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Research challenges of autonomic computing
Abstract
Autonomic computing is a grand-challenge vision of the future in which computing systems will manage themselves in accordance with high-level objectives specified by humans. The IT industry recognizes that meeting this challenge is imperative; otherwise, IT systems will soon become virtually impossible to administer. But meeting this challenge is also extremely difficult, and will require a worldwide collaboration among the best minds of academia and industry. In the hope of motivating researchers in relevant areas to apply their expertise to this vitally important problem, I outline some of the main scientific and engineering challenges that collectively make up the grand challenge of autonomic computing, and provide pointers to initial efforts to address these challenges.
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Citations
Cited by
Self-adaptive software: Landscape and research challenges
Salehie, M., & Tahvildari, L. (2009) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/1516533.1516538 · Cited by 1,266
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
Tesauro, G. (2007) · IEEE Internet Computing · doi:10.1109/mic.2007.21 · Cited by 143
Metadata
- Authors
- Kephart, J. O.
- Venue
- Proceedings of the 27th international conference on Software engineering - ICSE '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 15
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2049430277
- Cited by
- 268
- References
- 0
- Type
- conference abstract
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
