Learning to dynamically select cost optimal schedulers in cloud computing environments
File(s)
Accepted version
Author(s)
Tuli, Shreshth
Casale, Giuliano
Jennings, Nicholas R
Type
Journal Article
Abstract
The operational cost of a cloud computing platform is one of the most significant Quality of Service (QoS) criteria for schedulers, crucial to keep up with the growing computational demands. Several data-driven deep neural network (DNN)-based schedulers have been proposed in recent years that outperform alternative approaches by providing scalable and effective resource management for dynamic workloads. However, state-of-the-art schedulers rely on advanced DNNs with high computational requirements, implying high scheduling costs. In non-stationary contexts, the most sophisticated schedulers may not always be required, and it may be sufficient to rely on low-cost schedulers to temporarily save operational costs. In this work, we propose MetaNet, a surrogate model that predicts the operational costs and scheduling overheads of a large number of DNNbased schedulers and chooses one on-the-fly to jointly optimize job scheduling and execution costs. This facilitates improvements in execution costs, energy usage and service level agreement violations of up to 11%, 43% and 13% compared to the state-of-the-art methods.
Date Issued
2023-04-26
Date Acceptance
2023-04-01
Citation
ACM SIGMETRICS Performance Evaluation Review, 2023, 50 (4), pp.29-31
ISSN
0163-5999
Publisher
Association for Computing Machinery (ACM)
Start Page
29
End Page
31
Journal / Book Title
ACM SIGMETRICS Performance Evaluation Review
Volume
50
Issue
4
Copyright Statement
© 2023 The Author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM SIGMETRICS Performance Evaluation Review, https://doi.org/10.1145/3595244.3595255
Identifier
http://dx.doi.org/10.1145/3595244.3595255
Publication Status
Published
Date Publish Online
2023-04-27