Toward a fuzzy control-based approach to design of self-adaptive software

RINGA: Design and verification of finite state machine for self-adaptive software at runtime

The major goal of self-adaptive software is to provide a mechanism that allows a software system to dynamically change its architectural configuration during run-time to cope with requirement changes and unexpected conditions. Software which needs to handle dynamically changing internal and external environment is one of the areas in which self-adaptive software may do an important role in improving the reliability and performance of software systems. There are three main capabilities that are necessary to support self-adaptive software: the ability to monitor and recognize internal/external situations that affect behavior of the software system; the ability to determine when and what to reconfigure in the software system to handle the situations; and the ability to dynamically change the software architecture during run-time to make the reconfiguration effective. In this paper, we describe a software framework to support such capabilities to realize self-adaptive software and its experiment results.

Self-adaptation ability is particularly desirable for mission critical software (MCS). This paper proposes a fuzzy control-based approach to provide a systematic, engineering, and intuitive way for programmers to achieve software self adaptation. This approach uses fuzzy logic to handle uncertainty in software, uses natural-language style to describe self-adaptation logic, and makes control visible in software. These greatly shorten the knowledge and semantics gap between control engineering and software engineering, and reduce the realization difficulty of software self-adaptation. We illustrate our approach through the development of an adaptive MCS application in process control systems.

Energy-aware software is self-adaptive in nature which dynamically changes its behaviour to save energy. Context information plays a major role in developing such self-adaptive and energy-aware software. Any changes in context information may exhibit different number of operating conditions at run-time. The software should be efficiently developed to be more energy-efficient under different operating conditions through well defined dynamic adaptation policies. Developing such energy-aware adaptive behavior is a challenging task with current programming methods. We employed feature-oriented software development (FOSD) for developing such energy-aware self-adaptive software. In this paper, as first step, domain analysis framework for energy-aware self-adaptive software is proposed. The proposed framework uses feature models to explicitly specify the energy-aware features and context information. An illustrative example is presented to show the usefulness of the proposed domain analysis framework.

When computational process occurs in uncertain environments, the environmental changes often cause unpredictable influence on the computation. Self-adaptive software is one of the possible ways to tackle and handle this motivation. It evaluates its own behavior and surrounding environment, and adapts itself when the evaluation indicates that it is not accomplishing what the system is intended to do, or when better functionality is possible. Since one of the objectives of role models is to support object adaptation to the environment through role binding mechanism, it is expected that role modeling can be conveniently used to build self-adaptive systems. This paper provides some essential features or mechanisms of self-adaptive systems which are intended to be captured by adaptive role models. We propose Self-Epsilon model, an extension of our developed role-based model Epsilon, to combine and integrate all those features with role-based mechanisms into a single model. This paper also shows how the model can be applied to practically design and implement self-adaptive systems. We illustrate our approach through a case study of a simplified system of coordinated traffic jam monitoring used in some related work. The evaluation shows the reasonable and acceptable scalability of our model implementation in the framework.

The development of self-adaptive software (SaS) has specific characteristics compared to traditional one, since it allows that changes to be incorporated at runtime. Automated processes have been used as a feasible solution to conduct the software adaptation at runtime. In parallel, reference model has been used to aggregate knowledge and architectural artifacts, since capture the systems essence of specific domains. However, there is currently no reference model based on reflection for the development of SaS. Thus, the main contribution of this paper is to present a reference model based on reflection for development of SaS that have a need to adapt at runtime. To present the applicability of this model, a case study was conducted and good perspective to efficiently contribute to the area of SaS has been obtained.

An examination of designer and user perceptions of JAD and the traditional IS design methodology

Design methods support the derivation of alternative design solutions, and based on product requirements, it provides a platform for the evaluation and selection of appropriate solutions. Because of the driving needs of design and manufacturing industries and also the quest to remain competitive, several design methodologies aimed at addressing aspects of industrial requirements have emerged. This study identifies current best practice design methodologies and their performance when compared with current trends in design and manufacturing. Based on the review, the strengths and weaknesses of current methods are highlighted and used as the platform to support the recommendation for an integrated design and manufacturing methodology. The integrated design and manufacturing methodology rest on the strengths of product design, cost engineering, enterprise and process simulation modelling techniques. This method can help in the (re)design and (re)engineering of products and processes for better cost indications; help determine appropriate manufacturing paradigms; perform virtual experiments to understand implication of production volume changes on designs and flexibility needs in production systems.

This paper presents a high-level design methodology, called input space adaptive design, and new design automation algorithms for optimizing energy consumption and performance. Our techniques can be applied to behaviors described in hardware description languages, predesigned register-transfer level (RTL) circuits, or in the context of traditional high-level design methodologies. An input space adaptive design exploits the well-known fact that the quality of circuits can be significantly optimized by employing algorithms and implementation architectures that adapt to input statistics. This paper shows that harnessing the principles of input space adaptive design into a structured high-level design methodology can lead to large improvements in performance and energy consumption. We illustrate the tradeoffs involved in such designs, and demonstrate the need for a systematic design methodology in order to realize the full potential for performance and energy improvements. We propose a methodology for input space adaptive design that consists of the following steps: identification of parts of the behavior that hold the highest potential for optimization, selection of input subspaces whose occurrence can lead to significant reductions in implementation complexity, and transformation of the behavior to realize performance and/or energy savings. Evaluations of performance, energy, and area characteristics of input space adaptive designs in the context of a commercial high-level design flow indicate that such designs can reduce energy consumption by up to 58.9% (average of 40.0%), and simultaneously improve performance by up to 57.5% (average of 41.8%) compared to well-optimized designs that do not employ such techniques. The energy-delay product is reduced by up to 77.9% (average of 64.8%). When the performance improvements are translated into additional energy savings through supply voltage reduction, input space adaptive designs consume up to 74.2% (average of 68.8%) less energy at the same performance. The average area overhead is only 7.4%.

This paper presents a flexible and integrated design methodology for image filtering hardware that allows the development of high level algorithms and the automation of their ASIC implementation. The proposed approach offers the algorithm developer all the necessary tools to design and simulate the image processing algorithms at a high level of abstraction, and without any hardware constraints. Once the algorithm has been completely specified its high level description is synthesised, with minimum intervention from the algorithm developer, to one of several levels of hardware description including RTL VHDL and a netlist of the ASIC target technology. The proposed development process uses Mentor Graphics DSP Station. DSP Station is a development system for signal processing hardware and it is not offered as an image processing development tool. This paper describes the adaptation of DSP Station as an integrated environment for the development of image processing algorithms and their ASIC implementation. The design methodology is illustrated by an example algorithm of a 2-D image filter and its architectural implementation.

Driven by recent telecommunication demand, shared communication channels are more abundant today than ever before. These shared wired/wireless networks for communication are being exploited more and more in control systems to connect sensors, controllers and actuators resulting in so-called networked control systems (NCSs). NCSs replace the more traditional control systems where dedicated point-to-point (wired) connections are being used. The advantages of using NCS technology are inexpensive and easily modifiable communication links which allow control algorithms to be easily implemented in situations where dedicated connections are not possible (either economically or physically). However, the drawback is that the control system is susceptible to undesirable (possibly destabilizing) side-effects such as time-varying transmission intervals, time-varying delays, packet dropouts, quantization and a shared communication medium. These network-induced effects undermine fundamental assumptions on which traditional control theory is built and, therefore, it is essential to develop new techniques and tools that can be used to analyze and/or design control systems which communicate via a shared network. This thesis advances NCS analysis and design methodologies by contributing new theoretical developments, new software tools and new experimental validation results. The first new theoretical development is in the area of analyzing robust stability properties with respect to network-induced effects. A sum of squares (SOS) approach for a class of nonlinear NCSs incorporating bounded time-varying delays, bounded time-varying transmission intervals and a shared communication medium is developed. Mathematical models that describe these nonlinear NCSs are cast into suitable hybrid system formulations. Based on these hybrid system formulations, (families of) Lyapunov functions are constructed using SOS techniques. Amongst other benefits, it is shown that this technique improves the guaranteed robustness margins compared to existing work. The second new theoretical development concerns the design of networkaware decentralized controllers guaranteeing robust stability properties with respect to network-induced effects. We develop one of the first approaches based on semidefinite programming techniques to synthesize stabilizing decentralized observer-based output-feedback controllers for linear plants where the controllers, sensors and actuators are connected via a shared communication network subject to time-varying transmission intervals and delays. To effectively deal with the shared communication medium, a switched observer structure is adopted that switches based on the transmitted measured outputs and a switched controller structure is also adopted that switches based on the transmitted control inputs at each transmission time. By taking a discrete-time switched linear system perspective on modeling these decentralized NCSs, we are able to derive a general model that captures all these networked and decentralized control aspects. We provide linear matrix inequality (LMI)-based synthesis conditions which, if satisfied, provide stabilizing observer-based controllers that are both decentralized and robust to network effects. Regarding new software tools, the first prototype of a toolbox is developed to automate (robust) stability analysis (and controller design) for NCSs. Specifically, it is shown that the toolbox can be employed to efficiently verify if a linear time-invariant (LTI) plant and an LTI controller interconnected with a shared network are robust to certain network imperfections. The main intention of the toolbox is to make the available theory readily accessible to and applicable for the general control community. Additionally, the chosen software structure enables the incorporation of custom models or custom stability/performance analysis conditions in an easy manner, thereby allowing the control community to contribute and to further develop the toolbox. Finally, an experimental case study involving a wirelessly controlled inverted pendulum/cart system is investigated. In particular, the communication network itself is analyzed such that the network-induced effects can be characterized in terms of bounds on the transmission intervals and transmission delays. Based on these bounds, the prototype toolbox is applied to analyze the robustness regions for different performance specifications, which aid in tuning the controller to achieve more closed-loop robustness with respect to the network-induced effects. This leads to a validation of the developed theory in an experimental setting. In addition to the validation of the developed theory, many new insights into the network behavior are obtained and explained, thereby raising new interesting questions for future research on NCSs.

Background Online social networks share similar topological characteristics as real-world social networks. Many studies have been conducted to analyze the online social networks, but it is difficult to link human interests with social network software design. Purpose The goal of this work is to propose a methodology involving the analysis of human interactions for use in designing online social network software. Methods We propose a novel use of social network analysis techniques to elicit requirements in order to design better online Social network-based software. The validation case study involved the collection of real-world data by means of a questionnaire to perform a network design construction and analysis. The key idea is to examine social network to help in the identification of behaviors and interests of people for better software requirements elicitation. Results The validation case study demonstrates how unexpected centrality measures can emerge in real world networks. Our case study can thus conducted as a baseline for better requirement elicitation studies for online social network software design. This work also indicates how sociometric methods may be used to analyze any social domain as a possible standard practice in online social network software design. Overall, the study proved the effectiveness of the proposed novel methodology for the design of online social network software. The methodology specifically improves upon traditional methods for software design by involving social network modeling and analysis to first study the behavior and elicit requirements to develop more resilient online social network sites.

Metric-based Evaluation of Implemented Software Architectures

Research in the field of VLSI digital signal processing (DSP) involves study of algorithms and architectures for VLSI implementation of signal and image processing algorithms, theory of architectures and design methodologies, and computer aided design of VLSI DSP systems which can meet the area-speed-power demands of applications using available technologies. The objective in design and implementation of VLSI DSP systems is to reduce the area and power while meeting the speed requirements of the real-time applications. Although about one and a half decades old, this field is still in its youth. The challenges in meeting the speed-power demands of new applications leads to innovations in theory of architectures. At the same time use of old techniques in new applications allows us to further generalize and extend known principles. Because of these continuing advances and because of lack of text books in this field, it remains difficult to educate practicing engineers and graduate students in this field. This paper presents can overview of two ten-week quarter courses offered at the University of Minnesota to train graduate students in this field. >

Formal inference processes in all kinds of engineering designs are based on the cognitive process and means of abstraction. This paper presents a formal treatment of system design. It first discusses the differences of system designs in abstract and concrete systems. A hierarchical abstraction model of system descriptivity (HAMSD) is developed that explains the abstract levels of entities and artifacts in both the concrete world and the abstract world. The HAMSD model classifies the design of a system at five levels known as the levels of (1) analogue objects, (2) diagrams, (3) natural languages, (4) professional notations, and (5) mathematics from the bottom up. According to the HAMSD model, an architectural, functional, or behavioral design of a system should adopt a higher-level abstract means to describe the system at a given abstract level, in order to obtain more powerful descriptivity and higher accuracy. Therefore, a mechanical system may be designed by using diagrams; while an information or software system can not be precisely described by diagrams. The reason is that the former is at an abstract level lower than the descriptive means of diagrams, but not so is the latter. Applications of the above findings in dealing with complicated problems in large-scale software system designs are discussed in the context of software engineering. Real-time process algebra (RTPA) is adopted as a highly abstract means in case studies on formal methodologies of software system design and modeling for software objects and design patterns.