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Exploiting serverless runtimes for large-scale optimization
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- Aytekin, Arda, 1986- (author)
- KTH,Reglerteknik
-
- Johansson, Mikael (author)
- KTH,Reglerteknik
- (creator_code:org_t)
- IEEE Computer Society, 2019
- 2019
- English.
- In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). - : IEEE Computer Society. ; , s. 499-501
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- Alternating direction method of multipliers
- Distributed optimization
- Optimization
- Serverless
Publication and Content Type
- ref (subject category)
- kon (subject category)
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