针对多NUMA云环境动态虚拟机调度难题，提出对称保持的强化学习方法SPANE。
Symmetry-Preserving Architecture for Multi-NUMA Environments (SPANE): A Deep Reinforcement Learning Approach for Dynamic VM Scheduling
- 利用深度强化学习捕捉多NUMA架构下的调度对称性，提升学习效率。
- 在华为东1数据集上使虚拟机平均等待时间减少45%。
- 兼具理论分析与实际性能优势，适合云资源管理研究者参考。
随着云计算的发展，云服务商越来越多采用多NUMA（非均匀内存访问）架构，带来虚拟机（VM）调度的新挑战。为此，我们提出了多NUMA物理机上的动态虚拟机分配问题（DVAMP），并形式化定义了离线与在线两种版本为混合整数线性规划问题，为分析提供严谨数学基础。推导出贪心在线算法的紧致性能界，揭示最优性差距随物理机数量和虚拟机寿命变化的关系。针对DVAMP挑战，提出SPANE（对称保持的多NUMA环境架构），一种新颖的深度强化学习方法，可保持物理机状态任意排列下的结果不变性，从而提升学习效率与解质量。在华为东1数据集上的大量实验表明，SPANE优于现有基线，平均虚拟机等待时间降低45%。本工作为云资源管理提供了理论洞见与实用解决方案，填补了多NUMA环境下虚拟机调度的研究空白，显著提升真实云系统性能。
原文摘要 · Abstract (English)
As cloud computing continues to evolve, the adoption of multi-NUMA (Non-Uniform Memory Access) architecture by cloud service providers has introduced new challenges in virtual machine (VM) scheduling. To address these challenges and more accurately reflect the complexities faced by modern cloud environments, we introduce the Dynamic VM Allocation problem in Multi-NUMA PM (DVAMP). We formally define both offline and online versions of DVAMP as mixed-integer linear programming problems, providing a rigorous mathematical foundation for analysis. A tight performance bound for greedy online algorithms is derived, offering insights into the worst-case optimality gap as a function of the number of physical machines and VM lifetime variability. To address the challenges posed by DVAMP, we propose SPANE (Symmetry-Preserving Architecture for Multi-NUMA Environments), a novel deep reinforcement learning approach that exploits the problem's inherent symmetries. SPANE produces invariant results under arbitrary permutations of physical machine states, enhancing learning efficiency and solution quality. Extensive experiments conducted on the Huawei-East-1 dataset demonstrate that SPANE outperforms existing baselines, reducing average VM wait time by 45%. Our work contributes to the field of cloud resource management by providing both theoretical insights and practical solutions for VM scheduling in multi-NUMA environments, addressing a critical gap in the literature and offering improved performance for real-world cloud systems.
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