Energy-Efficient QoS-Aware Scheduling for S-NUCA Many-Cores

TL;DR

This paper presents a QoS-aware scheduling policy for S-NUCA many-core systems that maintains application performance and reduces energy consumption by dynamically managing task migrations and core frequencies.

Contribution

It introduces a novel QoS management policy with application heartbeat monitoring, extending the HotSniper simulator for energy-efficient scheduling in S-NUCA architectures.

Findings

Achieves 18.7% energy savings over existing methods.

Effectively maintains application QoS levels.

Demonstrates improved energy efficiency in simulations.

Abstract

Optimizing performance and energy efficiency in many-core processors, especially within Non-Uniform Cache Access (NUCA) architectures, remains a critical challenge. The performance heterogeneity inherent in S-NUCA systems complicates task scheduling due to varying cache access latencies across cores. This paper introduces a novel QoS management policy to maintain application execution within predefined Quality of Service (QoS) targets, measured using the Application Heartbeats framework. QoS metrics like Heartbeats ensure predictable application performance in dynamic computing environments. The proposed policy dynamically controls QoS by orchestrating task migrations within the S-NUCA many-core system and adjusting the clock frequency of cores. After satisfying the QoS objectives, the policy optimizes energy efficiency, reducing overall system energy consumption without compromising performance constraints. Our work leverages the state-of-the-art multi-/many-core simulator {\em HotSniper}. We have extended it with two key components: an integrated heartbeat framework for precise, application-specific performance monitoring, and our QoS management policy that maintains application QoS requirements while minimizing the system's energy consumption. Experimental evaluations demonstrate that our approach effectively maintains desired QoS levels and achieves 18.7\% energy savings compared to state-of-the-art scheduling methods.