Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms

By Eli Cortez C. Vilarinho, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Felipe M. C. Da Fontoura, Ricardo Bianchini. Symposium on Operating Systems Principles, 2017

753 citations recorded by the source corpus.

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Who Limits the Resource Efficiency of My Datacenter: An Analysis of Alibaba Datacenter Traces (2019, background): “Since then, industry and academia have both devoted much effort to improve resource efficiency in datacenters [5, 7, 19].”
Twine: A Unified Cluster Management System for Shared Infrastructure (2020, background): “Past work overcommits CPU and memory by colocating batch jobs and online services [11, 22, 39, 43].”
Understanding and Co-designing the Data Ingestion Pipeline for Industry-Scale RecSys Training (2021, background): “Currently, users rely on various services to determine howmuchDPP parallelism to dedicate to each job; predicting and right-sizing resources across ML models and use cases is a challenging task and beyond the scope of this paper [14, 27].”
Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider (2020, methodology): “For example, data from Azure [12] shows that 63% of all VM allocations last longer than 15 minutes, and only less than 8% of the VMs last less 5 minutes or less.”