  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.
