End-to-End Orchestration of NextG Media Services over the Distributed Compute Continuum

NextG (5G and beyond) networks, through the increasing integration of cloud/edge computing technologies, are becoming highly distributed compute platforms ideally suited to host emerging resource-intensive and latency-sensitive applications (e.g., industrial automation, extended reality, distributed AI). The end-to-end orchestration of such demanding applications, which involves function/data placement, flow routing, and joint communication/computation/storage resource allocation, requires new models and algorithms able to capture: (i) their disaggregated microservice-based architecture, (ii) their complex processing graph structures, including multiple-input multiple-output processing stages, and (iii) the opportunities to efficiently share and replicate real-time data streams that may be useful for multiple functions and/or end users. To this end, we first identify the technical gaps in existing literature that prevent efficiently addressing the optimal orchestration of emerging applications described by information-aware directed acyclic graphs (DAGs). We then leverage the recently proposed Cloud Network Flow optimization framework and a novel functionally-equivalent DAG-to-Forest graph transformation procedure to design IDAGO (Information-Aware DAG Orchestration), a polynomial-time multi-criteria approximation algorithm for the optimal orchestration of NextG media services over NextG compute-integrated networks. Results show that IDAGO's multiplicative cost reductions over leading baselines scale linearly with aggregate service load, reaching up to 3X gains in scenarios based on AWS and Unreal Engine data under moderate service loads.