TY - GEN
T1 - Towards Workload-aware Cloud Efficiency
T2 - 16th ACM/SPEC International Conference on Performance, ICPE 2025
AU - Parayil, Anjaly
AU - Zhang, Jue
AU - Qin, Xiaoting
AU - Goiri, Íñigo
AU - Huang, Lexiang
AU - Zhu, Timothy
AU - Bansal, Chetan
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/5/5
Y1 - 2025/5/5
N2 - Cloud providers introduce features and optimizations to improve efficiency and reliability, such as Spot VMs, Harvest VMs, oversubscription, and auto-scaling. To use these effectively, it's important to understand workload characteristics. However, workload characterization can be complex and difficult to scale manually due to multiple signals involved. In this study, we conduct the first large-scale empirical study of first-party workloads at Microsoft to understand their characteristics. Through this 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 and identifies potential areas for future research.
AB - Cloud providers introduce features and optimizations to improve efficiency and reliability, such as Spot VMs, Harvest VMs, oversubscription, and auto-scaling. To use these effectively, it's important to understand workload characteristics. However, workload characterization can be complex and difficult to scale manually due to multiple signals involved. In this study, we conduct the first large-scale empirical study of first-party workloads at Microsoft to understand their characteristics. Through this 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 and identifies potential areas for future research.
UR - https://www.scopus.com/pages/publications/105007307641
UR - https://www.scopus.com/pages/publications/105007307641#tab=citedBy
U2 - 10.1145/3676151.3722008
DO - 10.1145/3676151.3722008
M3 - Conference contribution
AN - SCOPUS:105007307641
T3 - ICPE 2025 - Proceedings of the 16th ACM/SPEC International Conference on Performance
SP - 136
EP - 146
BT - ICPE 2025 - Proceedings of the 16th ACM/SPEC International Conference on Performance
PB - Association for Computing Machinery, Inc
Y2 - 5 May 2025 through 9 May 2025
ER -