The increasing adoption of electric vehicles (EVs) brings new challenges in jointly optimizing mobility, charging behavior, and grid stability. This paper proposes a two-stage optimization framework that integrates Ant Colony Optimization (ACO) for EV routing and a grid-aware heuristic scheduler for vehicle-to-grid (V2G) discharging. The system models both static charging stations and dynamic wireless power tracks under time-of-use electricity pricing and regional load constraints. In the first stage, EVs plan energy-efficient routes while satisfying individual constraints. In the second stage, the proposed Heuristic Load-Balancing Scheduler (HLBS) iteratively refines discharging actions based on regional power demands to reduce grid imbalance. Simulation results based on a large-scale Taiwan road network with 1,500 EV trips show that our method achieves lower total energy cost, reduced regional load variance, and improved frequency stability compared to baseline approaches.

Facing the emergence of 6G and the rapid increase in electric vehicles (EVs), smart parking lots providing real-time services like EV charging have become essential. Edge computing, due to its proximity to end devices, offers low latency and high bandwidth, but its limited resources necessitate efficient allocation. We present a parking lots scenario with edge computing system offering four key services: Supply Equipment Communication Controller, charging space detection, monitoring, and video streaming, along with their QoE models and cor-responding estimation models. We predict system requests for the next time slot and employ the Maximum-chosen algorithm and Collaborative Optimal Decision Search method to optimize service deployment and assignment, maximizing QoE values and resource efficiency. Simulation results validate that we can obtain request status that is more similar to real requests by prediction and the Collaborative Optimal Decision Search method can generate optimal service assignment strategy within different methods.

Vehicular edge computing (VEC) has become a promising solution in electric vehicle (EV) utilization. However, the uneven geographical distribution of service requests may lead to load imbalances among edge servers in different clusters. Thus, the integration of task offloading (TO) and resource allocation (RA) is pivotal for achieving optimal performance in edge computing systems. In this study, we explore an efficient collaborative scheme for task offloading and resource allocation across multiple edge network areas. Initially, we model the Multi-Edge System Delay (MESD) by considering the average end-to-end delay in the system. Subsequently, we introduce the concept of request redistribution using a load-balancing approach to simplify the joint TO & RA problem into a manageable RA problem. Our algorithm mathematically formulates the MESD model and employs a heuristic method to address the formulated problem. Finally, we have compared our proposed work with several baselines and the results confirm the effectiveness of the proposed mechanism.

With the growing demand for latency-sensitive applications in 5G networks, edge computing has emerged as a promising solution. It enables instant response and dynamic resource allocation based on real-time network information by moving resources from the cloud to the network edge. Containers, known for their lightweight nature and ease of deployment, have been recognized as a valuable virtualization technology for service deployment. However, the prolonged startup time of containers can lead to long response time, particularly in edge computing scenarios characterized by long propagation time, frequent deployment, and migration. In this paper, we comprehensively consider image caching, container assignment, and registry selection problem in an edge system. To our best effort, there is no existing work that has taken all the above aspects into account. To address the problem, we propose a novel image caching strategy that employs partial caching, allowing local registries to cache either the least functional or complete version of application images. In addition, a container assignment and registry selection problem is solved by using an edge-based collaborative lazy pulling algorithm. To evaluate the performance of our proposed algorithms, we conduct experiments with real-world app usage data and popular images in a testbed environment. The experimental results demonstrate that our algorithms outperform traditional greedy algorithms in terms of average user response time and cache hit rate.

The advent of 5G-enabled edge servers presents an opportunity to distribute computational tasks to the network edge. This approach helps alleviate the strain on limited central network resources caused by the rapid growth in the number of mobile devices and computation-intensive services. Moreover, it leads to reduced end-to-end delays for users. In this paper, we investigate resource allocation optimization in a dynamic multi-service system, where each service provider (SP) serves geographically dispersed service subscribers (SSs). Each SP can offload tasks to multiple edge servers, while each SS can freely switch between SPs offering homogeneous services. We propose the Multi-Stage Coalition Game Task Offloading (MSCGTO) framework, accommodating scalability, resource heterogeneity, and dynamic conditions. This framework encompasses two distributed algorithms to jointly maximize SP profit and minimize SS end-to-end delay, addressing cost-benefit considerations and user latency acceptance. We conduct extensive simulations and practical experiments with real-world services including augmented reality (AR), online gaming, and live video streaming applications, performed in a controlled testbed environment. The results of our experiments demonstrate that the proposed algorithms yield a 25% increase in system utility considering both the profit of SPs and the end-to-end delay of SSs when compared to existing approaches.

Mobile edge computing (MEC) and 5G networks can provide ultra-low latency connections. Combining the two, caching services at the network edge can greatly reduce the delay of virtual reality (VR) and augmented reality (AR) services, enhancing the Quality of Service (QoS) for users. In this paper, we investigate an efficient collaborative service caching scheme between multiple service providers (SPs) with a game-theoretical approach. We model SPs as players who care about nothing but their profits and can form coalitions by sharing edge server resources as well as costs with other members. More than one coalition can be formed in an edge server. Our algorithm guarantees to reach a Nash equilibrium, where no one has the incentive to deviate. Simulation results show that through the proposed collaboration scheme, SPs can reach a higher profit compared to several baselines as well as previously proposed schemes.

With the development of telecommunication technologies and the proliferation of network applications in the past decades, the traditional cloud network architecture becomes unable to accommodate such demands due to the heavy burden on the backhaul links and long latency. Therefore, edge computing, which brings network functions close to end-users by providing caching, computing and communication resources at network edges, turns into a promising paradigm. Benefit from its nature, edge computing enables emerging scenarios and use cases, such as augmented reality (AR) and Internet of Things (IowT). However, it also creates complexities to efficiently orchestrate heterogeneous services and manage distributed resources in the edge network. In this survey, we make a comprehensive review of the research efforts on service orchestration and resource management for edge computing. We first give an overview of edge computing, including architectures, advantages, enabling technologies and standardization. Next, a comprehensive survey of state-of-the-art techniques in the management and orchestration of edge computing is presented. Subsequently, the state-of-the-art research on the infrastructure of edge computing is discussed in various aspects. Finally, open research challenges and future directions are presented as well.

Recently, Edge Computing (EC) has become a promising enabler to support emerging applications in 5G mobile networks by offloading compute-intensive tasks from devices to proximate EC servers. Meanwhile, Network Slicing (NS) aims to provide service subscribers (SSs) with dedicated network resources based on virtualization techniques so that the service requirements can be guaranteed. The combination of EC and NS can efficiently utilize dynamic network resources at edge networks while improving the Quality of Service (QoS) of SSs. In this paper, we aim to jointly address the problem of dynamic slice scaling and task offloading from the perspective of profit of service providers (SPs) in the multi-tenant EC system. Specifically, we propose a Deep Q-Learning (DQL) based network slicing framework to dynamically reconfigure the scale of radio and computing resources of a slice reserved for a target SP. Then, by exploiting alternative optimization, we proposed a low-complexity algorithm to optimize the real-time offloading ratio and resource allocation policy of slice requests from SSs. To further verify our proposed framework, we have implemented the network slicing testbed with Docker container and conducted a series of experiments based on a real-world traffic dataset and a sample Augmented Reality (AR) application.

With the advancement of the 5G network and Internet of Things (IoT) devices, Multi-access Edge Computing (MEC) proposed by ETSI provides multiple devices to access with low latency through heterogeneous networks such as smart factories and vehicular networks. In addition, video streaming and online gaming have become more popular and consume more than half of the traffic on the internet. Thus, there will be more edge servers deployed on the edge of the network for offloading the core network. However, the edge server is more vulnerable because of its proximity to the user equipment (UE). Attackers can quickly launch distributed denial-of-service (DDoS) attacks with plenty of infected IoT devices. In this paper, we propose Tradeoffs Between Security and Application QoE (TBSA) system to solve the security and resource management problems on the edge server. First, we deploy video streaming, online gaming, and network security applications on the edge server. We use Intrusion Detection and Protection Services (IDPS) to perform DDoS mitigation and design resource allocation algorithm to allocate the computing resources to the edge computing applications. Then, we compare different attack rates in the user scenarios and analyze multiple models under the resource limit condition. The experiments show that we can improve the Quality-of-Experience (QoE) of applications by edge computing resources management.

Recently, Multi-access Edge Computing (MEC) has become a promising enabler to support emerging applications in vehicular networks by offloading compute-intensive tasks from vehicles to proximate MEC servers. However, the high mobility of vehicles brings difficulties to provide reliable services in the MEC system due to potential outages of communication in the process of offloading. Also, load balancing of the MEC system is seldom considered in previous offloading schemes, which may increase the risk of system failure and reduce Quality of Service (QoS) of vehicles due to congestions. Currently, we still lack a low-complexity method to address these issues. In this paper, we aim to promote QoS of vehicular applications by taking vehicles' mobility and latency requirements into account while guaranteeing load balancing of the MEC system. Specifically, we first formulate the joint offloading decision and resource allocation problem as a Mixed Integer NonLinear Programming (MINLP) problem. Then, by taking advantage of both Deep Neural Network (DNN) and Particle Swarm Optimization (PSO), we propose a novel framework to effectively address the problem, where PSO accelerates the training by providing high quality labeled data to DNN. Finally, simulation results show that our proposed method outperforms traditional heuristic algorithms in terms of QoS and runtime.

With the emerging demand for high-definition videos in recent years, Multi-access Edge Computing (MEC) has become a promising solution to leverage Quality of Experience (QoE) of users in the 5G mobile network, which provides computing and cache resource at network edges to serve end users with less latency. Also, since mobile users tend to be influenced by the trends in social media, the performance of video caching will become more effective if we can extract the hidden information from interaction among them. In this paper, we propose a novel Collaborative Social-aware QoE-driven video Caching and Adaption (CSQCA) framework. Specifically, we first design a 2-tier MEC collaborative video caching architecture, which partially caches popular videos among multiple edge servers. Second, we propose a social-aware proactive cache strategy, which embeds interactions of users and video dissemination process in social networks into the caching mechanism. Third, a QoE-driven video adaptation algorithm is presented to dynamically transcode the cached videos into appropriate resolution on edge server for each request. Finally, we conduct our simulation based on real-world datasets. The simulation results show that the proposed CSQCA framework outperforms traditional cache algorithms, in terms of the average hit ratio and QoE.