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

2025-04-23

IEEE International Symposium on Quality Electronic Design

AI Summary

In S-NUCA many-core architectures, non-uniform cache access latency leads to unpredictable QoS and challenges in energy-efficiency optimization. Method: This paper proposes a QoS-first, energy-aware two-stage co-scheduling framework—first deeply integrating the Application Heartbeats paradigm into S-NUCA scheduling, jointly optimizing cache latency modeling, DVFS-based dynamic voltage-frequency scaling, and cross-region task migration under strict heartbeat-based QoS constraints. A lightweight heartbeat monitoring module and QoS-aware scheduler are implemented atop an extended HotSniper simulator, enabling decoupled yet coordinated optimization of performance guarantees and energy efficiency. Contribution/Results: Experimental evaluation shows that, while strictly satisfying QoS requirements, the proposed approach reduces system energy consumption by 18.7% compared to state-of-the-art methods, significantly improving both energy efficiency and QoS predictability.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Satisfiability
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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 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.

Problem

• Optimizing performance and energy in S-NUCA many-cores
• Managing QoS with dynamic task and frequency control
• Reducing energy consumption without compromising QoS targets

Innovation

• Dynamic QoS management via task migration
• Core clock frequency adjustment for efficiency
• Integrated heartbeat framework for monitoring