@inproceedings{lee2022bumblebee,
author = {Lee, HyunJong and Noghabi, Shadi and Noble, Brian and Furlong, Matthew and Cox, Landon},
title = {BumbleBee: Application-aware adaptation for edge-cloud orchestration},
organization = {ACM/IEEE},
booktitle = {Symposium on Edge Computing},
year = {2022},
month = {December},
abstract = {Modern developers rely on container-orchestration frameworks like Kubernetes to deploy and manage hybrid workloads that span the edge and cloud. When network conditions between the edge and cloud change unexpectedly, a workload must adapt its internal behavior. Unfortunately, container-orchestration frameworks do not offer an easy way to express, deploy, and manage adaptation strategies. As a result, fine-tuning or modifying a workload's adaptive behavior can require modifying containers built from large, complex codebases that may be maintained by separate development teams. This paper presents BumbleBee, a lightweight extension for container-orchestration frameworks that separates the concerns of application logic and adaptation logic. BumbleBee provides a simple in-network programming abstraction for making decisions about network data using application semantics. Experiments with a BumbleBee prototype show that edge ML-workloads can adapt to network variability and survive disconnections, edge stream-processing workloads can improve benchmark results between 37.8% and 23x, and HLS video-streaming can reduce stalled playback by 77%.},
url = {http://approjects.co.za/?big=en-us/research/publication/bumblebee-application-aware-adaptation-for-edge-cloud-orchestration/},
}