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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.12928 |
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| _version_ | 1866915572975927296 |
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| author | Schirmer, Trever Carl, Natalie Höller, Nils Pfandzelter, Tobias Bermbach, David |
| author_facet | Schirmer, Trever Carl, Natalie Höller, Nils Pfandzelter, Tobias Bermbach, David |
| contents | Serverless Function-as-a-Service (FaaS) is a popular cloud paradigm to quickly and cheaply implement complex applications. Because the function instances cloud providers start to execute user code run on shared infrastructure, their performance can vary. From a user perspective, slower instances not only take longer to complete, but also increase cost due to the pay-per-use model of FaaS services where execution duration is billed with microsecond accuracy. In this paper, we present Minos, a system to take advantage of this performance variation by intentionally terminating instances that are slow. Fast instances are not terminated, so that they can be re-used for subsequent invocations. One use case for this are data processing and machine learning workflows, which often download files as a first step, during which Minos can run a short benchmark. Only if the benchmark passes, the main part of the function is actually executed. Otherwise, the request is re-queued and the instance crashes itself, so that the platform has to assign the request to another (potentially faster) instance. In our experiments, this leads to a speedup of up to 13% in the resource intensive part of a data processing workflow, resulting in up to 4% faster overall performance (and consequently 4% cheaper prices). Longer and complex workflows lead to increased savings, as the pool of fast instances is re-used more often. For platforms exhibiting this behavior, users get better performance and save money by wasting more of the platforms resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Minos: Exploiting Cloud Performance Variation with Function-as-a-Service Instance Selection Schirmer, Trever Carl, Natalie Höller, Nils Pfandzelter, Tobias Bermbach, David Distributed, Parallel, and Cluster Computing Serverless Function-as-a-Service (FaaS) is a popular cloud paradigm to quickly and cheaply implement complex applications. Because the function instances cloud providers start to execute user code run on shared infrastructure, their performance can vary. From a user perspective, slower instances not only take longer to complete, but also increase cost due to the pay-per-use model of FaaS services where execution duration is billed with microsecond accuracy. In this paper, we present Minos, a system to take advantage of this performance variation by intentionally terminating instances that are slow. Fast instances are not terminated, so that they can be re-used for subsequent invocations. One use case for this are data processing and machine learning workflows, which often download files as a first step, during which Minos can run a short benchmark. Only if the benchmark passes, the main part of the function is actually executed. Otherwise, the request is re-queued and the instance crashes itself, so that the platform has to assign the request to another (potentially faster) instance. In our experiments, this leads to a speedup of up to 13% in the resource intensive part of a data processing workflow, resulting in up to 4% faster overall performance (and consequently 4% cheaper prices). Longer and complex workflows lead to increased savings, as the pool of fast instances is re-used more often. For platforms exhibiting this behavior, users get better performance and save money by wasting more of the platforms resources. |
| title | Minos: Exploiting Cloud Performance Variation with Function-as-a-Service Instance Selection |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.12928 |