Tongji/Yale University IBM Tongji/Yale University California Institute of Technology Tongji University 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.