Intelligent IoT is a prerequisite for societal priorities such as a smart power grid, smart urban infrastructures and smart highways. These applications bring requirements such as real-time guarantees, data and action consistency, fault-tolerance, high availability, temporal data indexing, scalability, and even self-organization and self-stabilization. Existing platforms are oriented towards asynchronous, out of band upload of data to the cloud: Important functionality, but not enough to address the need. Cornell's Cascade project seeks to close the gap by creating a new platform for hosting ML and AI, optimized to achieve sharply lower delay and substantially higher bandwidth than in any existing platform. At the same time, Cascade introduces much stronger guarantees - a mix that we believe will be particularly appealing in applications where events should trigger a quick and trustworthy response. This short paper is intended as a brief overview of the effort, with details to be published elsewhere.
Cascade: An Edge Computing Platform for Real-time Machine Intelligence / Song W.; Yang Y.; Liu T.; Merlina A.; Garrett T.; Vitenberg R.; Rosa L.; Awatramani A.; Wang Z.; Birman K.. - ELETTRONICO. - (2022), pp. 2-6. (Intervento presentato al convegno 2022 Workshop on Advanced tools, programming languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems tenutosi a Salerno nel July 2022) [10.1145/3524053.3542741].
Cascade: An Edge Computing Platform for Real-time Machine Intelligence
Rosa L.Membro del Collaboration Group
;
2022
Abstract
Intelligent IoT is a prerequisite for societal priorities such as a smart power grid, smart urban infrastructures and smart highways. These applications bring requirements such as real-time guarantees, data and action consistency, fault-tolerance, high availability, temporal data indexing, scalability, and even self-organization and self-stabilization. Existing platforms are oriented towards asynchronous, out of band upload of data to the cloud: Important functionality, but not enough to address the need. Cornell's Cascade project seeks to close the gap by creating a new platform for hosting ML and AI, optimized to achieve sharply lower delay and substantially higher bandwidth than in any existing platform. At the same time, Cascade introduces much stronger guarantees - a mix that we believe will be particularly appealing in applications where events should trigger a quick and trustworthy response. This short paper is intended as a brief overview of the effort, with details to be published elsewhere.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
