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Resource Orchestration for Multi-Domain, Exascale, Geo-Distributed Data Analytics

Document Type Expired Internet-Draft (individual)
Expired & archived
Authors Qiao Xiang , Jensen Zhang , Franck Le , Y. Richard Yang , Harvey Newman
Last updated 2022-01-13 (Latest revision 2021-07-12)
RFC stream (None)
Intended RFC status (None)
Stream Stream state (No stream defined)
Consensus boilerplate Unknown
RFC Editor Note (None)
IESG IESG state Expired
Telechat date (None)
Responsible AD (None)
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This Internet-Draft is no longer active. A copy of the expired Internet-Draft is available in these formats:


As the data volume increases exponentially over time, data analytics is transiting from a single-domain network to a multi-domain, geo- distributed network, where different member networks contribute various resources, e.g., computation, storage and networking resources, to collaboratively collect, share and analyze extremely large amounts of data. Such a network calls for a resource orchestration framework that emphasizes the performance predictability of data analytics jobs, the high utilization of resources, and the autonomy and privacy of member networks. This document presents the design of Unicorn, a unified resource orchestration framework for multi-domain, geo-distributed data analytics, which uses the Application-Layer Traffic Optimization (ALTO) protocol as the key component for (1) allows member networks to provide accurate information on different types of resources; (2) keeps the private information of member networks; and (3) allows data analytics jobs to accurately describe their requirements of different types of resources. As a part of Unicorn, an ALTO extension for privacy-preserving interdomain information aggregation is also presented.


Qiao Xiang
Jensen Zhang
Franck Le
Y. Richard Yang
Harvey Newman

(Note: The e-mail addresses provided for the authors of this Internet-Draft may no longer be valid.)