Predoaia, Ionut orcid.org/0000-0002-2009-4054 and García-López, Pedro (2023) Leveraging Intra-Function Parallelism in Serverless Machine Learning. In: WoSC '23: Proceedings of the 9th International Workshop on Serverless Computing. 9th International Workshop on Serverless Computing, WoSC '23, 11-15 Dec 2023 WoSC '23 . ACM , ITA , 36–41.
Abstract
Running stateful machine learning algorithms with serverless architectures inherently induces overheads, as serverless functions are not directly network-addressable, hence one must rely on a remote storage service for storing the shared state. To hide the access latency to the remote storage, one can employ intra-function parallelism to take advantage of the multicore computing resources of the serverless functions. In this work, we port to serverless two stateful machine learning algorithms, k-means clustering and logistic regression, and then adopt intra-function parallelism to parallelize the execution of the serverless functions. Several experiments have demonstrated that intra-function parallelism delivers performance improvements in serverless machine learning. Improved performances of up to 68% have been achieved when running k-means on serverless functions that employ intra-function parallelism. We demonstrate with k-means and logistic regression that from a performance perspective it is preferable to execute a smaller number of multiple-vCPUs workers than a larger number of single-vCPU workers, due to decreased synchronization overheads.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Keywords: | Intra-Function Parallelism,Lithops,Machine Learning,Multicore Functions,Serverless,Stateful |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 05 Feb 2024 12:50 |
Last Modified: | 17 Nov 2024 00:32 |
Published Version: | https://doi.org/10.1145/3631295.3631399 |
Status: | Published |
Publisher: | ACM |
Series Name: | WoSC '23 |
Identification Number: | 10.1145/3631295.3631399 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208734 |
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Description: Leveraging Intra-Function Parallelism in Serverless Machine Learning