A Comparison of Autonomic Decision Making Techniques
Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal despite changing environmental conditions and demands. Different decision mechanisms are considered in the literature, but in t...
| Main Authors: | , , , , |
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| Other Authors: | |
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2011
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| Online Access: | http://hdl.handle.net/1721.1/62020 |
| _version_ | 1826195952494444544 |
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| author | Maggio, Martina Hoffmann, Henry Santambrogio, Marco D. Agarwal, Anant Leva, Alberto |
| author2 | Anant Agarwal |
| author_facet | Anant Agarwal Maggio, Martina Hoffmann, Henry Santambrogio, Marco D. Agarwal, Anant Leva, Alberto |
| author_sort | Maggio, Martina |
| collection | MIT |
| description | Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal despite changing environmental conditions and demands. Different decision mechanisms are considered in the literature, but in the vast majority of the cases a single technique is applied to a given instance of the problem. This paper proposes a comparison of some state of the art approaches for decision making, applied to a self-optimizing autonomic system that allocates resources to a software application, which provides direct performance feedback at runtime. The Application Heartbeats framework is used to provide the sensor data (feedback), and a variety of decision mechanisms, from heuristics to control-theory and machine learning, are investigated. The results obtained with these solutions are compared by means of case studies using standard benchmarks. |
| first_indexed | 2024-09-23T10:18:26Z |
| id | mit-1721.1/62020 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:18:26Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | mit-1721.1/620202019-04-12T11:56:39Z A Comparison of Autonomic Decision Making Techniques Maggio, Martina Hoffmann, Henry Santambrogio, Marco D. Agarwal, Anant Leva, Alberto Anant Agarwal Computer Architecture Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal despite changing environmental conditions and demands. Different decision mechanisms are considered in the literature, but in the vast majority of the cases a single technique is applied to a given instance of the problem. This paper proposes a comparison of some state of the art approaches for decision making, applied to a self-optimizing autonomic system that allocates resources to a software application, which provides direct performance feedback at runtime. The Application Heartbeats framework is used to provide the sensor data (feedback), and a variety of decision mechanisms, from heuristics to control-theory and machine learning, are investigated. The results obtained with these solutions are compared by means of case studies using standard benchmarks. 2011-04-01T19:30:09Z 2011-04-01T19:30:09Z 2011-04-01 http://hdl.handle.net/1721.1/62020 MIT-CSAIL-TR-2011-019 10 p. application/pdf |
| spellingShingle | Maggio, Martina Hoffmann, Henry Santambrogio, Marco D. Agarwal, Anant Leva, Alberto A Comparison of Autonomic Decision Making Techniques |
| title | A Comparison of Autonomic Decision Making Techniques |
| title_full | A Comparison of Autonomic Decision Making Techniques |
| title_fullStr | A Comparison of Autonomic Decision Making Techniques |
| title_full_unstemmed | A Comparison of Autonomic Decision Making Techniques |
| title_short | A Comparison of Autonomic Decision Making Techniques |
| title_sort | comparison of autonomic decision making techniques |
| url | http://hdl.handle.net/1721.1/62020 |
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