The hierarchical cooperative resource scheduling architecture provides a promising direction for efficient collaborative processing of edge computing under the dynamic and intricate landscape of the Industrial Internet of Things (IIoT). However, existing scheduling algorithms often struggle to effectively capture the intricate information features inherent in hierarchical and collaborative domains, leading to suboptimal solutions. To tackle this challenge, we introduce a novel hierarchical cooperative resource scheduling framework based on Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL). We first leverage hierarchical GNN to facilitate seamless information exchange among internal nodes and adjacent nodes between layers in the hierarchical structure and transform it into node embeddings. These meticulously designed embeddings are then input into the policy model of DRL for the iterative learning process to generate higher-quality solutions by leveraging global feature information. Experiment results unequivocally demonstrate the superiority of our approach over baselines in terms of scheduling performance. Furthermore, our model exhibits robust generalization capabilities across various scenarios.

In the Industrial Internet of Things (IIoT) environment, time-sensitive tasks require efficient utilization of computing and network resources to ensure timely completion and fast processing. However, using existing scheduling schemes based on software-defined network (SDN), time-sensitive network (TSN), or information technology (IT) can lead to problems such as resource inefficiency, transmission delays, and task timeouts. To overcome these challenges, this paper proposes a collaborative scheduling method that combines SDN for global resource management and TSN for precise resource allocation. Additionally, a control plane algorithm is introduced to adapt task resources. This approach effectively schedules global computing and network resources, enabling timely and efficient task processing. Extensive experiments demonstrate the superiority of this method in terms of task completion rate and processing time compared to other baselines in various network environments.

Cloud-edge collaboration, as an emerging computing paradigm, aims to solve the shortcomings of remote transmission of conventional cloud computing. More precisely, it combines the powerful resource service capability of cloud computing with the advantages of low latency and relatively low energy consumption of edge computing to achieve the goal of optimization of various applications. However, with the rapid growth of computation-intensive industrial tasks, the overload problem of edge networks is becoming increasingly serious. Prior studies usually assume that the real-time state of edge resources has been known when selecting the offloading strategy so as to classify and execute tasks, but do not consider the fragmentation and heterogeneity features of edge computing resources. In light of these, we first generalize and model the computing resources of the edge nodes uniformly and then propose new heterogeneous task classification and recognition methods empowered by edge intelligence. We conduct intensive experiments to justify that our proposed design can minimize the data transmission delay caused by repeated computational tasks while saving energy consumption.

The rapid development of the Industrial Internet of Things (IIoT) provides a significant opportunity to achieve comprehensive awareness and salient event detection in manufacturing factories. However, because of the limited onboard resources of IIoT terminal devices, it remains a challenging task in the face of the processing requirements of compute-intensive and latency-critical applications. To overcome this challenge, we study the hierarchical collaboration dynamic resource scheduling problem for the IIoT cloud-edge computing model. First, we divide the network into different domains for autonomous management and hierarchical collaboration according to the dynamically available computing resources and transmission delay of edge nodes. Second, we establish a computing model of task data size and resource requirement to maximize the processing benefit of tasks and load balancing between domains, formulate the task-domain Pareto optimality matching problem and the task-node optimal matching problem, which is transformed into the 0-1 Multiple Knapsack Problem (MKP). Third, we develop a hierarchical collaboration dynamic resource scheduling algorithm to solve the above optimal matching problems and set each time slot duration according to the processing rate of the algorithm. Extensive experiments show that our method provides an efficient and reliable scheduling strategy for IIoT in various scenarios with good scalability.

The unprecedented prosperity of the Industrial Internet of Things (IIoT) promotes the traditional industry transforming into intelligent manufacturing so that the whole production process can be comprehensively controlled to achieve flexible production. Intelligent scheduling, as one of the key enabling techniques, is desired to allocate the production of several machines by an efficient solution with minimum makespan. Existing approaches adopt a fixed search paradigm based on expert knowledge to seek satisfactory solutions. However, considering the varying data distribution and large sized of the practical problems, these methods fail to guarantee the quality of the obtained solution under the real-time requirement. To address this challenge, we formulate the production scheduling problem as a Markov decision process (MDP) and specifically design a job scheduling model made up of a job batching module for the hybrid flow-shop scheduling problem on batch processing machines (HFSP-BPM). Our proposed model consists of an actor network that learns the action under different conditions and a critic network that evaluates the action of the actor. We analyze the convergence of the model under different parameter settings to determine the optimal parameter. Extensive numerical experiments on both publicly available data set and real steel plant production data set demonstrate that the proposed deep reinforcement learning (DRL) approach compared with other baselines, more than 6% average improvements can be observed in many instances.

The Industrial Internet of Things (IIoT) is deemed a promising direction to drive a new industrial revolution. However, due to the isolation of the existing OT network and IT network, the requirements of low latency, low jitter, and high reliability for transmission and processing of industrial time-sensitive tasks data traffic in IIoT scenarios with strong dynamic and complex topology face a series of non-trivial challenges. In this paper, we propose a hierarchical computing network collaboration architecture for IIoT based on edge/fog computing. Our architecture is built upon the Time-Sensitive Networking (TSN) to flexibly support different requirements of large-scale industrial production applications by constructing the hierarchical computing network collaboration domain, combined with an improved Cyclic Queuing and Forwarding (CQF) scheduling shaper mechanism. We tackle the critical problems of architecture design by presenting three essential components. Moreover, we build and implement our simulation testbed based on Omnet++, and evaluate our design.