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

TL;DR

This paper introduces IDAGO, a novel polynomial-time algorithm for orchestrating complex, resource-intensive NextG media services over distributed compute networks, addressing key gaps in existing models.

Contribution

The paper develops IDAGO, a new algorithm that efficiently orchestrates NextG media services by modeling their complex structures and resource sharing opportunities.

Findings

IDAGO effectively optimizes resource allocation for NextG media services.

The approach captures complex DAG structures and enables efficient data stream sharing.

IDAGO operates in polynomial time, making it practical for real-world deployment.

Abstract

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.