Poster: INSANE – A Uniform Middleware API for Differentiated Quality using Heterogeneous Acceleration Techniques at the Network Edge

Abstract

Next-generation AI applications benefit from executing close to the network edge to better exploit co-locality to datasources and controlled actuators, and to meet stringent latency requirements. In the edge-enabled cloud continuum, time and safety-critical traffic coexists with best-effort flows, resulting in heterogeneous requirements that current networking middleware and frameworks struggle to support. This paper proposes INSANE, INtegrated Selective Acceleration at the Network Edge, the first edge-oriented middleware that integrates different network acceleration techniques (XDP, DPDK, RDMA, and TSN) within the same data distribution service. INSANE offers a uniform and simple interface, useful to support common data distribution patterns, that allow developers to exploit at runtime the most suitable network technology available in the dynamically determined deployment environment.