Leveraging Intra-Function Parallelism in Serverless Machine Learning

TY - GEN

T1 - Leveraging Intra-Function Parallelism in Serverless Machine Learning

AU - Predoaia, Ionut

AU - García-López, Pedro

N1 - This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

PY - 2023/12/11

Y1 - 2023/12/11

N2 - 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.

AB - 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.

KW - Intra-Function Parallelism

KW - Lithops

KW - Machine Learning

KW - Multicore Functions

KW - Serverless

KW - Stateful

UR - http://www.scopus.com/inward/record.url?scp=85180125744&partnerID=8YFLogxK

U2 - 10.1145/3631295.3631399

DO - 10.1145/3631295.3631399

M3 - Conference contribution

SP - 36

EP - 41

BT - WoSC '23: Proceedings of the 9th International Workshop on Serverless Computing

PB - ACM

T2 - 9th International Workshop on Serverless Computing, WoSC '23

Y2 - 11 December 2023 through 15 December 2023

ER -