Vehicle-Infrastructure Collaboration (VIC) integrates vehicles and roadside infrastructure using advanced communication technologies, forming a crucial component of intelligent transportation systems (ITS). In a VIC system, tasks generated by vehicles can either be processed locally or offloaded to nearby edge servers. Current research often focuses on optimizing task scheduling but overlooks the inherent spatiotemporal correlations among tasks, which can lead to redundant computations due to similar tasks producing identical results. Additionally, the diversity in research scenarios and model constructions has resulted in the absence of a unified simulation verification platform, making it difficult to compare and validate various scheduling algorithms. To address these challenges, we have developed a comprehensive VIC simulation platform (CVSP). This platform not only features vehicle simulation capabilities like those of SUMO for modeling vehicle movement, but it also allows for the customization of driving scenarios and configurations, including edge resource settings, and incorporates a unified algorithm execution module for evaluating the performance of scheduling algorithms. Using CVSP can provide a clearer understanding of the strengths and weaknesses of scheduling algorithms, which in turn benefits the development of VIC systems. To tackle the spatiotemporal correlations observed in vehicular tasks, we propose a branch and bound algorithm based on computation-reuse (BB-CR). This algorithm integrates a computational reuse model derived from fused vehicle and road data. We simulate both non-congested and congested scenarios of vehicles traversing intersections to validate the performance of the baseline and BB-CR on the CVSP. The results highlight the versatility of the CVSP, showing that the BB-CR algorithm reduces system costs and minimizes the probability of task loss compared to the baseline. The code is publicly available at https://github.com/lzp991105/comprehensive-VIC-simulation-platform.git.

Federated Learning (FL) has emerged as a promising technique for collaboratively training machine learning models among multiple participants while preserving privacy-sensitive data. However, the conventional parameter server architecture presents challenges in terms of communication overhead when employing iterative optimization methods such as Stochastic Gradient Descent (SGD). Although communication compression techniques can reduce the traffic cost of FL during each training round, they often lead to degraded convergence rates, mainly due to compression errors and data heterogeneity. To address these issues, this paper presents FedQClip, an innovative approach that combines quantization and Clipped SGD. FedQClip leverages an adaptive step size inversely proportional to the$\ell_{2}$norm of the gradient, effectively mitigating the negative impacts of quantized errors. Additionally, clipped operations can be applied locally and globally to further expedite training. Theoretical analyses provide evidence that, even under the settings of Non-IID (non-independent and identically distributed) data, FedQClip achieves a convergence rate of$\mathcal{O}(\frac{1}{\sqrt{T}})$, effectively addressing the convergence degradation caused by compression errors. Furthermore, our theoretical analysis highlights the importance of selecting an appropriate number of local updates to enhance the convergence of FL training. Through extensive experiments, we demonstrate that FedQClip outperforms state-of-the-art methods in terms of communication efficiency and convergence rate.

In the context of Vehicle-Infrastructure Collaboration (VIC), this study addresses the critical challenge of optimizing Service Function Chains (SFCs) deployment within resource-constrained and delay-sensitive vehicular edge networks. By leveraging Virtual Network Function (VNF) technology, which shifts network services from traditional hardware to a more agile, container-based edge computing architecture, we aim to enhance the Quality of Service (QoS) for vehicular users. SFCs, composed of multiple VNFs arranged in a specific sequence, are pivotal for delivering a range of functional services essential for QoS enhancement. Our objective is to optimize the placement of SFCs by reusing VNF instances to minimize both delay and operational costs. The reuse of VNF instances, however, introduces two significant challenges: the effective placement of SFCs within vehicular edge networks and the optimization of VNF instance containers positioning. To address these challenges, we propose a robust framework called SPAVM (Service Placement and VNF Migration), which consists of two primary components: one for SFC placement and the other for VNF migration. For SFC placement, we introduce the Service Placement based on VNF Instance Reuse algorithm (SPVIR), which maximizes the utilization of existing VNF container resources. For VNF migration, we propose the VNF Instance Migration algorithm (VIMA), which considers edge server connectivity to determine optimal migration targets for VNF instance containers. Extensive experiments validate the proposed algorithms' performance, demonstrating their effectiveness in reducing delay and costs, thereby enhancing the overall efficiency of vehicular edge networks.

In the domain of Vehicular Edge Computing (VEC), this paper addresses the complex problem of Service Function Chain (SFC) placement, which is crucial for the efficient deployment of cloud applications in vehicular environments. Our approach leverages Virtual Network Function (VNF) technology, transitioning network services from traditional hardware to more flexible, container-based edge computing frameworks. The primary objective is to organize these VNFs into functional SFCs, thereby minimizing service delays in VEC systems. SFC placement faces two significant challenges. The first is the sequential dependency among VNFs within an SFC, which adds substantial complexity to container deployment. The second challenge is the cold start delay of VNF containers, a critical issue in scenarios requiring swift response times, which negatively impacts service quality in VEC applications. To address these challenges, we propose a comprehensive model for SFC placement that considers the deployment status of VNF containers, acknowledging the complexity of this NP-hard problem. The core of our contribution is the development of the Single SFC Placement Algorithm (SSPA), a sophisticated, greedy-based approach designed for the effective placement of individual SFCs. We enhance this algorithm by incorporating a Particle Swarm Optimization (PSO) technique, making it capable of efficiently handling the placement of multiple SFCs. Our solution aims to improve edge resource utilization and mitigate startup delays associated with the initial activation of containers, thereby reducing service delays. Extensive experimental evaluations demonstrate that our algorithm achieves a 34.3% average reduction in service delay compared to four baselines and a 5.8% average reduction compared to other container-aware algorithms.

Graph Neural Networks (GNNs) have emerged as a powerful technique across various applications, due to their effective message-passing mechanism. However, their deployment is constrained by limited computational resources, energy concerns, and low-latency processing requirements. While existing works employ logit-based knowledge distillation from GNNs to guide Multilayer Perceptrons (MLPs) training, these methods may lead to reduced accuracy and compromised robustness. These drawbacks arise from two primary factors: the insufficient exploitation of the rich information embedded within the graph structures and the inherent susceptibility of MLPs to noisy data. To tackle these issues, we propose a Mixed Multi-order Knowledge Distillation (MMKD) method, which combines the GNN's logits with hidden layer information through the multi-order distillation to improve the accuracy of the MLP. Moreover, we employ both raw data and perturbed data as input, enhancing the density of knowledge extraction as well as the MLPs' generalization. Extensive experiments across seven benchmark datasets verify the superior performance of our approach in terms of effectiveness and robustness. In comparison with the baseline, our approach achieves an accuracy improvement of up to 8.68% in typical GNN tasks.

Lightweight and layered structure containers in serverless edge computing (SEC) provide flexible service configurations and computing for vehicles with diverse service requests in the Vehicle-Infrastructure Collaboration (VIC) environment. Despite progress in service request scheduling for the VIC system, the effect of layer sharing between different service images on request scheduling has not been fully explored. Additionally, the cold-start latency of service containers in SEC can significantly degrade the responsiveness of vehicle services, and container retention is proposed to minimize its impact and improve overall system performance. However, the existing research neglects the complex coupling relationship between request scheduling and container retention decisions, while focusing on the single decision optimization problem. Consequently, minimizing system costs by single decision optimization may not achieve the effect of joint decision optimization. To bridge this gap, we study the joint service request scheduling and container retention problem based on layer sharing and container caching. First, we model the joint decision problem with specific constraints and aim to minimize the long-term system cost while considering vehicle mobility. Second, an online co-decision scheme called Onco is proposed to solve the problem, which incorporates request scheduling and container retention for multiple vehicle services. Finally, both synthetic and real trace-driven simulation experiments have been conducted to evaluate the performance of Onco. The experimental results show that Onco outperforms state-of-the-art baselines in terms of system cost reduction and response time improvement.

Lightweight containers in serverless-based vehicular edge computing (SVEC) offer flexible service provisioning for edge service providers (ESPs), enabling quick response to various service requests from mobile vehicles and improving the quality of service delivery. However, the unpredictability of vehicle mobility and the variability of service requests pose significant challenges to service placement for ESPs. Moreover, the cold-start latency experienced by service containers in SVEC can greatly hinder the responsiveness of vehicle services. To mitigate this impact and enhance the overall system performance, container retention is introduced as a solution. In this paper, we study the joint service placement and container retention problem in the dynamic SVEC system. First, we model the joint decision problem with specific constraints and aim to maximize the profit of each edge considering vehicle mobility. Second, an online co-decision scheme called CMU-O is proposed to solve the problem, which adopts an improved upper confidence bound method based on dynamic osmotic pressure (UCB-OP). Finally, the experimental results demonstrate that the service request success rate of the CMU-O is on average 7% higher than the baselines. Furthermore, the profits obtained under the CMU-O are also on average 23% higher than the baselines.

Container has been widely used in application development and management systems. However, there are two major challenges faced in the real deployment at edge servers. The varying workload of service requests and the startup delay of containers force a flexible resource provisioning scheme in containerized edge computing. To this end, we proposeCEC, a containerized edge computing framework for dynamic resource provisioning, and especially for the smart connected community where exists multiple intelligent applications.CECintegrates workload prediction and resource pre-provisioning to enable low latency of user service requests and high utilization of edge resources. First, we present an online periodic request prediction algorithm. Then, we designed a control-based resource pre-provisioning algorithm based on the predicted request distribution, which is a self-adaptive controller to tune the resource for containers. We evaluate the performance ofCECby simulation and system experiments. The simulation experiment shows that the prediction accuracy of the proposed algorithm is higher than other two prediction algorithms. The testbed experiments demonstrate that the control-based resource pre-provisioning algorithm has low service latency and high resource utilization compared with baselines.

With the development of Internet of Things and mobile computing, the explosive proliferation of latency-sensitive applications raises high computation demands for mobile devices. To this end, offloading computation of applications to edge-enabled Internet of Vehicles (IoV) has emerged as an effective solution. However, most of the existing studies on this issue assume that IoV can be easily formed in the practical environment, and neglect the dependency relationship between tasks of the offloading application. In this article, we first give several observations based on the analysis results of the real traffic dataset to verify the feasibility of aggregating vehicular resources in the real world. Then, we design a Latency-aware Real-time Scheduling Framework for the edge-enabled IoV, named LARS, in which mobile users can offload applications to LARS, and the offloading tasks can be scheduled to the appropriate vehicular resources in real-time. First, we propose a clustering-based algorithm to generateHerds, which treats connected vehicles as edge computation resources to provide cooperative computing services. Second, considering the dependency relationship between tasks in the job, we present a greedy-based task scheduling algorithm for offloading jobs, the objective of which is to minimize the total latency of the job as well as maximize the resource utilization ofHerds. The simulation experiment based on the real traffic dataset shows thatHerdsgenerated by the proposed clustering-based algorithm can maintain a stable period to provide computing service, and the experiments on testbed include two case studies demonstrate that the superiority of the proposed scheme compared to baselines, in terms of latency and resource utilization.

With the rapid development of Internet social media platforms and the popularization of smart terminal devices, video services such as short videos have surged. However, the massive amount of smart devices connected to the core network causes the increase of load on the backhaul link, which makes traditional cloud computing unable to meet the low-latency requirement of user for video services fully. To this end, we propose to implement proactive video caching at the edge to improve the quality of user experience. Thus, a novel collaborative video caching framework at the edge, CVC, is proposed. In this framework, we present a video request prediction model based on federated learning, aiming to reduce communication costs. Also, we design two methods to improve the hit rate and reduce latency, including a collaborative caching decision method (CCD) and a collaborative service response method (CSR). Under the video service scenario, the experimental results show that CVC can improve the cache hit rate and reduce the waiting delay of users compared with the traditional caching algorithms. Simultaneously, CVC can also reduce the overall system’s communication cost and caching cost.

In the era of 5G, with the increasing demands on computation and massive data traffic of the Internet of Things (IoT), mobile edge computing (MEC) and ultradense network (UDN) are considered to be two enabling and promising technologies, which result in the so-called ultradense edge computing (UDEC). Task offloading as an effective solution offers low latency and flexible computation for mobile users in the UDEC network. However, the limited computing resources at the edge clouds and the dynamic demands of mobile users make it challenging to schedule computing requests to appropriate edge clouds. To this end, we first formulate the transmitting power allocation (PA) problem for mobile users to minimize energy consumption. Using the quasiconvex technique, we address the PA problem and present a noncooperative game model based on subgradient (NCGG). Then, we model the problem of joint request offloading and resource scheduling (JRORS) as a mixed-integer nonlinear program to minimize the response delay of requests. The JRORS problem can be divided into two problems, namely, the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a double decision-making problem and propose a multiple-objective optimization algorithm based on i-NSGA-II, referred to as MO-NSGA. The simulation results show that NCGG can save the transmitting energy consumption and has a good convergence property, and MO-NSGA outperforms the existing approaches in terms of response rate and can maintain a good performance in a dynamic UDEC network.