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“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?
Frank is Distinguished Engineer in Cisco’s Emerging Technologies and Incubation group, driving software and architecture development for Edge platforms, solutions, associated services and applications. He is involved in several open source projects and is a Linux Foundation Networking... Read More →
Kris Flautner is responsible for product strategy in Cisco’s Emerging Technology & Incubation Group after having been CEO of Banzai Cloud, a Cisco acquisition, that turned Cloud Native dreams into enterprise reality through cloud software. Before that, he was general manager of... Read More →