{"id":833986,"date":"2022-04-10T14:30:48","date_gmt":"2022-04-10T21:30:48","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=833986"},"modified":"2022-04-10T14:30:48","modified_gmt":"2022-04-10T21:30:48","slug":"bumblebee-application-aware-adaptation-for-edge-cloud-orchestration","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/bumblebee-application-aware-adaptation-for-edge-cloud-orchestration\/","title":{"rendered":"BumbleBee: Application-aware adaptation for edge-cloud orchestration"},"content":{"rendered":"
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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%.<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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 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