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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. |
- Bartolini, Davide Basilio, et al.
(författare)
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The Autonomic Operating System Research Project - Achievements and Future Directions
- 2013
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Konferensbidrag (refereegranskat)abstract
- Traditionally, hypervisors, operating systems, and runtime systems have been providing an abstraction layer over the bare-metal hardware. Traditional abstractions, however, do not consider for non-functional requirements such as system-level constraints or users' objectives. As these requirements are gaining increasing importance, researchers are looking into making user-specified and system-level objectives first-class citizens in the computer systems' realm. This paper describes the Autonomic Operating System (AcOS) project; AcOS enhances commodity operating systems with an autonomic layer that enables self-* properties through adaptive resource allocation. With AcOS, we investigate intelligent resource allocation to achieve user-specified service-level objectives on application performance and to respect system-level thresholds on CPU temperature. We give a broad overview of \system, elaborate on its achievements, and discuss research perspectives.
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| 4. |
- Hoffmann, Henry, et al.
(författare)
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A Generalized Software System for Accurate and Efficient Management of Application Performance Goals
- 2013
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Konferensbidrag (refereegranskat)abstract
- A number of techniques have been proposed to provide run- time performance guarantees while minimizing power consumption. One drawback of existing approaches is that they work only on a fixed set of components (or actuators) that must be specified at design time. If new components become available, these management systems must be redesigned and reimplemented. In this paper, we propose PTRADE, a novel performance management framework that is general with respect to the components it manages. PTRADE can be deployed to work on a new system with different components without redesign and reimplementation. PTRADE’s generality is demonstrated through the management of performance goals for a variety of benchmarks on two different Linux/x86 systems and a simulated 128-core system, each with different components governing power and performance tradeoffs. Our experimental results show that PTRADE provides generality while meeting performance goals and ap- proaching optimal power consumption. PTRADE consumes only 7% more power than optimal on the Linux/x86 systems and 3% more power than optimal on the simulated many-core.
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| 5. |
- 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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| 6. |
- Maggio, Martina, et al.
(författare)
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Power Optimization in Embedded Systems via Feedback Control of Resource Allocation
- 2013
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Ingår i: IEEE Transactions on Control Systems Technology. - 1558-0865. ; 21:1, s. 239-246
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Tidskriftsartikel (refereegranskat)abstract
- Embedded systems often operate in so variable con- ditions that design can only be carried out for some worst-case scenario. This leads to over-provisioned resources, and undue power consumption. Feedback control is an effective (and not yet fully explored) way to tailor resource usage online, thereby making the system behave and consume as if it was optimized for each specific utilization case. A control-theoretical methodology is here proposed to complement architecture design in a view to said tailoring. Experimental results show that a so addressed architecture meets performance requirements, while consuming less power than any fixed (i.e., uncontrolled) one capable of attaining the same goals. Also, the methodology naturally induces computationally lightweight control laws.
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| 7. |
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| 8. |
- 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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| 9. |
- Sironi, Filippo, et al.
(författare)
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ThermOS: System Support for Dynamic Thermal Management of Chip Multi-Processors
- 2013
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Ingår i: 22nd International Conference on Parallel Architectures and Compilation Techniques (PACT), 2013. - 1089-795X. - 9781479910182 ; , s. 41-50
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Konferensbidrag (refereegranskat)abstract
- Constraining the temperature of computing systems has become a dominant design aspect. The supply voltage decrease has lost its pace even though the feature size is shrinking constantly. This results in an increased number of transistors per unit of area and hence a growing power density. Researchers started investigating dynamic thermal management techniques to trade-off performance for energy consumption, thus reducing power density and temperature. Software dynamic thermal management displayed some advantages with respect to hardware solutions found in chip multi-processors. Hardware dynamic thermal management indiscriminately trades-off performance for energy consumption, possibly affecting established service-level agreements, while software solutions can avoid this issue. We propose ThermOS, a framework that harnesses formal, feedback control and idle cycle injection to reduce thermal emergencies while showing better efficiency than common dynamic thermal management techniques like dynamic voltage and frequency scaling.
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