Recent advances in programmable networking hardware create a new computing paradigm called in-network computing. This new paradigm allows functionality that has been served by commodity servers, ranging from network middleboxes to components of distributed systems, to be performed in the network. I argue that to fully unleash its potential, we need resource elasticity and fault resiliency via higher-level abstractions.

In this talk, I demonstrate that in-network computing can be elastic and resilient by designing high-level abstractions and runtime systems that enable us to leverage compute and memory resources available outside of a single type of device — e.g., programmable switches — while hiding the complexities of dealing with device heterogeneity. I begin by introducing TEA, a framework that provides elastic memory by enabling memory-intensive in-switch applications, such as cloud-scale load balancers, to leverage DRAM on remote servers via virtual table abstraction. Then I present ExoPlane and RedPlane, frameworks that support evolving in-network computing workloads and requirements — i.e., serving multiple concurrent applications and making them fault-tolerant — via infinite switch resource and one big fault-tolerant switch abstractions. Several systems in the industry are now adopting some of the technologies presented in this talk.