Cloud-edge-device (CED) cooperative computing is an emerging paradigm that extends the reach of cloud services, providing higher flexibility and scalability to modern AI-driven computing services. However, traditional “one-size-fits-all” AI model construction at the edge struggles to accommodate the strong heterogeneity of target devices. This leads to an increasing demand for specialized models tailored for local resources in AI applications. To this end, we introduce ComHA, a cooperative model building framework based on hierarchical Automated Machine Learning (AutoML) to bridge the gap between hyperparameter optimization on the cloud and local model customization at the edge. In this framework, the cloud performs high-level AutoML to reduce the search space of learning algorithms, model architectures, and relevant hyperparameters to a specific set based on target device specifications. Subsequently, edge devices execute low-level AutoML to identify and train the optimal model, customized for their local data and resources. This approach aims to strike a balance between the benefit and cost of customized model building. Through extensive experiments conducted on a real-world testbed with public datasets, our results demonstrate that ComHA outperforms traditional methods in producing tailored models of high accuracy and low inference latency in various environments.

In End-Edge-Cloud (EEC) computing environments, the diversity of devices often requires cloud-trained models to be adapted for end/edge devices, complicating decentralized project management. To address this, end/edge devices are increasingly using local model sharing instead of traditional cloud solutions. Popular platforms like GitHub and DockerHub lack the necessary data authenticity and security for high-stakes applications. While blockchain can ensure secure data sharing, permissioned blockchains struggle with the dynamic nature of EEC devices. To solve this, we propose BlockEdge, a hybrid blockchain architecture combining a permissioned blockchain with Practical Byzantine Fault Tolerance (PBFT) for cloud-based data management and a permissionless blockchain with Proof of Work (PoW) for decentralized model sharing at the end/edge. We enhance the PoW process with a dynamic mining algorithm and a lazy-loading Merkle tree structure, improving energy efficiency and computational performance. Experimental results show that BlockEdge reduces energy consumption by over 50% and cuts data update time by 91.73%, effectively addressing the energy and time inefficiencies of mainstream consensus mechanisms.

The fuzzy broad learning system (FBLS) has received increasing attention due to its ability to quickly train from broad learning systems (BLS) and interpretability with fuzzy inference. However, the randomness of BLS brings instability to the training performance of the model, so the hyperparameters of the model are crucial for its performance. Currently, many FBLS use grid search to determine hyperparameters. However, grid search brings longer search time and the parameters obtained have randomness, which may not necessarily be the optimal hyperparameters. In response to these challenges, this paper proposes a fuzzy broad learning system with automatic determination of hyperparameters (ADHFBLS). We construct a novel FBLS based on the interval type-2 fuzzy set and design an incremental learning algorithm for rules and enhancement nodes to support rapid model expansion. Meanwhile, a heuristic hyperparameter automatic optimization algorithm is designed to overcome the randomness and long optimization time of grid search. Experiments have shown that ADHFBLS has higher accuracy and shorter model tuning time compared to some state-of-the-art models based on FBLS.

In Mobile Edge Computing (MEC) scenarios, computational tasks are popularly deployed using containerization to isolate the runtime environment. To complete the execution of the task, the edge server first pulls the image, then instantiates and runs the container. Since it takes a lot of time for the edge server to download the image from the cloud, image reuse reduces the pulling latency significantly. However, the limited storage capacity of edge servers hinders image reuse. Recent works have enhanced reuse efficiency by leveraging the hierarchical structure of images and caching high-value layers. However, their efficiency remains limited due to the lack of multi-container collaboration. This paper proposes a novel container scheduling strategy based on image layer reuse and sequence arrangement (ILR-SA) for MEC scenarios, which achieves efficient scheduling by collaborating multiple containers. First, containers are greedily deployed into the edge cluster. Then, the execution sequence of containers is modeled as an optimal Hamiltonian path problem, efficiently solved by our proposed decomposition algorithm. Finally, an efficient image layer update strategy is used to achieve layer reuse. We conduct rigorous experiments to demonstrate that our proposed container scheduling strategy reduces the computational task completion time by up to 91.3% compared to existing approaches.

Task scheduling in cloud computing is a challenging but crucial task for ensuring service quality and load balance. Mainstream scheduling algorithms, such as heuristic algorithms and reinforcement learning, have made progress in this area. However, online task scheduling algorithms, such as reinforcement learning, can pose computational challenges in scenarios with limited computational power and heterogeneous resources. Heuristic algorithms, which are more suitable for offline scheduling where the types and quantities of tasks are known in advance, also require substantial computational resources for online scheduling. In this work, we propose the end-edge-cloud (EEC) heterogeneous resources scheduling method (EHRSM) based on a recurrent neural network (RNN) model and particle swarm optimization (PSO). EHRSM uses an RNN model trained on a dataset generated by dynamic programming to recognize and cache online tasks, efficiently transforming online task scheduling into offline scheduling. Additionally, a PSO algorithm with Cantor expansion (CE) for coding optimization is used to complete the offline scheduling. Experimental results show that the method is effective in converting online scheduling to offline scheduling, reducing the average task completion time and waiting time. Compared with existing online scheduling methods, EHRSM reduces task completion time by up to 48.24%.

Since the rapid development of deep learning (DL) technology, large-scale GPU clusters receive a large number of DL workloads daily. To speed up the completion time, the workloads usually occupy several GPUs on a server. However, workload scheduling inevitably generates resource fragmentation, which results in many scattered GPU resources being unavailable. Existing works address improving resource utilization by reducing GPU resource fragmentation, while they focus on resource scheduling for a single cluster and ignore multiple clusters. Multi-cluster scenarios, such as virtual clusters and geo-distributed clusters, require load balancing to avoid some clusters exhausting resources while some clusters are idle while improving resource utilization, which is not well addressed by existing works. In this paper, we propose MCG-Sched, a scheduling strategy to reduce resource fragmentation in multiple GPU clusters while maintaining load balancing among clusters. MCG-Sched measures the fragmented resources with the distribution of workload demands and uses a scheme that minimizes fragmentation in workload scheduling. Meanwhile, MCG-Sched achieves balanced load scheduling across clusters through the load balancing index. MCG-Sched senses the workload requests in the waiting queue, and prioritizes the workloads by combining fragmentation measurement and load balancing index to maximize resource utilization and load balancing during load peak. Our experiments show that MCG-Sched reduces unallocated GPUs up to 1.45× and workload waiting time by more than 40% compared to existing fragmentation-aware methods and achieves effective load balancing.

Predicting the runtime of tasks is of great significance as it can help users better understand the future runtime consumption of the tasks and make decisions for their heterogeneous devices, or be applied to task scheduling. Learning features from user task history data for predicting task runtime is a mainstream method. However, this method faces many challenges when applied to edge intelligence. In the Big Data era, user devices and data features are constantly evolving, necessitating frequent model retrains. Meanwhile, the noisy data from these devices requires robust methods for valuable insight extraction. In this paper, we propose an edge server-oriented deep neuro-fuzzy system (ESODNFS) that can be trained and inferred on edge servers, for providing users with task runtime prediction services. We divided the dataset and trained it on multiple improved adaptive-network-based fuzzy inference system units (ANFISU), and finally conducted joint training on a deep neural network (DNN). By partitioning the dataset, we reduced the number of parameters for each ANFISU, and at the same time, multiple units can be trained in parallel, supporting fast training and iteration. Additionally, the application of fuzzy inference can effectively learn the features in noisy data and make accurate predictions. The experimental results show that ESODNFS can accurately predict the runtime of real tasks. Compared with other DNN and DNFS, it can achieve good prediction results while reducing training time by over 35%.

Today's Industrial Internet of Things (IIoT) have achieved excellent manufacturing efficiency and automation results by leveraging machine learning (ML) and deep learning (DL). However, trustworthiness of ML/DL brings significant challenges to IIoT. This article proposes an evolving deep multiple kernel learning network through genetic algorithm (KNGA). Our KNGA method uses genetic algorithm (GA) to find the best deep multiple kernel learning structure, including the weights and the topology of the model. Compared with the current well-known models, KNGA has advantages in three aspects: 1) It can achieve good results without using many samples during model training; 2) the model can evolve in the process of training, including self-growth, and self-pruning; and 3) its trustworthiness and reliability can be guaranteed. Moreover, the whole model ensures excellent performance and requires manual adjustment of only a few parameters. Extensive experiments on the UCI, KEEL, Caltech256, and MNIST datasets demonstrate the effectiveness and trustworthiness of the proposed method.