KubeCon + CloudNative North America 2022

“Cloud native?” Check! Apply the same principles at the Edge? Hmmm! How do I operate Apps across 1000s of locations, which are often hidden behind layers of NAT? How do I run AI apps on nodes that are too small to fit the AI model? How to make it operationally simple? Lets discuss and demo! We’re all familiar with “cloud native” -but once we start to operate applications at the edge, we have to adopt a new set of principles and evolve our cloud-native paradigms. We deploy Apps at the edge to achieve lower latency or higher performance, to comply with data sovereignty regulations, to reduce transit cost or to perform near real-time decision making on local data sources. Developing and operating Edge apps requires us to answer questions like: How do I operate Apps across 1000s of locations, which are often hidden behind layers of NAT and have spotty cloud connectivity? How do I run computation heavy tasks, like AI apps, on a set of nodes where each node does not have sufficient CPU and memory to run the entire model? How do I deal with a heterogeneous environment, with x86 and ARM-based devices? Which additional tools do I need to assure compliance to data-privacy rules, run AI models that just don’t fit a single compute element, or perform federated learning in an efficient way?