This study investigates the concept of adaptivity in relation to the evolution of software and hence software embedded products. Whilst laying out the benefits of adaptivity in products, it discusses the potential future threats engendered by the actual change observed in the functionality principles of adaptive products. The discussion is based upon cybernetic theory which defines control technology in the 20th century anew. Accordingly, literature survey on cybernetic theory, evolution of software from conventional to adaptive structure is presented. The changes in the functionality principles of adaptive systems and the similarities that these changes show with living autonomous systems is also investigated. The roles of product and user are redefined in relation to changing control mechanisms. Then, the new direction that the conventional product-user relationship has taken with adaptive products is examined. Finally, the potential future threats this new direction might bring is discussed with the help of two control conflict situations.

Differentiated-Service networks is designed for solving scalability problems through traffic aggregation. However, it can't guarantee end-to-end QoS of individual flow. In this thesis, we propose a Self-Adaptive Control Scheme for Differentiated-Service networks that can improve the throughput of individual flows dynamically. In this scheme, egress routers monitor the average throughput of individual flow, and send the Self-Adaptive Control Messages to ingress routers if need. The ingress router re-allocate network resources to improve throughput of high-priority flows depending on the Control Messages. We use NS-2 simulator to prove that our scheme that can improve throughput of high-priority flows dynamically, and suggest that a better time interval of Self-Adaptive control can be determined based on the queue sizes, packets arrival rate and departure rate. Finally, we use Random Early Detection (RED) queue instead of Drop-Tail queue to reduce unfairness of individual flows when there are congestion and insufficient network resources.

Our research posits a context-sensitive, adaptable, assistive services and technology system (CAAST) that takes advantage of the advancements in mobile computing to provide barrier-free access to environmental information and devices. To inform our research we explore the following topics: the deficiencies associated with current assistive technologies; the advances in wireless sensor node technology; the interference and accuracy problems associated with wireless location detection; the coordination problems associated with service discovery; the management and coordination problems associated with decentralized sensor nodes; the separation of information and activities from the human interface; the efficiency and abstraction problems associated with interface description languages; and the adaptation of information and activities to meet the needs of those with disabilities. As a result of our research into these areas we devise an assistive technology, CAAST, that intends to be a comprehensive approach to universal access to information and activities for those with disabilities. / Department of Computer Science

Context is important to today's mobile and ubiquitous systems as operational requirements are only valid under certain context conditions. Detecting context and adapting automatically to that context is a key feature of many of these systems. However, when the operational context associated with a particular requirement changes drastically in a way that designers could not have anticipated, many systems are unable to effectively adapt their operating parameters to continue meeting user needs. Automatically detecting and implementing this system context evolution is highly desirable because it allows for increased uncertainty to be built into the system at design time in order to efficiently and effectively cope with these kinds of drastic changes. This thesis is an empirical investigation and discussion towards integrating data mining algorithms into self-adaptive systems to analyze and de fine new context relevant to specific system requirements when current system context parameters are no longer sufficient. / Graduate / 0984 / arook@uvic.ca

There is a strong synergy between the concepts of evolution and adaptation in software engineering: software adaptation refers to both the current software being adapted and to the evolution process that leads to the new adapted software. Evolution changes for the purpose of adaptation are usually made at development or compile time, and are meant to handle predictable situations in the form of software change requests. On the other hand, software may also change and adapt itself based on the changes in its environment. Such adaptive changes are usually dynamic, and are suitable for dealing with unpredictable or temporary changes in the software's operating environment. A promising solution for software adaptation is to develop self-adaptive software systems that can manage changes dynamically at runtime in a rapid and reliable way. One of the main advantages of self-adaptive software is its ability to manage the complexity that stems from highly dynamic and nondeterministic operating environments. If a self-adaptive software system has been engineered and used properly, it can greatly improve the cost-effectiveness of software change through its lifespan. However, in practice, many of the existing approaches towards self-adaptive software are rather expensive and may increase the overall system complexity, as well as subsequent future maintenance costs. This means that in many cases, self-adaptive software is not a good solution, because its development and maintenance costs are not paid off. The situation is even worse in the case of making current (legacy) systems adaptive. There are several factors that have an impact on the cost-effectiveness and usability of self-adaptive software; however the main objective of this thesis is to make a software system adaptive in a cost-effective way, while keeping the target adaptive software generic, usable, and evolvable, so as to support future changes. In order to effectively engineer and use self-adaptive software systems, in this thesis we propose a new conceptual model for identifying and specifying problem spaces in the context of self-adaptive software systems. Based on the foundations of this conceptual model, we propose a model-centric approach for engineering self-adaptive software by designing a generic adaptation framework and a supporting evolution process. This approach is particularly tailored to facilitate and simplify the process of evolving and adapting current (legacy) software towards runtime adaptivity. The conducted case studies reveal the applicability and effectiveness of this approach in bringing self-adaptive behaviour into non-adaptive applications that essentially demand adaptive behaviour to sustain.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006. / Schwan, Karsten, Committee Member ; Schimmel, David, Committee Member ; Copeland, John, Committee Member ; Owen, Henry, Committee Chair ; Wills, Linda, Committee Member.

In a complex and ever changing software environment, controlling and coordinating asoftware system's architecture and its components have become an almost an impossibletask. It takes a lot of effort from a developer and even then it is not a foolproof plan. In thisMasters thesis, we introduce Architectural Agents. Architectural agents are specializedconfigurable components. They are the key elements that make a software environmentself-adaptive. Should there be any problem in the software, the architectural agents havingmonitored the software architecture can know which component is having the problem andfix it.This thesis aims towards making software self-adaptive by using Architectural Agents.This can be achieved by combining two frameworks namely, Prism-MW and JADE. Eachof these frameworks have their own property that contribute towards achieving the goal ofthe thesis. Prism-MW decomposes a software into separate and easily manageablecomponents. JADE on the other hand, creates agents. Combining these two frameworksmeans using the agents to control the software components. This will make the softwareenvironment self-adaptive.We support our claims and theories by practically proving them.

As software evolves and becomes more complex, self-adaptive systems become a moreinteresting solution. Self-adaptive software systems are capable to perform changes inthemselves without human intervention. To make this possible it is necessary toperform a good observation of the system and its environment. This observation is madeby a monitoring system.In this paper, a framework for monitoring OSGi based applications is presented.OSGi is a module system and service platform for Java. This framework offers run-timeinformation about OSGi modules, services and their behavior.The first step is to make a state-of-the-art survey of existing methods to monitor inthe field of self-adaptive systems and OSGi based applications. The survey reviews aset of articles in the area. It is performed to discover what are the common objectivesand problems that any monitoring system faces. After that, the requirements for theframework are stated. These requirements specify the functionality that the frameworkis required to provide, along with the quality attributes that it has to meet. Todemonstrate use of the contributed monitoring framework, we have developed twoexample demonstrators. The objective of these demonstrators is to provide users of theframework with working examples, so that they can use the framework to develop theirown monitoring systems.

Au cours des dernières années, il y a un intérêt croissant pour les systèmes logiciels capables de faire face à la dynamique des environnements en constante évolution. Actuellement, les systèmes auto-adaptatifs sont nécessaires pour l’adaptation dynamique à des situations nouvelles en maximisant performances et disponibilité. Les systèmes ubiquitaires et pervasifs fonctionnent dans des environnements complexes et hétérogènes et utilisent des dispositifs à ressources limitées où des événements peuvent compromettre la qualité du système. En conséquence, il est souhaitable de s’appuyer sur des mécanismes d’adaptation du système en fonction des événements se produisant dans le contexte d’exécution. En particulier, la communauté du génie logiciel pour les systèmes auto-adaptatif (Software Engineering for Self-Adaptive Systems - SEAMS) s’efforce d’atteindre un ensemble de propriétés d’autogestion dans les systèmes informatiques. Ces propriétés d’autogestion comprennent les propriétés dites self-configuring, self-healing, self-optimizing et self-protecting. Afin de parvenir à l’autogestion, le système logiciel met en œuvre un mécanisme de boucle de commande autonome nommé boucle MAPE-K [78]. La boucle MAPE-K est le paradigme de référence pour concevoir un logiciel auto-adaptatif dans le contexte de l’informatique autonome. Cet modèle se compose de capteurs et d’effecteurs ainsi que quatre activités clés : Monitor, Analyze, Plan et Execute, complétées d’une base de connaissance appelée Knowledge, qui permet le passage des informations entre les autres activités [78]. L’étude de la littérature récente sur le sujet [109, 71] montre que l’adaptation dynamique est généralement effectuée de manière réactive, et que dans ce cas les systèmes logiciels ne sont pas en mesure d’anticiper des situations problématiques récurrentes. Dans certaines situations, cela pourrait conduire à des surcoûts inutiles ou des indisponibilités temporaires de ressources du système. En revanche, une approche proactive n’est pas simplement agir en réponse à des événements de l’environnement, mais a un comportement déterminé par un but en prenant par anticipation des initiatives pour améliorer la performance du système ou la qualité de service. / In this thesis we proposed a proactive self-adaptation by integrating predictive analysis into two phases of the software process. At design time, we propose a predictive modeling process, which includes the activities: define goals, collect data, select model structure, prepare data, build candidate predictive models, training, testing and cross-validation of the candidate models and selection of the ''best'' models based on a measure of model goodness. At runtime, we consume the predictions from the selected predictive models using the running system actual data. Depending on the input data and the time allowed for learning algorithms, we argue that the software system can foresee future possible input variables of the system and adapt proactively in order to accomplish middle and long term goals and requirements.

Dynamic optimisation problems are problems where the search space does not remain constant over time. Evolutionary algorithms aimed at static optimisation problems often fail to effectively optimise dynamic problems. The main reason for this is that the algorithms converge to a single optimum in the search space, and then lack the necessary diversity to locate new optima once the environment changes. Many approaches to adapting traditional evolutionary algorithms to dynamic environments are available in the literature, but differential evolution (DE) has been investigated as a base algorithm by only a few researchers. This thesis reports on adaptations of existing DE-based optimisation algorithms for dynamic environments. A novel approach, which evolves DE sub-populations based on performance in order to discover optima in an dynamic environment earlier, is proposed. It is shown that this approach reduces the average error in a wide range of benchmark instances. A second approach, which is shown to improve the location of individual optima in the search space, is combined with the first approach to form a new DE-based algorithm for dynamic optimisation problems. The algorithm is further adapted to dynamically spawn and remove sub-populations, which is shown to be an effective strategy on benchmark problems where the number of optima is unknown or fluctuates over time. Finally, approaches to self-adapting DE control parameters are incorporated into the newly created algorithms. Experimental evidence is presented to show that, apart from reducing the number of parameters to fine-tune, a benefit in terms of lower error values is found when employing self-adaptive control parameters. / Thesis (PhD)--University of Pretoria, 2012. / Computer Science / unrestricted