Scalable Infrastructure for Workload Characterization of Cluster Traces

Thomas van Loo, Anshul Jindal, Shajulin Benedict, Mohak Chadha, Michael Gerndt

2022

Abstract

In the recent past, characterizing workloads has been attempted to gain a foothold in the emerging serverless cloud market, especially in the large production cloud clusters of Google, AWS, and so forth. While analyzing and characterizing real workloads from a large production cloud cluster benefits cloud providers, researchers, and daily users, analyzing the workload traces of these clusters has been an arduous task due to the heterogeneous nature of data. This article proposes a scalable infrastructure based on Google’s dataproc for analyzing the workload traces of cloud environments. We evaluated the functioning of the proposed infrastructure using the workload traces of Google cloud cluster-usage-traces-v3. We perform the workload characterization on this dataset, focusing on the heterogeneity of the workload, the variations in job durations, aspects of resources consumption, and the overall availability of resources provided by the cluster. The findings reported in the paper will be beneficial for cloud infrastructure providers and users while managing the cloud computing resources, especially serverless platforms.

Paper Citation

in Harvard Style

van Loo T., Jindal A., Benedict S., Chadha M. and Gerndt M. (2022). Scalable Infrastructure for Workload Characterization of Cluster Traces. In Proceedings of the 12th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-570-8, pages 254-263. DOI: 10.5220/0011080300003200

in Bibtex Style

@conference{closer22,
author={Thomas van Loo and Anshul Jindal and Shajulin Benedict and Mohak Chadha and Michael Gerndt},
title={Scalable Infrastructure for Workload Characterization of Cluster Traces},
booktitle={Proceedings of the 12th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2022},
pages={254-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011080300003200},
isbn={978-989-758-570-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Scalable Infrastructure for Workload Characterization of Cluster Traces
SN - 978-989-758-570-8
AU - van Loo T.
AU - Jindal A.
AU - Benedict S.
AU - Chadha M.
AU - Gerndt M.
PY - 2022
SP - 254
EP - 263
DO - 10.5220/0011080300003200