Hotspot-Aware Scheduling of Virtual Machines with Overcommitment for Ultimate Utilization in Cloud Datacenters
For cloud operators, this work improves resource utilization while preventing hotspots, but the problem and approach are incremental extensions of existing bin-packing and robustness theory.
The paper tackles under-utilization in cloud datacenters by proposing a hotspot-aware VM scheduling approach that considers dynamic CPU utilization, achieving gaps of 1.6% and 3.1% compared to lower and upper bounds.
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 $Γ$-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 $α$. 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.