CALOREE: Learning Control for Predictable Latency and Low Energy

Nikita Mishra; Connor Imes; John D. Lafferty; Henry Hoffmann. 8 November, 2017.
Communicated by Henry Hoffmann.


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 environments. 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 defines 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%.

Original Document

The original document is available in PDF (uploaded 8 November, 2017 by Henry Hoffmann).