Marzia Zaman

Applications of clustering techniques to software partitioning, recovery and restructuring

The artifacts constituting a software system are sometimes unnecessarily coupled with one another or may drift over time. As a result, support of software partitioning, recovery, and restructuring is often necessary. This paper presents studies on applying the numerical taxonomy clustering technique to software applications. The objective is to facilitate those activities just mentioned and to improve design, evaluation and evolution. Numerical taxonomy is mathematically simple and yet it is a useful mechanism for component clustering and software partitioning. The technique can be applied at various levels of abstraction or to different software life-cycle phases. We have applied the technique to: (1) software partitioning at the software architecture design phase; (2) grouping of components based on the source code to recover the software architecture in the reverse engineering process; (3) restructuring of a software to support evolution in the maintenance stage; and (4) improving cohesion and reducing coupling for source code. In this paper, we provide an introduction to the numerical taxonomy, discuss our experiences in applying the approach to various areas, and relate the technique to the context of similar work.

Program restructuring through clustering techniques

Program restructuring is a key method for improving the quality of ill-structured programs, thereby increasing the understandability and reducing the maintenance cost. It is a challenging task and a great deal of research is still ongoing. This work presents an approach to program restructuring at the function level, based on clustering techniques with cohesion as the major concern. Clustering has been widely used to group related entities together. The approach focuses on automated support for identifying ill-structured or low-cohesive functions and providing heuristic advice in both the development and evolution phases. A new similarity measure is defined and studied intensively. The approach is applied to restructure a real industrial program. The empirical observations show that the heuristic advice provided by the approach can help software designers make better decision of why and how to restructure a program. Specific source code level software metrics are presented to demonstrate the value of the approach.

Program restructuring using clustering techniques

Program restructuring is a key method for improving the quality of ill-structured programs, thereby increasing the understandability and reducing the maintenance cost. It is a challenging task and a great deal of research is still ongoing. This paper presents an approach to program restructuring inside of a function based on clustering techniques with cohesion as the major concern. Clustering has been widely used to group related entities together. The approach focuses on automated support for identifying ill-structured or low-cohesive functions and providing heuristic advice in both the development and evolution phases. A new similarity measure is defined and studied intensively specifically from the function perspective. A comparative study on three different hierarchical agglomerative clustering algorithms is also conducted. The best algorithm is applied to restructuring of functions of a real industrial system. The empirical observations show that the heuristic advice provided by the approach can help software designers make better decision of why and how to restructure a program. Specific source code level software metrics are presented to demonstrate the value of the approach.

SOFTWARE ARCHITECTURE DECOMPOSITION USING ATTRIBUTES

Software architectural design has an enormous effect on downstream software artifacts. Decomposition of function for the final system is one of the critical steps in software architectural design. The process of decomposition is typically conducted by designers based on their intuition and past experiences, which may not be robust sometimes. This paper presents a study of applying the clustering technique to support system decomposition based on requirements and their attributes. The approach can support the architectural design process by grouping closely related requirements to form a subsystem or module. In this paper, we demonstrate our experiments in applying the approach to an industrial communication protocol software system and comparing several clustering algorithms. The result obtained from WPGMA (weighted pair-group method using arithmetic averages) shows closer resemblance than other clustering methods to the one developed by the designer.

A Framework for Automatic Resource Provisioning for Private Clouds

A private cloud is maintained by an enterprise forits internal use. In such a scenario instead of buying the resources the enterprise can acquire the resources from a public cloud such as the ones provided by Amazon and Microsoft. On conventional systems rigorous analysis of the system and its workload is performed for determining the appropriate number of resources to be deployed on the private cloud. This paper presents a middleware framework that avoids this step of a priori capacity analysis and allows such private cloud owners to provision resources automatically such that a specified grade of service is maintained. The proposed framework performs dynamic resource provisioning that also leads to a reduction of operational cost. Additional resources are acquired during high traffic periods and released during low traffic periods such that the desired grade of service is always maintained. The paper describes the architecture of the framework and the experience gained from a prototype implementation including a preliminary analysis of its performance.

Routing Management Algorithm Based on Spectrum Trading and Spectrum Competition in Cognitive Radio Networks

Traditionally in routing approaches, each node allows a maximum load through the selected route. The existing routing approaches in cognitive radio networks (CRN) do not take into account spectrum trading as well as spectrum competition among licensed users (PUs). This paper introduces a novel routing algorithm that is based on spectrum trading and spectrum competition for cognitive radio networks while supporting different QoS levels for unlicensed users (SUs). The proposed path selection algorithm among different paths is based on user profiles which contain parameters such as SU identification, number of hops, channel identification, neighbor identification, probabilities of idle slots and PU presence. Each node shares its profile with the neighbor PU, which then exchanges its profile with other PUs and decides based on the information received. In spectrum trading phase a PU calculates its price based on the SU requirements. In spectrum competition phase a new coefficient α is defined that controls the price because of competition among PUs and depends on many factors such as the number of primary users, available channels, and duration of the usage. All possible paths are managed and categorized based on the level of QoS requested by SUs and the price offered by the PU.

A game theory approach: Dynamic behaviours for spectrum management in cognitive radio network

The dynamic behavior for spectrum management in cognitive radio networks is considered in this paper, which consists of spectrum trading and spectrum competition among multiple spectrum owners and spectrum leasers. The primary users adjust their behaviors in renting the spectrum to secondary users in order to achieve higher profits. The secondary users adjust the spectrum renting by observing the changes in the price and the quality of the spectrum. It is however problematic when the primary users and secondary users make the decisions dynamically. A three layer game theoretic approach is introduced in this paper to address this problem. The upper layer models the spectrum competition among primary users; a Bertrand game is formulated where the Nash equilibrium is considered as the solution. The middle layer models the spectrum trading between the primary user and secondary user; a Stackelberg game is formulated where the Nash equilibrium is considered as the solution. The lower layer models the dynamic selection strategies among secondary users in order to select the offered spectrum; an evolutionary game is formulated where the Nash equilibrium is the solution. Basically, the solution in each game is found in terms of the size of the offered spectrum to the secondary users and the spectrum price. The proposed game theory model is used to examine network dynamics under different levels of QoS where the actions of each user are made dynamically.

Comparison of Spectrum Management without Game Theory (SMWG) and with Game Theory (SMG) for Network Performance in Cognitive Radio Network

In this paper, we have introduced two models for spectrum management (spectrum trading and spectrum competition) in cognitive radio network. The first model is without game theory and the second one is with game theory. The first model for spectrum management without game theory, called SMWG, which has been designed to provide an efficient and dynamic equations to enhance the network performance. SMWG provides a novel function, called QoS function, to support three levels of QoS. In addition, it provides a novel factor, called Competition Factor, to control the behaviors among spectrum owners (primary users). In the second model, we have applied the game theory concept into spectrum management (SMG) to compare the network performance in both cases (SMWG and SMG). SMG provides two games, the first one is to model the dynamic behavior of spectrum competition among primary users, a Bertrand game is formulated where the Nash equilibrium is considered as the solution. The second game is to model the spectrum trading between the primary user and the secondary user, a Stackelberg game is formulated where the Nash equilibrium is again considered as the solution. Basically, the solution is found in terms of the size of the offered spectrum to the secondary users with regards to the offered spectrum price. We compare SMWG with conventional scheme, also compare SMG with conventional scheme, and finally compare SMWG with SMG in terms of network performance.

Higher layer issues in cognitive radio network

Cognitive radio networks are smart networks that automatically sense the channel and adjust the network parameters accordingly. Cognitive radio is an emerging technology that enables the dynamic deployment of highly adaptive radios that are built upon software defined radio technology. The radio technology allows the unlicensed operation to be in the licensed band. The cognitive radio network paradigm therefore raises many technical challenges that appear in different layers, such as the power efficiency, spectrum management, spectrum detection, environment awareness, and distributed spectrum measurements in the physical layer, the route selection as well as the route robustness in the network layer, and the security issues like the unauthorized intrusion and malicious users in the application layer. In this paper we aim at presenting an overview of research issues especially in network and application layers as well as the proposed solutions for them.

Reflection on Software Architecture Practices - What Works, What Remains to Be Seen, and What Are the Gaps

This report presents a reflection on software architecture practices based on our past ten year’s industrial experiences, particularly in the area of telecommunications. The report summarizes the methods, tools, and techniques that we have used on various projects. We also discuss, based on our experiences, what methods are useful, what remains to be validated, and what the gaps are between the state of practices and our wishes.