2025/05/19 by Trever Schirmer, Natalie Carl, Schirmer, Trever +7
Computer Science · #Cloud Computing and Resource Management #Distributed #Distributed systems and fault tolerance #FOS: Computer and information sciences #Parallel #Software System Performance and Reliability #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2505.12928
openalex publication_date 2025/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.