Computing-aware networks (CAN) can provide ubiquitous AI inference services for intelligent applications. However, due to the huge differences in the demand for inference services of intelligent applications and the continuous innovation of computing devices, traditional protocol-based scheduling methods make it difficult to schedule complex heterogeneous computing resources. In this paper, we propose a CAN resource scheduling method, named SMAD, which can adapt to external environmental changes without human modification of the protocol mechanism. Aiming at the scheduling problem of complex heterogeneous computing resources and concurrent random diverse inference tasks, a constrained multi-objective optimization problem of scheduling service quantity, accuracy, and delay is formulated. Through the general Markov Decision Process (MDP) transformation from the model, the Deep Reinforcement Learning (DRL)-based AI-native scheduling algorithm framework can further solve the optimization problem. Meanwhile, the AI-native framework aggregates diverse device states into a data tensor, integrates the DRL algorithm in a data-driven manner to generate a dynamic action tensor, and reversely drives full-stack resource scheduling for global closed-loop optimization in CAN, aligning with inference service demands. Extensive simulation results show that the proposed SMAD has good convergence performance. Compared with the traditional DRL algorithm, it significantly increases the number of concurrent schedulable tasks and reduces the inference service delay.

Industrial Internet of Things (IIoT) applications, such as industrial process control, demand ultra-high reliability and bounded delay. The Reliable and Available Wireless (RAW) initiative within the IETF DetNet working group addresses these needs by applying IEEE 802.15.4 time-slotted channel hopping (TSCH) technology and leveraging techniques like Packet Replication, Elimination, and Ordering Functions (PREOF) to ensure deterministic performance for IIoT. However, while PREOF improves reliability, its redundant transmission mechanism inevitably increases energy consumption, conflicting with the energy constraints of TSCH nodes. The existing multipath routing approaches struggle to address this challenge, failing to jointly consider both energy efficiency and deterministic performance. Additionally, these approaches often overlook the delay variation caused by multipath transmissions of different lengths—a key factor that can undermine deterministic performance by increasing buffering requirements and affecting the predictability of data flows. In this paper, we investigate a multipath optimization problem aiming at improving energy efficiency and minimizing delay variation while meeting the requirements of bounded reliability and delay for deterministic flows. Considering the above multipath routing optimization problem, which aims to satisfy multiple objectives under multiple constraints, is typically NP-hard, solving these challenges with traditional methods is highly complex. Thus, we further propose a Energy-Efficient Multi-path Routing (EEMR) algorithm that utilizes deep reinforcement learning (DRL) to optimize the multipath selection, effectively enhancing energy efficiency for deterministism. EEMR can be extended to solve optimization problems in holistic-deterministic multi-domain scenarios, such as smart factories integrating 5G and DetNet. We compare the performance of our proposed method with several baseline methods. Empirical evaluations show that EEMR significantly reduces energy comsumption and delay variation compared to baseline methods under various environment settings.

The emerging intelligent services, spurred by the rise of the intelligent Internet, are placing multidimensional requirements on the network to collaboratively guarantee computing and networking resources. In this article, we propose a unified end-to-end intelligent resource scheduling method for converging networks [e.g., Internet of Things (IoT)], which can always globally abstract the available resources from different networks with a unified model description, and jointly planning the resources from end-to-end by deep reinforcement learning (DRL) algorithms supporting both discrete and continuous variable decisions. The method proposes a three-layer architecture, including service layer, network layer, and adaption layer, which aims at optimizing the flow transmission performance. Through the general Markov decision process (MDP) transformation from the model, the DRL-assisted algorithm can further solve the optimization problem. We categorize heterogeneous network resource scheduling into horizontal and vertical scenarios, applying the proposed architecture to both. Compared with the existing diverse learning (DiLearn) and naive (DiNaive) approaches, the proposed approach is not only time-saving but also can schedule 28.4% and$8\times $more flows in horizontal scheduling scenarios, and improve 54.2% and$3.5\times $flows in vertical scheduling scenarios, respectively.

Traditional route forwarding generates obvious performance problems and it cannot fulfill the increasing diversity in the number of user accesses and Quality of Service (QoS). It is necessary to investigating an intelligent and reliable routing for online audio/video mixed traffic with high-real time to meet different QoS requirements. In this article, we design a routing scheme based on deep reinforcement learning (DRL) and graph neural networks (GNNs), which could be easily implemented as an application on a controller, named RtDG. Specifically, a network topology is first extracted using GNN to generate high-dimensional feature representations. To obtain QoS utility values comprehensively, we set four parameters, namely, bandwidth, delay, packet loss rate, and delay jitter, and construct weighted formulas using the parameters determined by Bayesian optimization. We use proximal policy optimization (PPO) to make output decisions while adding a KL scatter penalty to the loss function. And then, the controller assigns it to switches via traffic table based on the calculated QoS values. Furthermore, we deploy an overlay network using Mininet and ONOS, enabling optimal pathfinding without changing the existing network architecture. Meanwhile, tests are conducted under background traffic of online audio/video. Extensive simulation results demonstrate that the RtDG can significantly reduce average delay and packet loss rate by 52.21% and 57.83%, compared to traditional routing strategies. Especially under high traffic conditions, it is able to consider the uncertainty during path selection and achieve excellent routing performance.

The widespread adoption of real-time services on the Internet has aroused interest in the study of low-latency and deterministic communications. Deterministic guarantee over wide-area networks (WANs), the primary infrastructure for communications, is essential to achieving end-to-end deterministic transmission. However, applying off-the-shelf deterministic schemes to WANs is challenging due to the statistical multiplexing nature of WANs and the non-periodic nature of WAN traffic. In this paper, we propose a novel deterministic framework for WANs, named DetWAN, which guarantees the timely delivery of WAN traffic via deadline-aware routing and scheduling. We design a coordinated earliest deadline first (CEDF) scheduling scheme in the data plane of the DetWAN, which provides determinism for non-periodic deadline-constrained traffic while following statistical multiplexing. To precisely estimate the capacity of deadline-constrained traffic that the DetWAN can satisfy, we derive an end-to-end deadline satisfiability criterion in the DetWAN by introducing the deadline curve into traffic modeling. Based on the criterion, we formulate the deadline-aware routing and scheduling problem as a stochastic optimization problem to maximize the timely delivery ratio. Furthermore, we propose a distributed admission control algorithm based on multi-agent deep reinforcement learning in the control plane to solve the problem in a highly autonomous manner. The algorithm can jointly determine optimal routes and per-hop deadline budgets for traffic flows in a decentralized mode. Extensive evaluation results validate the deterministic guarantee as well as the high throughput of the DetWAN and show that the proposed admission control algorithm can significantly improve the timely delivery ratio compared with benchmarks in WAN scenarios.

Low-Earth orbit (LEO) satellite communications play a vital role in global and emergency communications and the movement of LEO satellites around the Earth necessitates frequent handovers for terrestrial users. Efficient handovers are complicated due to the limited coverage and rapid movement of LEO satellites, as well as the high mobility of user equipment (UE), leading to significant handover overhead. To address these challenges, this paper introduces an efficient area-division based handover scheme, in which, the Earth's surface is divided into areas. To reduce the impact of the UE's speed on the handover decision and reduce the handover overhead, handover decisions are made for areas instead of individual UEs based on the satellite trajectories and area characteristics when UEs move within the same area. The graph-based approach is used to compute the handover sequence, which takes multiple handover factors into account to ensure handover performance. We analyse the effects of area size and UE's speed on the overhead and handover failure rate. The experimental results verify that the proposed handover scheme can reduce the handover overhead and improve the handover success rate and data transmission efficiency.