CALOREE
| Autor: | John Lafferty, Connor Imes, Henry Hoffmann, Nikita Mishra |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
Speedup Computer science Distributed computing Energy consumption 02 engineering and technology 01 natural sciences Computer Graphics and Computer-Aided Design 020202 computer hardware & architecture Low energy Control system 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Latency (engineering) Software |
| Zdroj: | ASPLOS |
| ISSN: | 1558-1160 0362-1340 |
| Popis: | Many modern computing systems must provide reliable latency with minimal energy. Two central challenges arise when allocating system resources to meet these conflicting goals: (1) complexity modern hardware exposes diverse resources with complicated interactions and (2) dynamics latency must be maintained despite unpredictable changes in operating environment or input. Machine learning accurately models the latency of complex, interacting resources, but does not address system dynamics; control theory adjusts to dynamic changes, but struggles with complex resource interaction. We therefore propose CALOREE, a resource manager that learns key control parameters to meet latency requirements with minimal energy in complex, dynamic en- vironments. CALOREE breaks resource allocation into two sub-tasks: learning how interacting resources affect speedup, and controlling speedup to meet latency requirements with minimal energy. CALOREE deines a general control system whose parameters are customized by a learning framework while maintaining control-theoretic formal guarantees that the latency goal will be met. We test CALOREE's ability to deliver reliable latency on heterogeneous ARM big.LITTLE architectures in both single and multi-application scenarios. Compared to the best prior learning and control solutions, CALOREE reduces deadline misses by 60% and energy consumption by 13%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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