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Title: BumbleBee: Application-aware adaptation for edge-cloud orchestration
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%.  more » « less
Award ID(s):
1717064
PAR ID:
10506915
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEEACM Symposium on Edge Computing
ISSN:
2837-4827
ISBN:
978-1-6654-8611-8
Page Range / eLocation ID:
122 to 135
Format(s):
Medium: X
Location:
Seattle, WA, USA
Sponsoring Org:
National Science Foundation
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