Search-based system architecture development using a holistic modeling approach

This dissertation presents an innovative approach to system architecting where search algorithms are used to explore design trade space for good architecture alternatives. Such an approach is achieved by integrating certain model construction, alternative generation, simulation, and assessment processes into a coherent and automated framework. This framework is facilitated by a holistic modeling approach that combines the capabilities of Object Process Methodology (OPM), Colored Petri Net (CPN), and feature model. The resultant holistic model can not only capture the structural, behavioral, and dynamic aspects of a system, allowing simulation and strong analysis methods to be applied, it can also specify the architectural design space. Both object-oriented analysis and design (OOA/D) and domain engineering were exploited to capture design variables and their domains and define architecture generation operations. A fully realized framework (with genetic algorithms as the search algorithm) was developed. Both the proposed framework and its suggested implementation, including the proposed holistic modeling approach and architecture alternative generation operations, are generic. They are targeted at systems that can be specified using object-oriented or process-oriented paradigm. The broad applicability of the proposed approach is demonstrated on two examples. One is the configuration of reconfigurable manufacturing systems (RMSs) under multi-objective optimization and the other is the architecture design of a manned lunar landing system for the Apollo program. The test results show that the proposed approach can cover a huge number of architecture alternatives and support the assessment of several performance measures. A set of quality results was obtained after running the optimization algorithm following the proposed framework --Abstract, page iii

Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

Modeling methodologies of synergic effects related to climate change and sustainable energy management

The main aim of the present article is to highlight the synergic effects related to climate change and sustainable energy management. Due to our model and given methodology it is possible to examine the relations between the terms mentioned above. In our Knowledge Attributed Petri Net based simulation model the most significant elements are highlighted in question of integrating Cellular Neural Networks and Fuzzy Systems into an object oriented formal description. This methodology implies the importance of holistic approach - namely the usage of predefined risk categories, spreading of causes and effects in physical space - that is crucial in these investigations. Only marginal attention is paid to the synergic effects related to adaptation and mitigation efforts according to climate change. Taking every result into account, this methodologically complex subject is possible to examine from the point of sustainability

Understanding the Elements of Executable Architectures Through a Multi-Dimensional Analysis Framework

The objective of this dissertation study is to conduct a holistic investigation into the elements of executable architectures. Current research in the field of Executable Architectures has provided valuable solution-specific demonstrations and has also shown the value derived from such an endeavor. However, a common theory underlying their applications has been missing. This dissertation develops and explores a method for holistically developing an Executable Architecture Specification (EAS), i.e., a meta-model containing both semantic and syntactic information, using a conceptual framework for guiding data coding, analysis, and validation. Utilization of this method resulted in the description of the elements of executable architecture in terms of a set of nine information interrogatives: an executable architecture information ontology. Once the detail-rich EAS was constructed with this ontology, it became possible to define the potential elements of executable architecture through an intermediate level meta-model. The intermediate level meta-model was further refined into an interrogative level meta-model using only the nine information interrogatives, at a very high level of abstraction