Self-Adaptive QoS Management of Computation and Communication Resources in Many-Core SoCs

Abstract

Providing quality of service (QoS) for many-core systems with dynamic application admission is challenging due to the high amount of resources to manage and the unpredictability of computation and communication events. Related works propose a self-adaptive QoS mechanism concerned either in communication or computation resources, lacking, however, a comprehensive QoS management of both. Assuming a many-core system with QoS monitoring, runtime circuit-switching establishment, task migration, and a soft real-time task scheduler, this work fills this gap by proposing a novel self-adaptive QoS management. The contribution of this proposal comes with the following features in the QoS management: ( i ) comprehensiveness, by covering communication and computation resources; ( ii ) online, adopting the ODA (Observe, Decide, Act) runtime closed-loop adaptation; and ( iii ) reactive and proactive decisions, by using a dynamic application profile extraction technique, which enables the QoS management to be aware of the profile of running applications, allowing it to take proactive decisions based on a prediction analysis. The proposed QoS management adopts a decentralized organization by partitioning the system in clusters, each one managed by a dedicated processor, making the proposal scalable. Results show that the proactive feature accurately extracts the applications’ profile, and can prevent future QoS violations. The synergy of reactive and proactive decisions was able to sustain QoS, reducing the deadline miss rate by 99.5% with a severe disturbance in communication and computation levels, and avoiding deadline misses up to 70% of system utilization.