EdgeBench: Benchmarking Edge Computing Platforms

The emerging trend of edge computing has led several cloud providers to release their own platforms for performing computation at the 'edge' of the network. We compare two such platforms, Amazon AWS Greengrass and Microsoft Azure IoT Edge, using a new benchmark comprising a suite of performance metrics. We also compare the performance of the edge frameworks to cloud-only implementations available in their respective cloud ecosystems. Amazon AWS Greengrass and Azure IoT Edge use different underlying technologies, edge Lambda functions vs. containers, and so we also elaborate on platform features available to developers. Our study shows that both of these edge platforms provide comparable performance, which nevertheless differs in important ways for key types of workloads used in edge applications. Finally, we discuss several current issues and challenges we faced in deploying these platforms.

[1]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[2]  Paul R. Brenner,et al.  Serverless Computing: Design, Implementation, and Performance , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW).

[3]  Maciej Malawski,et al.  Benchmarking Heterogeneous Cloud Functions , 2017, Euro-Par Workshops.

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

[5]  Zheng Zhang,et al.  MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems , 2015, ArXiv.

[6]  Vasilios Andrikopoulos,et al.  Using a Microbenchmark to Compare Function as a Service Solutions , 2018, ESOCC.

[7]  Mahadev Satyanarayanan,et al.  The Emergence of Edge Computing , 2017, Computer.

[8]  Anthony S. Deese.  Implementation of Unsupervised k-Means Clustering Algorithm Within Amazon Web Services Lambda , 2018, 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[9]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.