A Robust Approach for Hotspots Prevention and Resolution in Cloud Services

Abstract

Cloud providers offer virtual machines (VM) located in physical machines (PM) using the “pay-as-you-go” model to satisfy emerging demand for online computational services. If the instantaneous utilized capacity requested by VMs exceeds a certain threshold of the total capacity a PM can offer, a hotspot happens and may cause unacceptable VM performance degradation. Hotspots can be resolved by relocating some VMs to other PMs using live migration. However, the problem of selecting which VM(s) to release is challenging because the utilized capacity demanded by VMs changes continuously over time. In this work, we propose a Predicted Mixed Integer Linear Programming (MILP) Robust Solver (PMRS), which predicts the utilized capacity range of each VM and applies the Γ-robustness theory to ensure that PM is hotspot-safe with desired probability. The latter allows us to formulate the hotspot resolution as a Γ-robust knapsack problem (Γ-RKP) that can be solved by a novel MILP model. Extensive experiments based on real-trace data and large-scale synthetic data demonstrate the effectiveness of the PMRS. More encouragingly, the application of the PMRS in the real-production environment benefits Huawei Cloud by resolving all existing and 94%+ potential future hotspots with minimal migration overhead.

Keywords: Cloud service, hotspot prevention, Γ-robustness, knapsack problem, data-driven optimization

JEL Classification: C61, C63