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.

翻译：下一代（5G及更高版本）网络通过日益深入的云/边缘计算技术融合，正演化为高度分布式的计算平台，其理想适用于承载新兴的资源密集型与低延迟敏感型应用（如工业自动化、扩展现实、分布式人工智能）。此类高要求应用的端到端编排涉及功能/数据放置、流路由以及通信/计算/存储资源的联合分配，这需要能够刻画以下特征的新型模型与算法：（i）其基于微服务的解耦式架构；（ii）其复杂的处理图结构，包括多输入多输出处理阶段；以及（iii）为可能对多个功能和/或终端用户有用的数据流实现高效共享与复制的机会。为此，我们首先指出现有文献中存在的技术空白，这些空白阻碍了高效解决以信息感知有向无环图（DAG）描述的新兴应用的最优编排问题。随后，我们利用近期提出的云网络流优化框架及一种新颖的功能等价DAG到森林图转换过程，设计了IDAGO（信息感知DAG编排算法）——一种用于在下一代计算集成网络上最优编排下一代媒体服务的多项式时间多准则近似算法。