Cloud providers offer virtual machines (VMs) located on physical machines (PMs) to meet the increasing demand for computational services. When the instantaneous utilized capacities of VMs exceed a PM's threshold, hotspots form and degrade VM performance. To resolve hotspots, some VMs must be live migrated to other PMs, but selecting which VMs is challenging as their utilized capacities change continuously. We propose a Predicted Mixed Integer Linear Programming (MILP) Robust Solver (PMRS) that applies Γ-robustness theory to ensure PMs are hotspot-safe with a desired probability. PMRS uses a “prediction + optimization” framework that first predicts VMs' future behaviors and then formulates the problem as a Γ-robust knapsack problem (Γ-RKP) solvable with a novel MILP model. Experiments with real-trace and synthetic data demonstrate PMRS's effectiveness. Moreover, we apply PMRS in a real production environment in Huawei Cloud, and observe significant benefits in resolving existing hotspots and 94%+ potential future hotspots with minimal migration cost.

云提供商提供位于物理机 (PM) 上的虚拟机 (VM)，以满足对计算服务日益增长的需求。当虚拟机的瞬时使用容量超过 PM 的阈值时，就会形成热点并降低虚拟机的性能。为了解决热点问题，一些虚拟机必须实时迁移到其他 PM，但选择哪些虚拟机具有挑战性，因为其使用容量不断变化。我们提出了一种预测混合整数线性规划(MILP) 鲁棒求解器 (PMRS)，它应用 Γ 鲁棒性理论来确保 PM 以所需的概率实现热点安全。PMRS 使用“预测 + 优化”框架，首先预测 VM 的未来行为，然后将问题表述为可通过新颖的 MILP 模型解决的 γ 鲁棒背包问题 (γ-RKP)。真实痕迹和合成数据的实验证明了 PMRS 的有效性。此外，我们将 PMRS 应用于华为云的真实生产环境中，并观察到以最低的迁移成本解决现有热点和 94% 以上的潜在未来热点的显着优势。