Edge computing is booming as a promising paradigm to extend service provisioning from the centralized cloud to the network edge. Benefit from the development of serverless computing, an edge server can be configured as a carrier of limited serverless functions, in the way of deploying Docker runtime and Kubernetes engine. Meanwhile, an application generally takes the form of directed acyclic graphs (DAGs), where vertices represent dependent functions and edges represent data traffic. The status quo of minimizing the completion time (a.k.a. makespan) of the application motivates the study on optimal function placement. However, current approaches lose sight of proactively splitting and mapping the traffic to the logical data paths between the heterogeneous edge servers, which could affect the makespan significantly. To remedy that, we propose an algorithm, termed as Dependent Function Embedding (DPE), to get the optimal edge server for each function to execute and the moment it starts executing. DPE finds the best segmentation of each data traffic by exquisitely solving several infinity norm minimization problems. DPE is theoretically verified to achieve the global optimality. Extensive experiments on Alibaba cluster trace show that DPE significantly outperforms two baseline algorithms in makespan by 43.19% and 40.71%, respectively.

In mobile edge computing (MEC) systems, mobile users (MUs) are capable of allocating local resources (CPU frequency and transmission power) and offloading tasks to edge servers in the vicinity in order to enhance their computation capabilities and reduce back-and-forth transmission over backhaul link. Nevertheless, mobile environment makes it hard to draw offloading and resource allocation decisions under dynamical wireless channel state and users’ locations. In real life, social relationship is also provably a significant factor affecting integral performance in collaborative work, which results in MUs decisions strongly coupled and renders this problem further intractable. Most of previous works ignore the impact of inter-user dependency (or data dependency among IoT devices). To bridge this gap, we study the service collaboration with master-slave dependency among service chains of MUs and formulate this combinational optimization problem as a mixed integer non-linear programming (MINLP) problem. To this end, we derive the closed-form expression of resource allocation solution by convex optimization and transform it to integer linear programming (ILP) problem. Subsequently, we propose a distributed algorithm based on Markov approximation which has polynomial computation complexity. Experimental result on real-world dataset substantiates the usefulness and superiority of our scheme, in terms of reducing latency and energy consumption.

The centralized storage and centralized authorization approach in medical information systems can lead to data tampering and private information privacy leakage, while the traditional access control model has an overly simple authentication approach, relies excessively on trusted third-party organizations for the enforcement of access control policies, and has low efficiency in processing access requests. To address these problems, this paper proposes an access control model based on the collaboration of blockchain main and side chains, AC-BMS. Firstly, a password-based authentication scheme is designed based on doctors’ identity information; then Polygon side chain is designed to enhance the storage scalability of the blockchain; finally, the access node information on the main Ethereum chain is located on the side chain, and resources are obtained by executing Roll-up contracts deployed on the side chain. It is confirmed by simulation experiments in Hyperledger Fabric that the access efficiency and throughput of the blockchain access model proposed in this paper are improved when the number of accesses is multiplied, the average access time is saved by 2–3 s, the latency time is floating and stable, and the security, scalability, and availability are enhanced.

The escalating challenges in urban traffic highlight the importance of accurate traffic forecasting for enhancing the Intelligent Transportation Systems (ITS), given its pivotal role in optimizing traffic management strategies. Due to the impressive performance of Graph Neural Networks in spatio-temporal data analysis, Spatio-Temporal Graph Neural Networks (STGNNs) have emerged as a mainstream approach for traffic forecasting. However, many recent STGNN-based works often utilize redundant and intricate architectures, leading to increased computational costs with only minor improvements in performance. To solve this issue, we propose a concise but effective Lightweight Spatio-Temporal Neural Network architecture, LSTNN. Specifically, we design two sampling strategies, downsampling and patched sampling, to decompose the time series and separately fit the seasonal component and trend components. Based on sampling strategies, we introduce a spatio-temporal localization module based on the raw traffic data and learnable spatio-temporal indexing. In particular, this module models spatio-temporal correlations by appending the spatio-temporal indexing to the traffic sequences. We visualized the spatio-temporal indexing, confirming the effectiveness of the spatio-temporal localization module design. Extensive experiments are conducted on four widely utilized real-world traffic datasets, showing the significant improvement of LSTNNcompared to baselines, including several state-of-the-art models. Furthermore, we conducted extensive experiments on four long-term time series datasets to validate the effectiveness of LSTNNin the Long-term Time Series Forecasting (LTSF) task. Our findings may inspire and encourage the design of more concise neural prediction architectures for real-world applications. We release the datasets and baseline implementations at: github.com/HduDBSI/LSTNN