GeoFF: Federated Serverless Workflows with Data Pre-Fetching
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915570262212608 |
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| author | Carl, Natalie Schirmer, Trever Pfandzelter, Tobias Bermbach, David |
| author_facet | Carl, Natalie Schirmer, Trever Pfandzelter, Tobias Bermbach, David |
| contents | Function-as-a-Service (FaaS) is a popular cloud computing model in which applications are implemented as work flows of multiple independent functions. While cloud providers usually offer composition services for such workflows, they do not support cross-platform workflows forcing developers to hardcode the composition logic. Furthermore, FaaS workflows tend to be slow due to cascading cold starts, inter-function latency, and data download latency on the critical path. In this paper, we propose GeoFF, a serverless choreography middleware that executes FaaS workflows across different public and private FaaS platforms, including ad-hoc workflow recomposition. Furthermore, GeoFF supports function pre-warming and data pre-fetching. This minimizes end-to-end workflow latency by taking cold starts and data download latency off the critical path. In experiments with our proof-of-concept prototype and a realistic application, we were able to reduce end-to-end latency by more than 50%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13594 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GeoFF: Federated Serverless Workflows with Data Pre-Fetching Carl, Natalie Schirmer, Trever Pfandzelter, Tobias Bermbach, David Distributed, Parallel, and Cluster Computing Function-as-a-Service (FaaS) is a popular cloud computing model in which applications are implemented as work flows of multiple independent functions. While cloud providers usually offer composition services for such workflows, they do not support cross-platform workflows forcing developers to hardcode the composition logic. Furthermore, FaaS workflows tend to be slow due to cascading cold starts, inter-function latency, and data download latency on the critical path. In this paper, we propose GeoFF, a serverless choreography middleware that executes FaaS workflows across different public and private FaaS platforms, including ad-hoc workflow recomposition. Furthermore, GeoFF supports function pre-warming and data pre-fetching. This minimizes end-to-end workflow latency by taking cold starts and data download latency off the critical path. In experiments with our proof-of-concept prototype and a realistic application, we were able to reduce end-to-end latency by more than 50%. |
| title | GeoFF: Federated Serverless Workflows with Data Pre-Fetching |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2405.13594 |