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Intelligent Management of Mobile Systems Through Computational Self-Awareness
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Author(s): Bryan Donyanavard (University of California, Irvine, USA), Amir M. Rahmani (University of California, Irvine, USA), Axel Jantsch (TU Wien, Austria), Onur Mutlu (Swiss Federal Institute of Technology in Zurich, Switzerland)and Nikil Dutt (University of California, Irvine, USA)
Copyright: 2021
Pages: 33
Source title:
Handbook of Research on Methodologies and Applications of Supercomputing
Source Author(s)/Editor(s): Veljko Milutinović (Indiana University, Bloomington, USA)and Miloš Kotlar (University of Belgrade, Serbia)
DOI: 10.4018/978-1-7998-7156-9.ch004
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Abstract
Runtime resource management for many-core systems is increasingly complex. The complexity can be due to diverse workload characteristics with conflicting demands, or limited shared resources such as memory bandwidth and power. Resource management strategies for many-core systems must distribute shared resource(s) appropriately across workloads, while coordinating the high-level system goals at runtime in a scalable and robust manner. In this chapter, the concept of reflection is used to explore adaptive resource management techniques that provide two key properties: the ability to adapt to (1) changing goals at runtime (i.e., self-adaptivity) and (2) changing dynamics of the modeled system (i.e., self-optimization). By supporting these self-awareness properties, the system can reason about the actions it takes by considering the significance of competing objectives, user requirements, and operating conditions while executing unpredictable workloads.
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