Container Workload Characterization Through Host System Tracing
Madeline Janecek, Naser Ezzati‐Jivan, Seyed Vahid Azhari
Abstract
The use of containers within cloud environments has become increasing popular due to their lightweight nature, scalability, and efficiency. However, as containers share their host's resources, advanced resource management techniques are essential to avoid performance impacting resource contention. Coarse measures such as CPU, disk, and network usage collected from internal agents are often considered, yet these methods may be improved upon to garner a more precise view of container workloads. In this paper, we present a container workload characterization method using host system tracing. Features derived from thread execution states are taken from tracing data to reveal container runtime behaviour. A PageRank-based algorithm is then used to identify the most significant threads for further analysis. Once this data is collected and vectorized, a two stage K-Means clustering technique is used to generate groups of containers with similar workloads. This eliminates the need for manual analysis of individual containers, and instead allows administrators to view and address container behaviours collectively. Experimental results show that our methodology can identify a variety of execution behaviours. Administrators may use these results to remove idle containers to free up system resources. Moreover, they may identify clusters of containers that are at risk of resource contention, allowing for more effective resource assignment.