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

arXiv: https://arxiv.org/abs/2407.08710 · HTML: https://mlsanity.com/paper/2407.08710/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services](https://mlsanity.com/paper/2510.11535/) (2025)

## Abstract (arXiv)

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 for efficiently sharing and replicating 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.