Towards Cloud Efficiency with Large-scale Workload Characterization

TL;DR

This paper presents a large-scale empirical analysis of Microsoft cloud workloads to understand their characteristics, aiming to improve cloud efficiency and reliability through better workload understanding.

Contribution

It provides the first comprehensive large-scale characterization of cloud workloads, revealing key features impacting efficiency and reliability.

Findings

Identified critical workload features affecting cloud performance

Analyzed variation of workload characteristics across different services

Provided insights for optimizing cloud resource management

Abstract

Cloud providers introduce features (e.g., Spot VMs, Harvest VMs, and Burstable VMs) and optimizations (e.g., oversubscription, auto-scaling, power harvesting, and overclocking) to improve efficiency and reliability. To effectively utilize these features, it's crucial to understand the characteristics of workloads running in the cloud. However, workload characteristics can be complex and depend on multiple signals, making manual characterization difficult and unscalable. In this study, we conduct the first large-scale examination of first-party workloads at Microsoft to understand their characteristics. Through an empirical study, we aim to answer the following questions: (1) What are the critical workload characteristics that impact efficiency and reliability on cloud platforms? (2) How do these characteristics vary across different workloads? (3) How can cloud platforms leverage these insights to efficiently characterize all workloads at scale? This study provides a deeper understanding of workload characteristics and their impact on cloud performance, which can aid in optimizing cloud services. Additionally, it identifies potential areas for future research.