Task offloading is a potential solution to satisfy the strict requirements of
computation-intensive and latency-sensitive vehicular applications due to the
limited onboard computing resources. However, the overwhelming upload traffic
may lead to unacceptable uploading time. To tackle this issue, for tasks taking
environmental data as input, the data perceived by roadside units (RSU)
equipped with several sensors can be directly exploited for computation,
resulting in a novel task offloading paradigm with integrated communications,
sensing and computing (I-CSC). With this paradigm, vehicles can select to
upload their sensed data to RSUs or transmit computing instructions to RSUs
during the offloading. By optimizing the computation mode and network
resources, in this paper, we investigate an I-CSC-based task offloading problem
to reduce the cost caused by resource consumption while guaranteeing the
latency of each task. Although this non-convex problem can be handled by the
alternating minimization (AM) algorithm that alternatively minimizes the
divided four sub-problems, it leads to high computational complexity and local
optimal solution. To tackle this challenge, we propose a creative structural
knowledge-driven meta-learning (SKDML) method, involving both the model-based
AM algorithm and neural networks. Specifically, borrowing the iterative
structure of the AM algorithm, also referred to as structural knowledge, the
proposed SKDML adopts long short-term memory (LSTM) network-based meta-learning
to learn an adaptive optimizer for updating variables in each sub-problem,
instead of the handcrafted counterpart in the AM algorithm.

通过使用集成通信、感知和计算的模式，该研究论文探讨了一种以任务卸载为基础的问题，旨在通过优化计算模式和网络资源，降低资源消耗的成本，并确保每个任务的延迟。为了解决该挑战，提出了一种创造性的基于结构知识的元学习方法，采用了 LSTM 神经网络以替代手工设计的卸载算法。

基于结构知识驱动的元学习在集成通信、传感和计算的车联网任务卸载中的应用

Structural Knowledge-Driven Meta-Learning for Task Offloading in  Vehicular Networks with Integrated Communications, Sensing and Computing

As computing power is becoming the core productivity of the digital economy
era, the concept of Computing and Network Convergence (CNC), under which
network and computing resources can be dynamically scheduled and allocated
according to users' needs, has been proposed and attracted wide attention.
Based on the tasks' properties, the network orchestration plane needs to
flexibly deploy tasks to appropriate computing nodes and arrange paths to the
computing nodes. This is a orchestration problem that involves resource
scheduling and path arrangement. Since CNC is relatively new, in this paper, we
review some researches and applications on CNC. Then, we design a CNC
orchestration method using reinforcement learning (RL), which is the first
attempt, that can flexibly allocate and schedule computing resources and
network resources. Which aims at high profit and low latency. Meanwhile, we use
multi-factors to determine the optimization objective so that the orchestration
strategy is optimized in terms of total performance from different aspects,
such as cost, profit, latency and system overload in our experiment. The
experiments shows that the proposed RL-based method can achieve higher profit
and lower latency than the greedy method, random selection and
balanced-resource method. We demonstrate RL is suitable for CNC orchestration.
This paper enlightens the RL application on CNC orchestration.

本文介绍了计算与网络融合的概念，提出了基于强化学习的计算与网络编排方法，通过实验显示该方法可以比贪婪算法、随机选择和均衡资源算法实现更高的利润和更低的延迟，并证明了强化学习在计算与网络编排上的适用性。

计算与网络融合编排中的强化学习

Reinforcement Learning in Computing and Network Convergence Orchestration

Network appliances perform different functions on network flows and
constitute an important part of an operator's network. Normally, a set of
chained network functions process network flows. Following the trend of
virtualization of networks, virtualization of the network functions has also
become a topic of interest. We define a model for formalizing the chaining of
network functions using a context-free language. We process deployment requests
and construct virtual network function graphs that can be mapped to the
network. We describe the mapping as a Mixed Integer Quadratically Constrained
Program (MIQCP) for finding the placement of the network functions and chaining
them together considering the limited network resources and requirements of the
functions. We have performed a Pareto set analysis to investigate the possible
trade-offs between different optimization objectives.

本文定义了一个上下文自由语言模型，用于网络功能固定，同时使用 MIQCP 实现网络功能在有限资源下的部署、映射和优化。