The SAP Cloud Infrastructure Dataset: A Reality Check of Scheduling and Placement of VMs in Cloud Computing

TL;DR

This paper presents a detailed dataset and analysis of VM scheduling and placement in SAP's cloud infrastructure, revealing inefficiencies and guiding future optimization algorithms.

Contribution

It introduces a unique, fine-grained telemetry dataset from SAP's cloud environment and highlights key scheduling issues, providing a foundation for improved resource management.

Findings

CPU contention exceeds 40% in some cases

CPU ready times reach up to 220 seconds

Over 80% of VMs underutilize allocated resources

Abstract

Allocating resources in a distributed environment is a fundamental challenge. In this paper, we analyze the scheduling and placement of virtual machines (VMs) in the cloud platform of SAP, the world's largest enterprise resource planning software vendor. Based on data from roughly 1,800 hypervisors and 48,000 VMs within a 30-day observation period, we highlight potential improvements for workload management. The data was measured through observability tooling that tracks resource usage and performance metrics across the entire infrastructure. In contrast to existing datasets, ours uniquely offers fine-grained time-series telemetry data of fully virtualized enterprise-level workloads from both long-running and memory-intensive SAP S/4HANA and diverse, general-purpose applications. Our key findings include several suboptimal scheduling situations, such as CPU resource contention exceeding 40%, CPU ready times of up to 220 seconds, significantly imbalanced compute hosts with a maximum CPU~utilization on intra-building block hosts of up to 99%, and overprovisioned CPU and memory resources resulting into over 80% of VMs using less than 70% of the provided resources. Bolstered by these findings, we derive requirements for the design and implementation of novel placement and scheduling algorithms and provide guidance to optimize resource allocations. We make the full dataset used in this study publicly available to enable data-driven evaluations of scheduling approaches for large-scale cloud infrastructures in future research.