| 1. |
- Maggio, Martina, et al.
(författare)
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Comparison of Decision Making Strategies for Self-Optimization in Autonomic Computing Systems
- 2012
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Ingår i: ACM Transactions on Autonomous and Adaptive Systems. - : Association for Computing Machinery (ACM). - 1556-4665 .- 1556-4703. ; 7:4
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Tidskriftsartikel (refereegranskat)abstract
- 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. 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. Our results indicate that the most suitable decision mechanism can vary depending on the specific test case but adaptive and model predictive control systems tend to produce good performance and may work best in a priori unknown situations.
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| 2. |
- Panerati, Jacopo, et al.
(författare)
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Coordination of Independent Loops in Self-Adaptive Systems
- 2014
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Ingår i: ACM Transactions on Reconfigurable Technology and Systems. - : Association for Computing Machinery (ACM). - 1936-7406 .- 1936-7414. ; 7:2, s. 12-16
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Tidskriftsartikel (refereegranskat)abstract
- Nowadays, the same piece of code should run on different architectures, providing performance guarantees in a variety of environments and situations. To this end, designers often integrate existing systems with ad-hoc adaptive strategies able to tune specific parameters that impact performance or energy—for example, frequency scaling. However, these strategies interfere with one another and unpredictable performance degradation may occur due to the interaction between different entities. In this article, we propose a software approach to reconfiguration when different strategies, called loops, are encapsulated in the system and are available to be activated. Our solution to loop coordination is based on machine learning and it selects a policy for the activation of loops inside of a system without prior knowledge. We implemented our solution on top of GNU/Linux and evaluated it with a significant subset of the PARSEC benchmark suite.
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| 3. |
- Panerati, Jacopo, et al.
(författare)
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On Self-adaptive Resource Allocation through Reinforcement Learning
- 2013
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Ingår i: NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 2013. - 9781467363822 ; , s. 23-30
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Konferensbidrag (refereegranskat)abstract
- Autonomic computing was proposed as a promising solution to overcome the complexity of modern systems, which is causing management operations to become increasingly difficult for human beings. This work proposes the Adaptation Manager, a comprehensive framework to implement autonomic managers capable of pursuing some of the objectives of autonomic computing (i.e., self-optimization and self-healing). The Adaptation Manager features an active performance monitoring infrastructure and two dynamic knobs to tune the scheduling decisions of an operating system and the working frequency of cores. The Adaptation Manager exploits artificial intelligence and reinforcement learning to close the Monitor-Plan-Analyze- Execute with Knowledge adaptation loop at the very base of every autonomic manager. We evaluate the Adaptation Manager, and especially the adaptation policies it learns by means of reinforcement learning, using a set of representative applications for multicore processors and show the effectiveness of our prototype on commodity computing systems.
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