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Exploiting serverless runtimes for large-scale optimization
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0003-1725-2901
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
2019 (English)In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), IEEE Computer Society, 2019, p. 499-501, article id 8814497Conference paper, Published paper (Refereed)
Abstract [en]

Serverless runtimes provide efficient and cost-effective environments for scalable computations, thanks to their event-driven and elastic nature. So far, they have mostly been used for stateless, data parallel and sporadic computations. In this work, we propose exploiting serverless runtimes to solve generic, large-scale optimization problems. To this end, we implement a parallel optimization algorithm for solving a regularized logistic regression problem, and use AWS Lambda for the compute-intensive work. We show that relative speedups up to 256 workers and efficiencies above 70% up to 64 workers can be expected.

Place, publisher, year, edition, pages
IEEE Computer Society, 2019. p. 499-501, article id 8814497
Series
IEEE International Conference on Cloud Computing, CLOUD, ISSN 2159-6182
Keywords [en]
Alternating direction method of multipliers, Distributed optimization, Optimization, Serverless
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-262586DOI: 10.1109/CLOUD.2019.00090Scopus ID: 2-s2.0-85072323908ISBN: 9781728127057 (print)OAI: oai:DiVA.org:kth-262586DiVA, id: diva2:1362998
Conference
12th IEEE International Conference on Cloud Computing, CLOUD 2019; Milan; Italy; 8 July 2019 through 13 July 2019
Note

QC 20191022

Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-22Bibliographically approved

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Aytekin, ArdaJohansson, Mikael

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Output format
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