FunctionBench: A Suite of Workloads for Serverless Cloud Function Service

Serverless computing is attracting considerable attention recently, but many published papers use micro-benchmarks for evaluation that might result in impracticality. To address this, we present FunctionBench, a suite of practical function workloads for public services. It contains realistic data-oriented applications that utilize various resources during execution. The source codes customized for various cloud service providers are publicly available. We are positive that it suggests opportunities for new function applications with lessen experiment setup overheads.

[1]  Mengyuan Li,et al.  Peeking Behind the Curtains of Serverless Platforms , 2018, USENIX Annual Technical Conference.

[2]  Kyungyong Lee,et al.  Distributed Matrix Multiplication Performance Estimator for Machine Learning Jobs in Cloud Computing , 2018, 2018 IEEE 11th International Conference on Cloud Computing (CLOUD).

[3]  Geoffrey C. Fox,et al.  Evaluation of Production Serverless Computing Environments , 2018, 2018 IEEE 11th International Conference on Cloud Computing (CLOUD).

[4]  Andrea C. Arpaci-Dusseau,et al.  SOCK: Rapid Task Provisioning with Serverless-Optimized Containers , 2018, USENIX Annual Technical Conference.

[5]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[6]  Joseph M. Hellerstein,et al.  Serverless Computing: One Step Forward, Two Steps Back , 2018, CIDR.

[7]  Jimmy J. Lin,et al.  Serverless Data Analytics with Flint , 2018, 2018 IEEE 11th International Conference on Cloud Computing (CLOUD).

[8]  Dilma Da Silva,et al.  Exploring Serverless Computing for Neural Network Training , 2018, 2018 IEEE 11th International Conference on Cloud Computing (CLOUD).