Hotspot-Aware Scheduling of Virtual Machines with Overcommitment for Ultimate Utilization in Cloud Datacenters

TL;DR

This paper proposes a hotspot-aware VM scheduling approach using probabilistic bin packing and AI prediction to improve resource utilization and prevent hotspots in cloud datacenters.

Contribution

It introduces Probabilistic k-Bins Packing with $b3$-robustness and a novel scheduling algorithm, CloseRadiusFit, for online VM scheduling considering dynamic CPU utilization.

Findings

CloseRadiusFit achieves gaps of 1.6% and 3.1% compared to bounds.

The approach effectively prevents hotspots with a specified probability.

The offline MILP model validates the near-optimality of the scheduling.

Abstract

We address the problem of under-utilization of resources in datacenters during cloud operations, specifically focusing on the challenge of online virtual machine (VM) scheduling. Rather than following the traditional approach of scheduling VMs based solely on their static flavors, we take into account their dynamic CPU utilization. We employ $\Gamma$-robustness theory to manage the dynamic nature and introduce a novel variant of bin packing - Probabilistic k-Bins Packing (PkBP), which theoretically protects the Physical Machines (PMs) from hotspots formation within a specified probability $\alpha$. We develop a scheduling algroithm named CloseRadiusFit and cold-start AI based prediction algorithms for the online version of PkBP. To verify the quality of our approach towards the optimal solutions, we solve the Offline PkBP problem by designing a novel Mixed Integer Linear Programming (MILP) model and a combination of numerical upper and lower bounds. Our experimental results demonstrate that CloseRadiusFit achieves narrow gaps of 1.6% and 3.1% when compared to the lower and upper bounds, respectively.