TR-2017-06
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.
Abstract
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).
