Self-adaptation via concurrent multi-action evaluation for unknown context

Context-aware computing has been attracting growing attention in recent years. Generally, there are several ways for a context-aware system to select a course of action for a particular change of context. One way is for the system developers to encompass all possible context changes in the domain knowledge. Other methods include system inferences and adaptive learning whereby the system executes one action and evaluates the outcome and self-adapts/self-learns based on that. However, in situations where a system encounters unknown contexts, the iterative approach would become unfeasible when the size of the action space increases. Providing efficient solutions to this problem has been the main goal of this research project. Based on the developed abstract model, the designed methodology replaces the single action implementation and evaluation by multiple actions implemented and evaluated concurrently. This parallel evaluation of actions speeds up significantly the evolution time taken to select the best action suited to unknown context compared to the iterative approach. The designed and implemented framework efficiently carries out concurrent multi-action evaluation when an unknown context is encountered and finds the best course of action. Two concrete implementations of the framework were carried out demonstrating the usability and adaptability of the framework across multiple domains. The first implementation was in the domain of database performance tuning. The concrete implementation of the framework demonstrated the ability of concurrent multi-action evaluation technique to performance tune a database when performance is regressed for an unknown reason. The second implementation demonstrated the ability of the framework to correctly determine the threshold price to be used in a name-your-own-price channel when an unknown context is encountered. In conclusion the research introduced a new paradigm of a self-adaptation technique for context-aware application. Among the existing body of work, the concurrent multi-action evaluation is classified under the abstract concept of experiment-based self-adaptation techniques

ACHIEVING AUTONOMIC SERVICE ORIENTED ARCHITECTURE USING CASE BASED REASONING

Service-Oriented Architecture (SOA) enables composition of large and complex computational units out of the available atomic services. However, implementation of SOA, for its dynamic nature, could bring about challenges in terms of service discovery, service interaction, service composition, robustness, etc. In the near future, SOA will often need to dynamically re-configuring and re-organizing its topologies of interactions between the web services because of some unpredictable events, such as crashes or network problems, which will cause service unavailability. Complexity and dynamism of the current and future global network system require service architecture that is capable of autonomously changing its structure and functionality to meet dynamic changes in the requirements and environment with little human intervention. This then needs to motivate the research described throughout this thesis. In this thesis, the idea of introducing autonomy and adapting case-based reasoning into SOA in order to extend the intelligence and capability of SOA is contributed and elaborated. It is conducted by proposing architecture of an autonomic SOA framework based on case-based reasoning and the architectural considerations of autonomic computing paradigm. It is then followed by developing and analyzing formal models of the proposed architecture using Petri Net. The framework is also tested and analyzed through case studies, simulation, and prototype development. The case studies show feasibility to employing case-based reasoning and autonomic computing into SOA domain and the simulation results show believability that it would increase the intelligence, capability, usability and robustness of SOA. It was shown that SOA can be improved to cope with dynamic environment and services unavailability by incorporating case-based reasoning and autonomic computing paradigm to monitor and analyze events and service requests, then to plan and execute the appropriate actions using the knowledge stored in knowledge database

Analysis of Autonomic Service Oriented Architecture

— Service-Oriented Architecture (SOA) enables composition of large and complex computational units out of the available atomic services. However, implementation of SOA, for its dynamic nature, could bring about challenges in terms of service discovery, service interaction, and service composition. SOA may often need to dynamically re-configure and re-organize its topologies of interactions between the web services because of some unpredictable events, such as crashes or network problems, which will cause service unavailability. Complexity and dynamism of the current and future global network systems require service architecture that is capable of autonomously changing its structure and functionality to meet dynamic changes in the requirements and environment with little human intervention. In this paper, formal models of a proposed autonomic SOA framework are developed and analyzed using Petri Net. The results showed that SOA can be improved to cope with dynamic environment and services unavailability by incorporating case-based reasoning and autonomic computing paradigm to monitor and analyze events and service requests, then to plan and execute the appropriate actions using the knowledge stored in knowledge database. Keywords— Service Oriented Architecture, autonomic computing, case-based reasoning, formal model, Petri Ne

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated