发表机构
University of Thessaly; Trinity College Dublin; University of Amsterdam（色萨利大学; 都柏林三一学院; 阿姆斯特丹大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出可靠性平衡调度策略，通过预测任务影响并惩罚失衡，在随机连续混合工作负载上提升平均和最小Rvalue，同时降低各指标标准差。
AI 中文摘要
多核系统中的电迁移退化在很大程度上取决于运行时工作负载如何跨核心分布。任务放置影响功耗、温度和电流密度，进而随时间塑造退化的空间分布。然而，仅依据当前可靠性状态对核心进行排序的调度器，在决策时可能并不总能区分候选核心，尤其是当可靠性值在决策时刻接近时。在这种情况下，调度决策可能由次要因素驱动，而非长期退化平衡。本文研究退化平衡调度，并提出一种可靠性平衡（RB）策略，该策略预测每个候选分配的完整任务影响。RB在保护预测的Rvalue下限的同时，惩罚电迁移暴露、应力、活跃时间和任务数量方面增加的失衡。在随机连续混合工作负载上，所提出的调度器在保持完成任务的同时，将平均Rvalue提高了0.37%，最小Rvalue提高了4.35%。它分别将Rvalue、暴露、应力和活跃时间的标准差降低了10.84%、9.39%、8.35%和16.38%。
英文摘要
Electromigration degradation in manycore systems depends strongly on how runtime workload is distributed across cores. Task placement affects power, temperature, and current density, which in turn shape the spatial distribution of degradation over time. However, a scheduler that ranks cores only by their current reliability state may not always distinguish among candidate cores, especially when reliability values are close at decision time. In this case, scheduling decisions can be driven by secondary factors rather than by long-term degradation balance. This paper studies degradation-balanced scheduling and proposes a Reliability-Balanced (RB) policy that predicts the full-task effect of each candidate assignment. RB penalizes added imbalance in electromigration exposure, stress, active time, and task count while guarding the predicted Rvalue floor. On randomized continuous mixed workloads, the proposed scheduler preserves completed tasks while improving average Rvalue by 0.37% and minimum Rvalue by 4.35%. It reduces Rvalue, exposure, stress, and active-time standard deviation by 10.84%, 9.39%, 8.35%, and 16.38%, respectively.
CommentsAccepted at ICECS 2026
