Minos: Exploiting Cloud Performance Variation with Function-as-a-Service Instance Selection
Trever Schirmer, Natalie Carl, Nils H\"oller, Tobias Pfandzelter, David Bermbach

TL;DR
Minos exploits performance variability in serverless FaaS environments by selectively terminating slow instances, leading to faster execution times and cost savings for data workflows.
Contribution
This paper introduces Minos, a novel system that leverages cloud performance variation by self-terminating slow instances to improve speed and reduce costs.
Findings
Up to 13% speedup in data processing workflows
Up to 4% overall performance improvement
Cost savings due to more efficient instance utilization
Abstract
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…
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Taxonomy
TopicsCloud Computing and Resource Management · Software System Performance and Reliability · Distributed systems and fault tolerance
