How Different are the Cloud Workloads? Characterizing Large-Scale Private and Public Cloud Workloads
- Xiaoting Qin ,
- Minghua Ma ,
- Yueng Zhao ,
- Jue Zhang ,
- Chao Du ,
- Yudong Liu ,
- A. Parayil ,
- Chetan Bansal ,
- Saravan Rajmohan ,
- Íñigo Goiri ,
- Eli Cortez ,
- Si Qin ,
- Qingwei Lin 林庆维 ,
- Dongmei Zhang
DSN |
Organized by IEEE
With the rapid development of cloud systems, an increasing number of service workloads are deployed in the private cloud and/or public cloud. Although large cloud providers such as Azure and Google have published workload traces in the past, prior work has not focused on analyzing and characterizing the differences between private and public cloud workloads in detail. Based on our experience working with Azure, one of the most widely used cloud platforms in the world, we find that the workload characteristics are different between the private and
public cloud workloads. Specifically, compared with the public cloud workloads, the private cloud workloads tend to be more homogeneous in both deployment sizes and utilization patterns, more static with occasional bursts in deployment characteristics, and more region-agnostic regarding the sensitivity to deployed regions. Our findings gain several insights and implications on cloud management and motivate us to build a centralized workload knowledge base.