An empirical study of modeling self-management capabilities in autonomic systems using case-based reasoning |
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| Affiliation: | 1. Department of Pediatric Surgery, Children''s Hospital Boston-Harvard Medical School, Boston, MA, USA;2. Department of Pediatric Surgery, St. Louis Children''s Hospital, Washington University, St. Louis, MO, USA;3. Department of Surgery, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA;4. Division of Pediatric Surgery, Ann and Robert H. Lurie Children''s Hospital of Chicago, Chicago, IL, USA;5. Department of Pediatric Surgery, Children''s Hospital of Wisconsin, Medical College of Wisconsin, Milwaukee, WI, USA;6. Department of Pediatric Surgery, Seattle Children''s Hospital, University of Washington, Seattle, WA, USA;7. Division of Pediatric Surgery, Primary Children''s Hospital, University of Utah, Salt Lake City, UT, USA;8. Department of Anesthesia, Children''s Hospital Boston-Harvard Medical School, Boston, MA, USA;1. Division of Gastroenterology, Gulhane Military Medical Academy, Ankara, Turkey;2. Division of Gastroenterology, Faculty of Medicine, University of Gaziantep, Gaziantep, Turkey;3. Department of Radiology, Gulhane Military Medical Academy, Ankara, Turkey;4. Department of Pathology, Gulhane Military Medical Academy, Ankara, Turkey;5. Division of Endocrinology, Gulhane Military Medical Academy, Ankara, Turkey;1. The Third Department of Tumor Surgery, Tangshan Gongren Hospital, People''s Republic of China;2. Department of Rehabilitation Medicine, Tangshan Gongren Hospital, People''s Republic of China;3. The Department of Radiology, Tangshan Gongren Hospital, People''s Republic of China |
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| Abstract: | Autonomic systems promise to inject self-managing capabilities in software systems. The major objectives of autonomic computing are to minimize human intervention and to enable a seamless self-adaptive behavior in the software systems. To achieve self-managing behavior, various methods have been exploited in past. Case-based reasoning (CBR) is a problem solving paradigm of artificial intelligence which exploits past experience, stored in the form of problem–solution pairs. We have applied CBR based modeling approach to achieve autonomicity in software systems. The proposed algorithms have been described and CBR implementation on externalization and internalization architectures of autonomic systems using two case studies RUBiS and Autonomic Forest Fire Application (AFFA) have been shown. The study highlights the effect of 10 different similarity measures, the role of adaptation and the effect of changing nearest neighborhood cardinality for a CBR solution cycle in autonomic managers. The results presented in this paper show that the proposed CBR based autonomic model exhibits 90–98% accuracy in diagnosing the problem and planning the solution. |
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