Can I read Acela: Predictable Datacenter-level Maintenance Job Scheduling on EtoBox?

Acela: Predictable Datacenter-level Maintenance Job Scheduling by Ding, Yi; Gao, Aijia; Ryden, Thibaud; Mitra, Kaushik; Kalmanje, Sukumar; Golany, Yanai; Carbin, Michael; Hoffmann, Henry is a scholarly article available to read on EtoBox.

What is Acela: Predictable Datacenter-level Maintenance Job Scheduling about?

Datacenter operators ensure fair and regular server maintenance by using automated processes to schedule maintenance jobs to complete within a strict time budget. Automating this scheduling problem is challenging because maintenance job duration varies based on both job type and hardware. While it is tempting to use prior machine learning techniques for predicting job duration, we find that the structure of the maintenance job scheduling problem creates a unique challenge. In particular, we show that prior machine learning methods that produce the lowest error predictions do not produce the best scheduling outcomes due to asymmetric costs. Specifically, underpredicting maintenance job duration has results in more servers being taken offline and longer server downtime than overpredicting maintenance job duration. The system cost of underprediction is much larger than that of overprediction. We present Acela, a machine learning system for predicting maintenance job duration, which uses quantile regression to bias duration predictions toward overprediction. We integrate Acela into a maintenance job scheduler and evaluate it on datasets from large-scale, production datacenters. Compare

Author

Ding, Yi; Gao, Aijia; Ryden, Thibaud; Mitra, Kaushik; Kalmanje, Sukumar; Golany, Yanai; Carbin, Michael; Hoffmann, Henry

Published

2022

Language

EN