Hoang V, Hung LH, Perez D, Deng H, Schooley R, Arumilli N, Yeung KY, Lloyd W. Container Profiler: Profiling resource utilization of containerized big data pipelines.
Gigascience 2022;
12:giad069. [PMID:
37624874 PMCID:
PMC10452954 DOI:
10.1093/gigascience/giad069]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 08/02/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND
This article presents the Container Profiler, a software tool that measures and records the resource usage of any containerized task. Our tool profiles the CPU, memory, disk, and network utilization of containerized tasks collecting over 60 Linux operating system metrics at the virtual machine, container, and process levels. The Container Profiler supports performing time-series profiling at a configurable sampling interval to enable continuous monitoring of the resources consumed by containerized tasks and pipelines.
RESULTS
To investigate the utility of the Container Profiler, we profile the resource utilization requirements of a multistage bioinformatics analytical pipeline (RNA sequencing using unique molecular identifiers). We examine profiling metrics to assess patterns of CPU, disk, and network resource utilization across the different stages of the pipeline. We also quantify the profiling overhead of our Container Profiler tool to assess the impact of profiling a running pipeline with different levels of profiling granularity, verifying that impacts are negligible.
CONCLUSIONS
The Container Profiler provides a useful tool that can be used to continuously monitor the resource consumption of long and complex containerized applications that run locally or on the cloud. This can help identify bottlenecks where more resources are needed to improve performance.
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