Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks.

pdf/unleashing-the-power-of-edge-cloud-generative-ai-in-mobile-3dflik6p.pdf

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving 5G and 6G networks. A 5G/6G network can comprise various network slices from unique or multiple tenants. Network providers need to perform intelligent and efficient resource management to offer slices that meet the quality of service and quality of experience requirements of 5G/6G use cases. Resource management is far from being a straightforward task. This task demands complex and dynamic mechanisms to control admission and allocate, schedule, and orchestrate resources. Intelligent and effective resource management needs to predict the services’ demand coming from tenants (each tenant with multiple network slice requests) and achieve autonomous behavior of slices. This paper identifies the relevant phases for resource management in network slicing and analyzes approaches using reinforcement learning (RL) and DRL algorithms for realizing each phase autonomously. We analyze the approaches according to the optimization objective, the network focus (core, radio access, edge, and end-to-end network), the space of states, the space of actions, the algorithms, the structure of deep neural networks, the exploration–exploitation method, and the use cases (or vertical applications). We also provide research directions related to RL/DRL-based network slice resource management.

https://www.mdpi.com/1424-8220/22/8/3031/pdf?version=1650001357

Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey

Network function virtualization (NFV) and mobile-edge computing (MEC) have been introduced by Internet service providers (ISPs) to deal with various challenges, which hinder them from satisfying ambitious quality of experience demands of the Internet-of-Things (IoT) applications. In NFV/MEC-enabled IoT networks, any IoT service can be expressed as a service function chain (SFC) consisting of several strictly ordered virtual network functions (VNFs), which can be geographically placed onto edge clouds close to IoT terminals. However, regarding the large number of IoT terminals and constant dynamics of IoT networks, determining the placement of VNFs and routing service paths that optimize the end-to-end delays in the hybrid edge clouds is a challenging problem. This problem is also called SFC dynamic orchestration problem (SFC-DOP). To address the SFC-DOP, we are motivated to creatively present an SFC dynamic orchestration framework for IoT deep reinforcement learning (DRL). Also, a DRL-based algorithm for SFC-DOP with the actor–critic and the deterministic policy gradient scheme is provided, which can efficiently deal with the SFC-DOP in IoT networks. The obtained experimental results show the outstanding performance of our proposal compared with the current benchmarks.

Dynamic Service Function Chain Orchestration for NFV/MEC-Enabled IoT Networks: A Deep Reinforcement Learning Approach

Software-defined network (SDN) and network function virtualization (NFV) are acknowledged as the most promising technologies to effectively allocate resource for network service. A service function chain (SFC), which can deploy virtualized network functions (VNFs) and chain them with associated flows allocation, can be used to represent each network service owing to the introduction of the SDN/NFV technology. Co-hosted applications on multiple Internet of Things terminals have dynamic and time-varying service requirements, in order to allocate network resources optimally and meet the end-to-end delay requirements of services, sufficient strategies are required to satisfy the continuously changing service demands. In this article, a deep learning model that combines the multitask regression layer above the graph neural networks is first presented to predict the future resource requirements of each VNF instance. The SFC deployment problem is then solved using the integer nonlinear programming (INLP) approach, and a novel prediction-assisted Viterbi algorithm is presented to overcome the scalability problem of the INLP approach. According to the simulation findings, the proposed deep model provides a minimum of a 6.2% improvement in prediction accuracy over baseline prediction models, and the proposed SFC deployment strategy has been demonstrated to deliver better performance in terms of acceptance ratio and revenue, compared to the current passive deployment algorithms. 

Forecast-Assisted Service Function Chain Dynamic Deployment for SDN/NFV-Enabled Cloud Management Systems

In this brief, the service function chain (SFC) routing in network function virtualization (NFV) with server capacity limitation and user minimum target rate constraint is investigated simultaneously After relaxing the constraints by the addition of extra cost terms, an extended NFV game incorporating the coupled constraints is formulated By using semi-tensor product of matrices, we prove that the formulated game is a potential game which possesses at least one Nash equilibrium We then present the equilibrium point algorithm for solving the SFC routes problem The numerical simulation of two cases with constraints and without constraints illustrates the influence of coupled limitations on the SFC routing

Game Theoretic Approach for a service function chain routing in NFV with coupled constraints

Edge-Integrated Internet-of-Vehicles (IoV) sinks service to edge nodes, which responses quickly to vehicles' requests and alleviates the burden of cloud servers. In such scenario, the entities of IoV need to be mutually authenticated since potential attacks can impersonate edge nodes to send false instructions to vehicles, or impersonate legitimate service subscribers to get free-ride IoV services. Furthermore, due to the high mobility characteristics of vehicles, frequent authentication is required between vehicles and edge nodes. We hope that the authentication process can be conducted efficiently to ensure the continuous service. Existing works cannot balance the security and efficiency well. This is still an issue worthy of discussion. In this article, we propose SEA, a secure and efficient blockchain-assisted authentication scheme for IoV. SEA achieves mutual authentication among vehicles, edge nodes and cloud servers. Specifically, the cloud server is only involved when vehicles are initially authenticated. And edge nodes realize the authentication of vehicles by querying the authentication result recorded by cloud on the blockchain, which significantly reduces the cryptographic computation overhead and eliminates network communication delay. Besides, session keys between any two entities involved are negotiated, which can secure sensitive data of vehicles. Extensive experiments have been conducted to show the security and efficiency of SEA.

Secure and Efficient Blockchain-Assisted Authentication for Edge-Integrated Internet-of-Vehicles

Network function virtualization (NFV) and mobile edge computing (MEC) are two of the promising technologies that are expected to play a critical role in mobile edge cloud networks, thus satisfying ambitious quality of experience (QoE) requirements of the Internet of things (IoT) applications. In MEC-NFV system, a service function chain (SFC) consists of an ordered set of virtual network functions (VNFs) that are connected based on the business logic of service providers. However, the inefficiency of the SFC orchestration process is one major problem due to the dynamic nature of mobile edge cloud networks and abundance of IoT terminals. In this letter, a quantum machine learning (QML)-based scheme is proposed as solution that can handle complex and dynamic SFC orchestration in mobile edge cloud networks. Simulation results show that our proposal significantly provides more than 8-fold reduction of run time compared to the Viterbi algorithm, and the end-to-end delay is only about 1.1 times of the exact solution.

A Novel Approach for Service Function Chain Dynamic Orchestration in Edge Clouds

Network function virtualization (NFV) technology continues gaining attention as a paradigm shift, and telecommunication services can be flexibly deployed and managed, resulting in lower CAPEX and OPEX for the Internet service providers (ISPs). Any telecommunication service is represented by a so-called service function chain (SFC), which can be achieved by a set of virtual network functions (VNFs) to be executed on virtual machines (VMs) in a strictly order according to SLAs. The NFV-enabled SFCs applied in the future 5G networks gradually emerges a challenging problem, particularly more and more users are trying to access their telecommunication services whenever and wherever, SFCs are needed to be dynamically and adaptively reconfigured, thus ensuring lower resource consumption and higher revenues for ISPs. This problem is called service function chain dynamic reconfiguration problem (SFC-DRP), and an integer linear programming (ILP) formulation with implementation in CPLEX is provided to exactly solve this problem. To reduce the algorithm time complexity, a novel heuristic solution is provided that can balance the tradeoff between service provider's revenue and reconfiguration cost. The obtained simulation results show that our proposal can approximate the performance of the ILP within a polynomial time, and outperform the existing benchmark in terms of reconfiguration overhead and the revenue from service provisioning respectively.

/pdf/an-approach-for-service-function-chain-reconfiguration-in-l5ia2sl4kt.pdf

An Approach for Service Function Chain Reconfiguration in Network Function Virtualization Architectures

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Dynamic Service Function Chain Orchestration for NFV/MEC-Enabled IoT Networks: A Deep Reinforcement Learning Approach

Secure and Efficient Blockchain-Assisted Authentication for Edge-Integrated Internet-of-Vehicles

A Novel Approach for Service Function Chain Dynamic Orchestration in Edge Clouds

An Approach for Service Function Chain Reconfiguration in Network Function Virtualization Architectures