The emergence and growth of 5G and beyond 5G (B5G) networks has brought about the rise of so-called ''programmable'' networks, i.e., networks whose operational requirements are so stringent that they can only be met in an automated manner, with minimal/no human involvement. Any requirements on such a network would need to be formally specified via intents, which can represent user requirements in a formal yet understandable manner. Meeting the user requirements via intents would necessitate the rapid implementation of resource allocation and scheduling in the network. Also, given the expected size and geographical distribution of programmable networks, multiple resource scheduling implementations would need to be implemented at the same time. This would necessitate the use of a meta-scheduler that can coordinate the various schedulers and dynamically ensure optimal resource scheduling across the network. To that end, in this position paper, we propose a research agenda for modeling, implementation, and inclusion of intent-based dynamic meta-scheduling in programmable networks. Our research agenda will be built on active inference, a type of causal inference. Active inference provides some level of autonomy to each scheduler while the meta-scheduler takes care of overall intent fulfillment. Our research agenda will comprise a strawman architecture for meta-scheduling and a set of research questions that need to be addressed to make intent-based dynamic meta-scheduling a reality.

翻译：5G及超5G（B5G）网络的出现与发展催生了所谓"可编程"网络的兴起。这类网络的运行需求极为严苛，必须通过自动化方式以最少（或无需）人工干预才能满足。对此类网络的任何需求都需通过意图进行形式化规约，从而以形式化且可理解的方式表征用户需求。通过意图满足用户需求将要求网络快速实现资源分配与调度机制。此外，考虑到可编程网络预期的规模与地理分布特性，需要同时部署多种资源调度实施方案。这必然要求采用能够协调各类调度器、动态确保全网资源调度最优化的元调度器。为此，本立场论文提出一项针对可编程网络中基于意图的动态元调度建模、实现与集成的研究议程。我们的研究议程将建立在主动推理（一种因果推理方法）的理论基础之上。主动推理为各调度器赋予一定自主性，而元调度器则负责整体意图的达成。该研究议程将包含元调度的基础架构原型，以及为实现基于意图的动态元调度所需解决的一系列研究问题。