SOAR 2009
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Preface Self-adaptability has been proposed as an effective approach to automate the complexity associated with the management of modern-day software systems. Self-adaptability endows a software system with the capability to adapt itself at runtime to deal with changing operating conditions or user requirements. Researchers in self-adaptive systems mostly take an architecture-centric focus on developing top–down solutions. In this approach, the system is monitored to maintain an explicit (architectural) representation of the system and based on a set of (possibly dynamic) goals, the system’s structure or behavior is adapted. Researchers of self-organizing systems mostly take an algorithmic/organizational focus on developing bottom–up solutions. In this approach, the system components adapt their local behavior or patterns of interaction to changing conditions and cooperatively realize system adaptation. Self-organizing approaches are often inspired by biological or natural phenomena. With the term “self-organizing architectures” (SOAR) we refer to an engineering approach for self-adaptive systems that combines architectural approaches for self-adaptability with principles and techniques from self-organization. Whereas both lines of research have been successful at alleviating some of the associated challenges of constructing self-adaptive systems, persistent challenges remain, in particular for building complex distributed self-adaptive systems. Among the hard challenges in the architectural-centric approach are handling uncertainty and providing decentralized scalable solutions. Some of the hard challenges in the self-organizing approach are connecting local interactions with global system behavior, and accommodating a disciplined engineering approach. The awareness grows that for building complex distributed self-adaptive systems, principles from both self-adaptive systems and self-organizing systems have to be combined. For instance, Web-scale information systems, intelligent transportation systems, and the power grid are all innately decentralized systems, but control in local sub-systems may be highly centralized. Engineering such complex systems puts forward questions such as: What kind of bottom–up mechanisms can be exploited in order to deal with uncertainty but at the same time provide the required assurances? How to derive and exploit tactics, architectural patterns, and reference architectures to realize robust, scalable, and long-lived solutions? The general goal of SOAR is to provide a middle ground that combines the architectural perspective of self-adaptive systems with the algorithmic perspective of self-organizing systems. The papers in this volume include both selected and thoroughly revised papers from the WICSA/ ECSA 2009 SOAR Workshop and invited papers. VI Preface The papers cover a broad range of topics related to self-organizing architectures, including self-adaptive architectures, decentralized architectures, nature-inspired approaches, and learning approaches. We hope that the papers in this volume stimulate further research in self-organizing architectures. May 2008 D. Weyns S. Malek R. de Lemos J. Andersson Organization SOAR 2009 was organized in conjunction with the Working IEEE/IFIP Conference on Software Architecture (WICSA) and the European Conference on Software Architecture (ECSA), Cambridge, UK, September 14, 2009. Program Co-chairs Danny Weyns Sam Malek Rogério de Lemos Jesper Andersson Katholieke Universiteit Leuven, Belgium George Mason University, USA University of Kent, UK Linnaeus University, Sweden Program Committee Nelly Bencomo Yuriy Brun David Garlan Kurt Geihs Holger Giese Jorge J. Gómez Sanz Tom Holvoet Mark Klein Marco Mamei Hausi A. Müller Flavio Oquendo Van Parunak Onn Shehory Mirko Viroli Lancaster University, UK University of Southern California, USA Carnegie Mellon University, USA University of Kassel, Germany Hasso Plattner Institute at the University of Postdam, Germany Universidad Complutense de Madrid, Spain Katholieke Universiteit Leuven, Belgium Software Engineering Institute, Carnegie Mellon, USA Universitá di Modena e Reggio Emilia, Italy University of Victoria, Canada Université de Bretagne-Sud, France Vector Research Center, Ann Arbor, USA IBM Haifa Research Lab, Israel Università di Bologna, Italy Website http://distrinet.cs.kuleuven.be/events/soar/2009/ Acknowledgements We are grateful to the WICSA/ECSA 2009 organizers for hosting SOAR. We thank the PC members for their critical review work. Finally, we thank the Springer staff for supporting the publication of this volume. Table of Contents Self-adaptive Approaches Elements of Self-adaptive Systems – A Decentralized Architectural Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carlos E. Cuesta and M. Pilar Romay 1 Improving Architecture-Based Self-adaptation Using Preemption . . . . . . . Rahul Raheja, Shang-Wen Cheng, David Garlan, and Bradley Schmerl 21 Weaving the Fabric of the Control Loop through Aspects . . . . . . . . . . . . . Robrecht Haesevoets, Eddy Truyen, Tom Holvoet, and Wouter Joosen 38 Self-organizing Approaches Self-organisation for Survival in Complex Computer Architectures . . . . . . Fiona A.C. Polack Self-organising Sensors for Wide Area Surveillance Using the Max-sum Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alex Rogers, Alessandro Farinelli, and Nicholas R. Jennings Multi-policy Optimization in Self-organizing Systems . . . . . . . . . . . . . . . . . Ivana Dusparic and Vinny Cahill A Bio-inspired Algorithm for Energy Optimization in a Self-organizing Data Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Donato Barbagallo, Elisabetta Di Nitto, Daniel J. Dubois, and Raffaela Mirandola Towards a Pervasive Infrastructure for Chemical-Inspired Self-organising Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mirko Viroli, Matteo Casadei, Elena Nardini, and Andrea Omicini 66 84 101 127 152 Hybrid Approaches Self-adaptive Architectures for Autonomic Computational Science . . . . . . Shantenu Jha, Manish Parashar, and Omer Rana Modelling the Asynchronous Dynamic Evolution of Architectural Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cristóbal Costa-Soria and Reiko Heckel 177 198 X Table of Contents A Self-organizing Architecture for Traffic Management . . . . . . . . . . . . . . . . Rym Zalila-Wenkstern, Travis Steel, and Gary Leask On the Modeling, Refinement and Integration of Decentralized Agent Coordination: A Case Study on Dissemination Processes in Networks . . . Jan Sudeikat and Wolfgang Renz 230 251 A Self-organizing Architecture for Pervasive Ecosystems . . . . . . . . . . . . . . Cynthia Villalba, Marco Mamei, and Franco Zambonelli 275 Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301