  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.
