The Night Shift: Understanding Performance Variability of Cloud Serverless Platforms
arXiv:2304.07177 · doi:10.1145/3592533.3592808
Abstract
Function-as-a-Service is a popular cloud programming model that supports developers by abstracting away most operational concerns with automatic deployment and scaling of applications. Due to the high level of abstraction, developers rely on the cloud platform to offer a consistent service level, as decreased performance leads to higher latency and higher cost given the pay-per-use model. In this paper, we measure performance variability of Google Cloud Functions over multiple months. Our results show that diurnal patterns can lead to performance differences of up to 15%, and that the frequency of unexpected cold starts increases threefold during the start of the week. This behavior can negatively impact researchers that conduct performance studies on cloud platforms and practitioners that run cloud applications.
arXiv commentAuthor's copy of paper accepted at the 1st Workshop on SErverless Systems, Applications and MEthodologies (SESAME '23)
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Cited by in corpus (7)
- Fusionize++: Improving Serverless Application Performance Using Dynamic Task Inlining and Infrastructure Optimization
- ProFaaStinate: Delaying Serverless Function Calls to Optimize Platform Performance
- Hiku: Pull-Based Scheduling for Serverless Computing
- Efficiently Detecting Performance Changes in FaaS Application Releases
- Sampling in Cloud Benchmarking: A Critical Review and Methodological Guidelines
- New Kids: An Architecture and Performance Investigation of Second-Generation Serverless Platforms
- Optimizing Cloud Deployment: Blending of IaaS and FaaS for Microservice Architecture