality of the system before it is built and therefore reducing unnecessary maintenance costs. centric In [36], performance analysis based on requirements traceability was presented. Requirement traceability is critical to providing a complete approach which will lead to an executable model for performance evaluation. The paper investigated the software architectures that are extended based on the performance requirements traceability to represent performance property. The extended architectures are then transformed into a simulation model colored Generalized Stochastic Petri Nets (GSPN) and the simulation results are used to validate performance requirements and evaluate system design. The parameters considered are queue length, number of requests to be serviced, server response time, server execution time, and processor speed. GENERAL PROPERTIES OF THE EXISTING DSA PERFORMANCE EVALUATION MODELS From survey of the existing DSA performance evaluation models, the following common attributes are identified: i. The models are algorithmic using hard computing principles. ii. Parameters for evaluation are machine centered and they are objective. For example, processor speed, bus and network bandwidth size, RAM size, cache size, server response time, server execution time, number of disk to be read and message size. Therefore the models are machine-centric. iii. The models are implemented at the architectural stage of the software life cycle. iv. Though in the existing models, the contributions of the client organization (end users) during software development process were acknowledged but none of the models draws parameters for evaluation from the contextual organizational decision variables. v. The models are re-useable and scalable. vi. Performance metrics considered are mostly the following: throughput, response time, and resource utilization. viii. The models are limited by their inability to cope with uncertainties and imprecision of data or information surrounding software projects in the early stage of the development life cycle. ix. The conceptual structures of some model (for example, probabilistic models) that can represent vague information are inadequate for dealing with problems in which information is perception-based and is expressed in linguistic form. x. The models are computationally intensive and are intolerant of noise. They cannot handle categorical data other than binary valued variables. CONCLUSION AND FUTURE WORK Conclusion In this paper, a review of research works on performance evaluation models from 1999 to 2010 is presented in order to establish the properties common to these models. It was deduced that most models for evaluating DSA performance are machine-centric. The following are some of the evaluation parameters identified: buffer size, processor speed, cache size, server response time, server execution time, number of disk block to be read, queue size, request arrival time, request departure time, bus size, network bandwidth size (uplink and down link), number of Central Processing Unit (CPU), number of request circulating in the system, system routing rate, latency of system, network time, system RAM (Random Access Memory) size, size of data object, size of message to be processed. The performance evaluation models are, therefore, classified as machine-centric models. They are established and used to evaluate DSA performance with respect to satisfying the machine and system process requirements. However subjective decision variables of users are not considered in the machine-centric models; also the models cannot cope with uncertainties and imprecision of data or information surrounding software projects in the development life cycle. Users are involved in DSA development in order to feed the software developers with the necessary organizational information. This helps the software developers to develop software system that will be accepted by end users and satisfies the organization’s requirements using available machine infrastructure. The question is “how do we measure the performance of the DSA from users’ perspectives in order to establish the extent of responsiveness of the DSA to the requirements of the client organization”. It is hoped that future research works will address this question. Future Work Management of the client organization and the end users are key players in software development process. Therefore, contextual organizational decision variables (for example: Organizational goals and task; Level of users competence/experience in Information Technology; Information requirements of users and the format; Internal service of the organization, and their relationships; The organization’s defined functions required in the user interface; Organization’s policies, rules or procedures for transaction process flow etcetera), should not be underestimated while establishing the variables to evaluate performance of software architecture. We therefore propose, as a result, that future works should identify and verify with some empirical analysis, both objective and subjective contextual organizational decision variables that could influence the choice of architectural style and design pattern made by the software developer. 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The first version is [24] that was presented in the International Conference on Computer Science and Application, World Congress on Engineering and Computer Science (WCECS 2010), San Francisco, USA, October 20 – 22, 2010. It is one of the preliminary results of an ongoing research that focuses on developing User-Centric Model to evaluate the performance of Distributed Software Architecture. Acknowledgement: This research is partly sponsored by the National Science Foundation (NSF) under Grant Nos. 1036324 and 0811453 and UNCFSP NSTI under the supervision of Dr Victor Mbarika in International Centre of Information Technology and Development, Southern University and A & M College, Baton Rouge, Louisiana, USA. Also Bells University of Technology, Ota, Ogun State, Nigeria is acknowledged for providing a partial support. About the Authors S.O. Olabiyisi, Ph.D. is a Senior Lecturer in the Department of Computer Science and Engineering, LAUTECH, Ogbomosho, Nigeria. His Research interests are Software Performance Evaluation, Computational Mathematics, Discrete Structures and Softcomputing. (e-mail:tundeolabiyisi@hotmail.com) E.O Omidiora, Ph.D. is a Senior Lecturer in the Department of Computer Science and Engineering, LAUTECH, Ogbomosho, Nigeria. His research interests are Computer Architecture, Softcomputing and e-Learning system. (e-mail: omidiorasayo@yahoo.co.uk) F.M.E. Uzoka, Ph.D. is a Faculty member in the Department of Computer Science and Information System, Mount Royal University, Calgary, Canada. He was a Senior Lecturer in Information Systems, University of Botswana. He conducted a two year postdoctoral research at the University of Calgary (2004-2005). His research interests are Software Engineering (ICSE17): 1995, pp 196207. [34] Tekinerdogan B. “ASAAM: Aspectual Software Architecture Analysis Method”, Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, Boston, USA: 2003. [35] Vibhu, S.S., Pankaj J., Kishor S.T. “Evaluating Performance Attribute of Layered Software Architecture”, CBSE 2005: Vol. 3489 of LNCS, pp 66-81. [36] Wise, J.C., Chang, C.K., Xia, J., Cleland-Huang, J. “Performance Analysis Based on Requirements Traceability”, Technical Report, Dept of Computer Science, Iowa State University, Iowa: 2005. Organizational Computing, Decision Support Systems, Technology Adoption and Innovation and Medical Informatics. He serves as a member of editorial/review board of a number of Information Systems journals/conferences (e-mail: uzokafm@yahoo.com). Boluwaji A. Akinnuwesi, Ph.D., is a Lecturer with Department of Information Technology, Bells University of Technology, Ota, Ogun State, Nigeria. He is also the Director of the Computer Centre in Bells University of Technology. He was a Research Scholar in International Centre of Information Technology and Development in Southern University, Baton Rouge, Louisiana, USA. His research area is Software Performance Engineering. His other research interest areas are Medical Informatics, Soft-computing, Expert System, and Software Engineering. He is a professional member of ACM, CPN (Computer Professional Registration Council of Nigeria) and NCS (Nigeria Computer Society). e-mail: akinboluade@yahoo.com. Victor Wacham A. Mbarika, Ph.D. is the Executive Director, International Center for IT and Development (ICITD, Southern University, T. T Allain #321, Baton Rouge, LA 70813, USA. He is Editor-in-Chief of The African Journal of Information Systems (AJIS, Phone: +1 225 715 4621 or +1 225 572 1042; Fax: +1 225 208 1046. (Email: victor@mbarika.com) Mathieu Kokoly Kourouma, Ph.D., is a professor in the Department of Computer Science, College of Sciences, at Southern University and A&M College. He has a Bachelor in Electrical and Computer Engineering from the Polytechnic Institute of the University of Conakry, Guinea, a Master and Ph.D. in Telecommunications and Computer Engineering, respectively, from the University of Louisiana at Lafayette - U.S.A. His research areas of interest are wireless communications, Sensor Networks, Cognitive Radio Networks, Telecommunications, Network Performance Analysis, Software Engineering and Development, and Database Design. He is a professional member of ACM, NSTA, and AAC&U. Emails: mkkourouma@cmps.subr.edu and mkourouma@gmail.com. Web site: www.cmps.subr.edu. Office number: (225)771-3652. Hyacinthe Aboudja, Ph.D. is currently visiting Assistant Professor in the Computer Science Department of the School of Business at Oklahoma City University. His research interest ranges from computer architecture, Real-time Systems Design, Theory of computing, System Performance Analysis, Software Engineering, and Computer Simulation of Biological Systems. He is a professional member of ACM and IEEE. (email: haboudja@okcu.edu)