The integration of Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) allows for efficient, advanced network services via Service Function Chains (SFCs). SFCs are sequences of ordered network functions designed to provide specific network services. However, the placement of SFCs is critical, especially for latency-sensitive applications such as telemedicine, due to spatial proximity between service functions, their processing order, and limited edge resources. This paper addresses the multi-SFC placement problem in MEC-NFV networks, aiming to reduce both deploying cost and routing cost. We propose an innovative algorithm based on topological sort, called TD-NFP, to solve this problem. The experimental results show that the proposed TD-NFP approach outperforms other benchmarks.

Videos are projected to account for roughly 80% of global mobile data traffic by 2028. Many camera-equipped mobile devices, such as surveillance drones, require realtime video analytics, encompassing tasks like object detection and action recognition. These devices, restricted by limited resource constraints, need to offload videos to Mobile Edge Computing (MEC) to simultaneously optimize video analytics performance and minimize delay. However, MEC is facing significant challenges in providing efficient video offloading solutions, especially due to uncertainties caused by dynamic device mobility and the associated trade offs in selecting video quality for offloading. Offloading high-quality videos enhances video analytics performance, such as object detection accuracy. Yet, as a mobile device relocates, lower video quality or serving by a different MEC cloudlet may be required (triggering a service migration) to maintain a satisfactory service performance. In this paper, we study the Video Offloading Problem (VOP) in MEC to address these challenges. We propose two uncertainty-aware approaches that model the uncertainties in the environment to solve VOP. Our first approach, focusing on system side, is based on Two-stage Stochastic Program and proposing a unique clustering-based Sample Average Approximation to effectively solve TSP-VOP. The second approach, focusing on device side, employs an online learning algorithm based on a multi-armed bandit to learn and select the optimal offloading solution online. Through extensive experiments, we show that our proposed approaches significantly enhance video offloading decisions, with high video quality and reduced service migration costs under uncertain device mobility, compared to other benchmarks.

The increasing demand for high-volume multimedia services through mobile user equipment (UEs) has imposed a significant burden on mobile networks. To cope with this growth in demand, it is necessary to extend the 5G network's ability to meet quality-of-service (QoS) requirements. The integration of Multi-access Edge Computing (MEC) with 5G technology, 5G-MEC, emerges as a pivotal solution, offering ultra-low latency, ultra-high reliability, and continuous connectivity to support various latency-sensitive applications for UEs. Despite these advancements, the mobility of UEs introduces significant spatio-temporal uncertainties, posing a major challenge on optimizing content delivery routes and directly impacting both latency and service continuity for UEs. Addressing this challenge necessitates suitable approaches for selecting optimal 5G-MEC components, with the goal of minimizing latency and reducing the frequency of handovers, ultimately ensuring a seamless content delivery experience. This paper proposes two learning-based approaches to tackle the problem of 5G-MEC component selection to facilitate QoS-aware content delivery in the absence of complete information about the dynamics of the 5G-MEC environment. First, we design an online sequential decision-making approach, called QCS-MAB, to decide on the content delivery routes in real-time while achieving a bounded performance. We then propose a deep learning approach, called QCS-DNN, to efficiently solve large-scale 5G-MEC component selection problems. We evaluate the effectiveness of our proposed approaches through extensive experiments using a real-world dataset. The results demonstrate that both QCS-MAB and QCS-DNN achieve near-optimal latency and significantly reduced handover times, significantly enhancing the 5G-MEC content delivery experience.

Many mobile device applications require low end-to-end latency to edge computing infrastructure when offloading their computation tasks in order to achieve real-time perception and cognition for users. User mobility brings significant challenges in providing low-latency offloading due to the limited coverage area of cloudlets. Virtual machine (VM)/container handoff is a promising solution to seamlessly transfer services from one cloudlet to another to maintain low latency as users move. However, an inefficient path planning for the handoff can result in system congestion and consequently poor quality of service (QoS). The situation can even worsen by selfish users who intentionally lie about their true parameters to achieve better service at the cost of degrading the whole system's performance. To fill this research gap, we propose an Online Service Handoff Mechanism (OSHM) to provide an efficient path dynamically for transferring VM/container from the current serving cloudlet to a nearby cloudlet at the destination of a mobile user. Our proposed path planning algorithm is based on a label correction methodology, leading to polynomial time complexity. OSHM is accompanied by our proposed payment determination function to discourage misreporting of unknown parameters. We discuss the theoretical properties of our proposed mechanism in implementing a system equilibrium and ensuring truthfulness. We also perform a comprehensive assessment through extensive experiments which show the efficiency of OSHM in terms of workload, handoff time, consumed energy, and other metrics compared to several benchmarks. Experimental results show that OSHM outperforms other algorithms, reducing at least 61% in average workload, 33% in average handoff time, and 29% in average energy consumption.

Downlink mobile edge computing (MEC) networks are requiblack to serve increasing large number of Internet of Things (IoT) devices with limited battery capacity. In order to serve massive user equipments with low power consumption requirements, in this paper, we propose an energy-efficient multi-carrier non-orthogonal multiple access (MC-NOMA) design which allows more than two IoT devices to multiplex and access the same subcarrier band. With the aim to minimize the total transmit energy while meeting the demands of each IoT device such as the low latency, in our design, we first derive the optimal successive interference cancellation (SIC) policy and minimum power allocated to every IoT device. Then we propose an optimal greedy algorithm to allocate the frequency blocks, and formulate the optimization of the computational resource allocation as a min-max problem. Subsequently, we characterize the MC-NOMA network with the potential game model, and present a scheduling scheme to manage massive IoT devices. Simulation results demonstrate that our proposed scheme can consume 3-10 dB less energy in a MEC network deployed with 256 IoT devices compablack with the conventional orthogonal multiple access (OMA) scheme and non-orthogonal multiple access (NOMA) scheme.

Most camera-based mobile devices require ultra low-latency video analytics such as object detection and action recognition. These devices face severe resource constraints, and thus, video offloading to Mobile Edge Computing (MEC) seems a reasonable solution. However, MEC is facing several key challenges-especially due to uncertainties caused by dynamic device mobility-to provide efficient video offloading solutions that enable both maximum performance for video analytics and minimum latency. In this paper, we study the Video Offloading Problem (VOP) in MEC in detail to address these challenges. We formulate VOP as a Two-stage Stochastic Program, called TSP-VOP, to model the uncertainties in the environment. We propose a novel clustering-based Sample Average Approximation to effectively solve TSP- VOP in uncertain dynamic environments, while satisfying the required latency. We perform extensive experiments to validate the effectiveness of our proposed algorithm.

By 2022, videos will account for 82% of global Internet traffic. Many camera-based mobile devices, though with limited resources, require ultra low-latency video analytics such as object detection and action recognition. To facilitate these devices with video offloading solutions, Mobile Edge Computing (MEC) is facing several key challenges due to uncertainties associated with the problem. This paper addresses these challenges by proposing a Two-stage Stochastic Program and a novel clustering-based Sample Average Approximation to effectively solve the video offloading problem in uncertain dynamic environments, while satisfying the required latency. The video offloading quality and other offloading decisions are adaptively made to jointly optimize the video quality and migration cost under the consideration of uncertain device mobility.

Light fidelity (LiFi) provides a promising solution for indoor high-speed wireless transmissions. However, the dense deployment of access points (APs) and the unity frequency reuse in LiFi system may induce the inter-cell interference (ICI) issue. In this paper, we consider the combined transmission (CT) which converts harmful interference to useful signals and thereby combats the ICI, and propose a dynamic user-centric clustering scheme for the downlink LiFi system. In our design, with the objective of maximizing the system throughput under proportional fairness constraints, the CT clustering is formulated as a mixed-integer non- linear programming (MINLP) problem. Then we set up an exact potential game (EPG) model, where the Nash equilibrium is found via the best response algorithm, to provide a suboptimal solution to the MINLP problem. Simulation results demonstrate that the LiFi system using our presented scheme exhibits a higher throughput and a better satisfaction degree than that employing the single-point transmission (SPT) scheme.