Hotspot resolution in cloud computing: A Γ-robust knapsack approach for virtual machine migration

Abstract

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.