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Title: Cypress: input size-sensitive container provisioning and request scheduling for serverless platforms
The growing popularity of the serverless platform has seen an increase in the number and variety of applications (apps) being deployed on it. The majority of these apps process user-provided input to produce the desired results. Existing work in the area of input-sensitive profiling has empirically shown that many such apps have input size-dependent execution times which can be determined through modelling techniques. Nevertheless, existing serverless resource management frameworks are agnostic to the input size-sensitive nature of these apps. We demonstrate in this paper that this can potentially lead to container over-provisioning and/or end-to-end Service Level Objective (SLO) violations. To address this, we propose Cypress, an input size-sensitive resource management framework, that minimizes the containers provisioned for apps, while ensuring a high degree of SLO compliance. We perform an extensive evaluation of Cypress on top of a Kubernetes-managed cluster using 5 apps from the AWS Serverless Application Repository and/or Open-FaaS Function Store with real-world traces and varied input size distributions. Our experimental results show that Cypress spawns up to 66% fewer containers, thereby, improving container utilization and saving cluster-wide energy by up to 2.95X and 23%, respectively, versus state-of-the-art frameworks, while remaining highly SLO-compliant (up to 99.99%).  more » « less
Award ID(s):
2116962
NSF-PAR ID:
10462094
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
SoCC '22: Proceedings of the 13th Symposium on Cloud Computing
Page Range / eLocation ID:
257 to 272
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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