GOAL: Supporting General and Dynamic Adaptation in Computing Systems. Pervaiz, A., Yang, Y. H., Duracz, A., Bartha, F., Sai, R., Imes, C., Cartwright, R., Palem, K., Lu, S., & Hoffmann, H. In Proceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, of Onward! 2022, pages 16–32, New York, NY, USA, 2022. Association for Computing Machinery.
GOAL: Supporting General and Dynamic Adaptation in Computing Systems [link]Paper  doi  abstract   bibtex   
Adaptive computing systems automatically monitor their behavior and dynamically adjust their own configuration parameters—or knobs—to ensure that user goals are met despite unpredictable external disturbances to the system. A major limitation of prior adaptation frameworks is that their internal adaptation logic is implemented for a specific, narrow set of goals and knobs, which impedes the development of complex adaptive systems that must meet different goals using different sets of knobs for different deployments, or even change goals during one deployment. To overcome this limitation we propose GOAL, an adaptation framework distinguished by its virtualized adaptation logic implemented independently of any specific goals or knobs. GOAL supports this logic with a programming interface allowing users to define and manipulate a wide range of goals and knobs within a running program. We demonstrate GOAL’s benefits by using it re-implement seven different adaptive systems from the literature, each of which has a different set of goals and knobs. We show GOAL’s general approach meets goals as well as prior approaches designed for specific goals and knobs. In dynamic scenarios where the goals and knobs are modified at runtime, GOAL achieves 93.7
@INPROCEEDINGS{GOAL,
  author = {Pervaiz, Ahsan and Yang, Yao Hsiang and Duracz, Adam and Bartha, Ferenc and Sai, Ryuichi and Imes, Connor and Cartwright, Robert and Palem, Krishna and Lu, Shan and Hoffmann, Henry},
  title = {GOAL: Supporting General and Dynamic Adaptation in Computing Systems},
  year = {2022},
  isbn = {9781450399098},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3563835.3567655},
  doi = {10.1145/3563835.3567655},
  abstract = {Adaptive computing systems automatically monitor their behavior and dynamically adjust their own configuration parameters—or knobs—to ensure that user goals are met despite unpredictable external disturbances to the system. A major limitation of prior adaptation frameworks is that their internal adaptation logic is implemented for a specific, narrow set of goals and knobs, which impedes the development of complex adaptive systems that must meet different goals using different sets of knobs for different deployments, or even change goals during one deployment. To overcome this limitation we propose GOAL, an adaptation framework distinguished by its virtualized adaptation logic implemented independently of any specific goals or knobs. GOAL supports this logic with a programming interface allowing users to define and manipulate a wide range of goals and knobs within a running program. We demonstrate GOAL’s benefits by using it re-implement seven different adaptive systems from the literature, each of which has a different set of goals and knobs. We show GOAL’s general approach meets goals as well as prior approaches designed for specific goals and knobs. In dynamic scenarios where the goals and knobs are modified at runtime, GOAL achieves 93.7},
  booktitle = {Proceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software},
  pages = {16–32},
  numpages = {17},
  keywords = {domain-specific language, control theory, adaptive computing, resource allocation, energy},
  location = {Auckland, New Zealand},
  series = {Onward! 2022},
  ISIArea = {CAS, OTH}
}
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