@book{AlnemrPolyvyanyyAbuJarouretal.2010,
  author    = {Alnemr, Rehab and Polyvyanyy, Artem and AbuJarour, Mohammed and Appeltauer, Malte and Hildebrandt, Dieter and Thomas, Ivonne and Overdick, Hagen and Sch{\"o}bel, Michael and Uflacker, Matthias and Kluth, Stephan and Menzel, Michael and Schmidt, Alexander and Hagedorn, Benjamin and Pascalau, Emilian and Perscheid, Michael and Vogel, Thomas and Hentschel, Uwe and Feinbube, Frank and Kowark, Thomas and Tr{\"u}mper, Jonas and Vogel, Tobias and Becker, Basil},
  title     = {Proceedings of the 4th Ph.D. Retreat of the HPI Research School on Service-oriented Systems Engineering},
  editor    = {Meinel, Christoph and Plattner, Hasso and D{\"o}llner, J{\"u}rgen Roland Friedrich and Weske, Mathias and Polze, Andreas and Hirschfeld, Robert and Naumann, Felix and Giese, Holger},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-036-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-40838},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {Getr. Z{\"a}hlung},
  year      = {2010},
  language  = {en}
}
@book{VogelGiese2013,
  author    = {Vogel, Thomas and Giese, Holger},
  title     = {Model-driven engineering of adaptation engines for self-adaptive software : executable runtime megamodels},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-227-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-63825},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {vi, 59},
  year      = {2013},
  abstract  = {The development of self-adaptive software requires the engineering of an adaptation engine that controls and adapts the underlying adaptable software by means of feedback loops. The adaptation engine often describes the adaptation by using runtime models representing relevant aspects of the adaptable software and particular activities such as analysis and planning that operate on these runtime models. To systematically address the interplay between runtime models and adaptation activities in adaptation engines, runtime megamodels have been proposed for self-adaptive software. A runtime megamodel is a specific runtime model whose elements are runtime models and adaptation activities. Thus, a megamodel captures the interplay between multiple models and between models and activities as well as the activation of the activities. In this article, we go one step further and present a modeling language for ExecUtable RuntimE MegAmodels (EUREMA) that considerably eases the development of adaptation engines by following a model-driven engineering approach. We provide a domain-specific modeling language and a runtime interpreter for adaptation engines, in particular for feedback loops. Megamodels are kept explicit and alive at runtime and by interpreting them, they are directly executed to run feedback loops. Additionally, they can be dynamically adjusted to adapt feedback loops. Thus, EUREMA supports development by making feedback loops, their runtime models, and adaptation activities explicit at a higher level of abstraction. Moreover, it enables complex solutions where multiple feedback loops interact or even operate on top of each other. Finally, it leverages the co-existence of self-adaptation and off-line adaptation for evolution.},
  language  = {en}
}
@article{VogelGiese2014,
  author    = {Vogel, Thomas and Giese, Holger},
  title     = {Model-Driven engineering of self-adaptive software with EUREMA},
  series = {ACM transactions on autonomous and adaptive systems},
  volume    = {8},
  journal   = {ACM transactions on autonomous and adaptive systems},
  number    = {4},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  issn      = {1556-4665},
  doi       = {10.1145/2555612},
  pages     = {33},
  year      = {2014},
  abstract  = {The development of self-adaptive software requires the engineering of an adaptation engine that controls the underlying adaptable software by feedback loops. The engine often describes the adaptation by runtime models representing the adaptable software and by activities such as analysis and planning that use these models. To systematically address the interplay between runtime models and adaptation activities, runtime megamodels have been proposed. A runtime megamodel is a specific model capturing runtime models and adaptation activities. In this article, we go one step further and present an executable modeling language for ExecUtable RuntimE MegAmodels (EUREMA) that eases the development of adaptation engines by following a model-driven engineering approach. We provide a domain-specific modeling language and a runtime interpreter for adaptation engines, in particular feedback loops. Megamodels are kept alive at runtime and by interpreting them, they are directly executed to run feedback loops. Additionally, they can be dynamically adjusted to adapt feedback loops. Thus, EUREMA supports development by making feedback loops explicit at a higher level of abstraction and it enables solutions where multiple feedback loops interact or operate on top of each other and self-adaptation co-exists with offline adaptation for evolution.},
  language  = {en}
}
@article{GhahremaniGieseVogel2020,
  author    = {Ghahremani, Sona and Giese, Holger and Vogel, Thomas},
  title     = {Improving scalability and reward of utility-driven self-healing for large dynamic architectures},
  series = {ACM transactions on autonomous and adaptive systems},
  volume    = {14},
  journal   = {ACM transactions on autonomous and adaptive systems},
  number    = {3},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  issn      = {1556-4665},
  doi       = {10.1145/3380965},
  pages     = {41},
  year      = {2020},
  abstract  = {Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfies certain conditions. They result in scalable solutions but often with merely satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal decisions by using an often costly optimization, which typically does not scale for large problems. We propose a rule-based and utility-driven adaptation scheme that achieves the benefits of both directions such that the adaptation decisions are optimal, whereas the computation scales by avoiding an expensive optimization. We use this adaptation scheme for architecture-based self-healing of large software systems. For this purpose, we define the utility for large dynamic architectures of such systems based on patterns that define issues the self-healing must address. Moreover, we use pattern-based adaptation rules to resolve these issues. Using a pattern-based scheme to define the utility and adaptation rules allows us to compute the impact of each rule application on the overall utility and to realize an incremental and efficient utility-driven self-healing. In addition to formally analyzing the computational effort and optimality of the proposed scheme, we thoroughly demonstrate its scalability and optimality in terms of reward in comparative experiments with a static rule-based approach as a baseline and a utility-driven approach using a constraint solver. These experiments are based on different failure profiles derived from real-world failure logs. We also investigate the impact of different failure profile characteristics on the scalability and reward to evaluate the robustness of the different approaches.},
  language  = {en}
}