Multi-access Edge Research Articles

Time Efficient Offloading Optimization in Automotive Multi-Access Edge Computing Networks Using Mean-Field Games

Emerging connected vehicular services, such as intelligent driving and high-definition (HD) map, are gaining increasing interest with the fast development of multi-access edge computing (MEC). For most time-sensitive and computation-intensive vehicular services, the data offloading process significantly influences the capacity and performance of MEC, especially when the number of connected vehicles is enormous. In this work, we consider data offloading optimization for a large-scale automotive MEC network. The problem is challenging due to the large number of connected vehicles and the complicated interaction between vehicles and edge servers. To tackle the scalability problem, we reformulate the original offloading optimization problem into a Mean-Field-Game (MFG) problem by abstracting the interaction among the connected vehicles as a distribution over their state spaces of task sizes, known as the mean-field term. To solve the problem efficiently, we propose a G-prox Primal-Dual-Hybrid-Gradient (PDHG) algorithm that transforms the MFG problem into a saddle-point problem. Based on our developed MFG model and G-prox PDHG algorithm, we propose the first data offloading scheme whose computation time is independent of the number of connected vehicles in automotive MEC systems. Extensive evaluation results corroborate the superior performance of our proposed scheme compared with the state-of-the-art methods.

UAV-Aided Vehicular Short-Packet Communication and Edge Computing System Under Time-Varying Channel

In this paper, a novel UAV-aided vehicular edge computing (VEC) network is proposed, where the vehicle and on-board UAV provide multi-access edge computing (MEC) service for the roadside Internet of Things (IoT) devices. In this system, considering the time-varying channel, we derive the lower bound of signal-to-noise ratio (SNR) based on the first-order Gauss-Markov process. Then, with the short-packet transmission, we maximize the total amount of computation by jointly optimizing the communication scheduling, the trajectories of the vehicle and on-board UAV, and the computing resource, subject to the mobility, connection and computation constraints. The formulated optimization problem is a mix-integer non-convex problem. To efficiently solve it, we propose an alternative algorithm based on the Lagrangian dual decomposition and successive convex approximation technique. Extensive simulation results are provided to show the performance gain of the proposed algorithm.

Data Analysis-Oriented Stochastic Scheduling for Cost Efficient Resource Allocation in NFV Based MEC Network

Recently, there has been increasing interest in data analysis services based on MapReduce framework, which is a programming model and an associated implementation for processing and generating Big Data sets with a parallel, distributed algorithm on a cluster. Due to limited resource configuration, it is hard for end users to deal with computing-intensive data analysis tasks by themselves. Therefore, outsourcing these tasks to nearby edge computing servers (i.e., multi-access edge computing) becomes a key step towards the next generation mobile networks. However, new challenges arise for designing cost efficient distributed system by jointly managing communication and computing resources with unpredictable tasks arrival mode in complex spatial and temporal domains. In this paper, based on the idea of network function virtualization (NFV), we distribute a series of operation functions of MapReduce framework on distributed edge computing servers to execute data analysis service and investigate how to dynamically minimize the overall operational cost with joint consideration of workflow scheduling, network function management, resource allocation, and system stabilization. Our method leverages Lyapunov optimization technique to make a tradeoff between the queue backlog and overall cost without using future information. Simulation results show that our method can effectively reduce computing and communication cost while guaranteeing quality of service for end users.

NSEN: Improving Session Efficiency in Distributed MEC Networks

The rapid development of 5G technology and edge computing prompts emerging edge applications with heterogeneous requirements for network services with respect to latency, reliability and throughput. Although content distribution networks (CDN) and edge computing proposals such as multiaccess edge computing (MEC) improve quality of service by deploying services close to end users at the edge, the session efficiency of service still is constrained by host name resolution which also leads to quality degradation of service in the case of user mobility. In this article, we proposed a novel network architecture, Named Service Edge Network (NSEN), on the basis of DNS protocol. We enabled NSEN functionality in the MEC reference architecture and presented high-efficiency session models to satisfy the requirements of various edge applications and enhance the mobility support in distributed MEC environment. The simulation and experiment results demonstrate the NSENenabled MEC architecture can improve session efficiency of MEC services in 5G cellular networks in terms of latency and throughput.

AI Service Placement for Multi-Access Edge Intelligence Systems in 6G

This paper studies the artificial intelligent (AI) task deployment problem of a multi-access edge intelligent system in a 6G network, in which the cloud server broadcasts the AI program to the edge computing nodes. In particular, task nodes can perform remote processing by offloading AI tasks to cloud servers or other edge computing nodes, or they can perform processing tasks locally. In order to minimize the total computing time and energy consumption of all task nodes and maximize the inference accuracy of AI tasks, we jointly optimize the resource allocation and computing offloading decision of each node by solving a mixed-integer non-linear programming (MINLP) problem. In order to efficiently solve this non-convex problem, we propose an alternating direction multiplier method (ADMM) based algorithm, which effectively decomposes the problem into easy-to-handle MINLP subproblems. Through the proposed ADMM-based algorithm, each task node can use local channel state information (CSI) to optimize its calculation mode and resource allocation, which is more suitable for large-scale networks. The simulation results show that this method is significantly better than other benchmarks in various network environments, and the computational complexity is relatively low.

Rendered Tile Reuse Scheme Based on FoV Prediction for MEC-Assisted Wireless VR Service

Interactive real-time wireless Virtual Reality (VR) has become increasingly popular because it provides an immersive experience for VR users anytime, anywhere. Introducing multi-access edge computing (MEC) to wireless VR service can solve the problem that VR devices having insufficient rendering power. However, the problem that some tiles in the overlapping field of views (FoVs) may be rendered repeatedly usually be ignored. Repeated rendering would waste the computing resources of edge nodes and damage users' quality of experience (QoE). This paper proposes a rendered tile reuse scheme based on FoV prediction and 3 C (caching, computing, and communication) optimization for the MEC-assisted VR service. Firstly, we model the above scheme as an optimization problem that aims to maximize the total users' QoE value under the motion to photons (MTP) delay constraints. Secondly, we use the recurrent neural network model with gated recurrent unit (GRU) architecture to dynamically predict the users' FoV in the next time slot. Thirdly, we use the proximal policy optimization (PPO)to learn the question's solution iteratively based on the results of FoV prediction. The simulation results show that our proposed algorithm is superior to other algorithms in improving the value of total users' QoE and reducing the MTP delay.

Dynamic Computation Offloading and Server Deployment for UAV-Enabled Multi-Access Edge Computing

Driven by the increasing demand of real-time mobile application processing, Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges. In this paper, we investigate an MEC network enabled by Unmanned Aerial Vehicles (UAV), and consider both the multi-user computation offloading and edge server deployment to minimize the system-wide computation cost under dynamic environment, where users generate tasks according to time-varying probabilities. We decompose the minimization problem by formulating two stochastic games for multi-user computation offloading and edge server deployment respectively, and prove that each formulated stochastic game has at least one Nash Equilibrium (NE). Two learning algorithms are proposed to reach the NEs with polynomial-time computational complexities. We further incorporate these two algorithms into a chess-like asynchronous updating algorithm to solve the system-wide computation cost minimization problem. Finally, performance evaluations based on real-world data are conducted and analyzed, corroborating that the proposed algorithms can achieve efficient computation offloading coupled with proper server deployment under dynamic environment for multiple users and MEC servers.

UxV-Based Deep-Learning-Integrated Automated and Secure Garbage Management Scheme Using Blockchain

This paper presents a deep learning (DL) model integrated automated and secure garbage management scheme using unmanned any vehicle (UxV) to minimize the human effort in terms of the traditional garbage management system. Different kinds of UxV (unmanned aerial vehicles, automated guided vehicles, unmanned surface vehicles, unmanned underwater vehicles, etc.) are utilized to establish an automated garbage management scheme to collect and place the garbage both from the ground and sea surfaces. However, due to the limited battery capacity and inadequate resources of different UxV, a lightweight DL model is developed to detect the garbage successfully with a higher accuracy rate. The proposed lightweight DL model uses two activation functions named MISH and rectified linear unit to enhance the feature extraction and detect the garbage. Moreover, a multi-access edge computing (MEC) server is allocated in the proposed scheme to improve the quality of service (QoS) (i.e., reduce latency and improve security). Furthermore, a blockchain-based secure hazardous garbage (e.g., infectious, toxic, or radioactive materials) tracking technique is concluded in this scheme to identify the individual and reduce the potential harm to the environment. Experimental results demonstrate that the UxV can successfully detect the garbage using the proposed lightweight DL model within a minimum time frame and the obtained accuracy is higher than the other existing DL models. Besides, QoS has been investigated to verify the efficacy of the proposed scheme. Finally, a private blockchain network is established to demonstrate the performance of the proposed hazardous garbage tracking technique.

Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN

To satisfy the continuously high energy consumption and high computational capacity requirements for IoT applications, such as video monitoring, we integrate solar harvesting and multi-access edge computing (MEC) technologies to develop a solar-powered MEC system. Considering the stochastic nature of solar arrivals and channel conditions, we formulate a stochastic optimization problem to maximize network energy efficiency under the constraints of energy queue stability, task queue stability, peak transmission power, and maximum CPU frequency of each sensor. To solve the long-term stochastic optimization problem, we propose a Lyapunov-based online joint computational offloading and resource scheduling optimization algorithm, transforming the long-term stochastic problem into a series of deterministic subproblems in each time slot. Simulation results show that the proposed algorithm can find the optimal solution to tradeoff long-term energy efficiency and queueing backlog without requiring a priori knowledge of the channel state and energy arrival, which is a more realistic solution for practical solar-powered MEC systems.

Multi-Access Edge Computing (MEC) Based on MIMO: A Survey.

With the rapid development of wireless communication technology and the emergence of intelligent applications, higher requirements have been put forward for data communication and computing capacity. Multi-access edge computing (MEC) can handle highly demanding applications by users by sinking the services and computing capabilities of the cloud to the edge of the cell. Meanwhile, the multiple input multiple output (MIMO) technology based on large-scale antenna arrays can achieve an order-of-magnitude improvement in system capacity. The introduction of MIMO into MEC takes full advantage of the energy and spectral efficiency of MIMO technology, providing a new computing paradigm for time-sensitive applications. In parallel, it can accommodate more users and cope with the inevitable trend of continuous data traffic explosion. In this paper, the state-of-the-art research status in this field is investigated, summarized and analyzed. Specifically, we first summarize a multi-base station cooperative mMIMO-MEC model that can easily be expanded to adapt to different MIMO-MEC application scenarios. Subsequently, we comprehensively analyze the current works, compare them to each other and summarize them, mainly from four aspects: research scenarios, application scenarios, evaluation indicators and research issues, and research algorithms. Finally, some open research challenges are identified and discussed, and these indicate the direction for future research on MIMO-MEC.

Content-aware QoE optimization in MEC-assisted Mobile video streaming

The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%–12.3%, 8%–65%, 3.3%–5.7%, and 60%–130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm.

Towards UAV-Based MEC Service Chain Resilience Evaluation: A Quantitative Modeling Approach

Unmanned aerial vehicle (UAV) and network function virtualization (NFV) facilitate the deployment of multi-access edge computing (MEC). In the UAV-based MEC (UMEC) network, virtualized network function (VNF) can be implemented as a lightweight container running on UMEC host operating system (OS). However, UMEC network is vulnerable to attack, which can result in resource degradation and even UMEC service disruption. Rejuvenation techniques, such as failover technique and live container migration technique, can mitigate the impact of resource degradation but their effectiveness to improve the resilience of UMEC services should be evaluated. This paper presents a quantitative modeling approach based on semi-Markov process to investigate the resilience of a UMEC service chain consisting of any number of VNFs executed in any number of UMEC hosts in terms of availability and reliability. Unlike existing studies, the semi-Markov model constructed in this paper can capture the time-dependent behaviors between VNFs, between host OSes, and between VNFs and host OSes on the condition that the holding times of the recovery and failure events follow any kind of distribution. We perform the sensitivity analysis to identify potential resilience bottlenecks. The results highlight that migration time is the parameter significantly affecting the resilience, which shed the insight on designing the UMEC service chain with high-grade resilience requirements. In addition, we carry out the numerical experiments to reveal that: (i) the type of failure time distribution has a significant effect on the resilience; and (ii) the resilience increases with decreasing number of VNFs, while the availability increases with increasing number of UMEC hosts and the reliability decreases with increasing number of UMEC hosts, which can provide meaningful guidance for the UAV placement optimization in the UMEC network.

Radio and Computing Resource Allocation in Co-Located Edge Computing: A Generalized Nash Equilibrium Model

Mobile Network Operators (MNO) can reduce their Capital and Operational Expenditure (CAPEX) and (OPEX) with the help of tower sharing approach by utilizing the physical infrastructure equipped by a third party tower provider to expand their network coverage. Moreover, Computing Resource Providers (CRP) are also setting up their micro-datacenters at tower stations to provide the Multi-access Edge Computing (MEC) services by cooperating with tower providers. Since both the communication and computing services contribute to the task offloading in MEC, the resource allocation has become a challenging problem. In this paper, we formulate the joint uplink, downlink, and computing resources allocation problem in which the objectives of both MNOs and CRP are to minimize their OPEX. The task offloading is modeled as a network of queues where the end-to-end latency is calculated based on the performance of the queue network. Then, the formulated problem is transformed into a Generalized Nash Equilibrium Problem (GNEP) to capture the conflicting interests in the resource allocation among MNOs and CRP. To solve the formulated GNEP efficiently, two decentralized algorithms are proposed by introducing the penalty parameters to the coupling constraints. In addition, the convergence and performance of the algorithms on different parameters are analyzed.

APP-Aware MAC Scheduling for MEC-Backed TDM-PON Mobile Fronthaul

In the 5G era and beyond, new applications (APPs) have more stringent requirements for low latency. The cloud radio access network (C-RAN) with TDM-PON-based mobile fronthaul (MFH) and multi-access edge computing (MEC) has been regarded as an efficient architecture for serving latency-critical APPs. Many studies have focused on latency optimization issues in the MFH or MEC segment. However, the ultimate latency from the end-user’s perspective is end-to-end (E2E) latency, which includes the latency in both the MFH and MEC segments. When coordinating MFH with MEC, the E2E latency optimization may be mismatched and inefficient. To address this issue, we propose an APP-aware MAC scheduling scheme that allocates uplink time resources based on the MEC-backed conditions and APPs’ traffic dynamics. Accordingly, we introduce an AI-based traffic prediction approach to capture traffic fluctuations in various APPs. Simulations show that our proposed scheme improves the performances of latency violation ratio and average E2E latency by up to 30% and 8%, respectively.

Privacy-Aware Laser Wireless Power Transfer for Aerial Multi-Access Edge Computing: A Colonel Blotto Game Approach

This article studies the integration of laser-beamed wireless power transfer (WPT) into high-altitude platform (HAP)-aided multiaccess edge computing (MEC) systems for the HAP-connected aerial user equipments (AUEs). By discretizing the 3-D coverage space of the HAP, we present a multitier tile grid-based spatial structure to provide the aerial locations in the form of tile grids to AUEs for laser charging. We identify a new privacy vulnerability caused by the openness during the WPT signaling transfer in the presence of a terrestrial adversary, which is able to launch the attacks by distributing the false tile grids to the AUEs. To address this vulnerability and enhance the location privacy of AUEs, we then propose a Colonel Blotto (CB) game framework to formulate the competitive tile grid allocation problem for the HAP and the adversary. The attack-defense interaction between the adversary and the HAP as a defender in their tile grid allocations to the AUEs is formulated as a CB game, which models the competition of two players for limited resources over multiple battlefields. Moreover, we derive the mixed-strategy Nash equilibria of the game for both symmetric and asymmetric tile grids between two players. Simulation results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.

New Improved Multi-Objective Gorilla Troops Algorithm for Dependent Tasks Offloading problem in Multi-Access Edge Computing

Computational offloading allows lightweight battery-operated devices such as IoT gadgets and mobile equipment to send computation tasks to nearby edge servers to be completed, which is a challenging problem in the multi-access edge computing (MEC) environment. Numerous conflicting objectives exist in this problem; for example, the execution time, energy consumption, and computation cost should all be optimized simultaneously. Furthermore, offloading an application that consists of dependent tasks is another important issue that cannot be neglected while addressing this problem. Recent methods are single objective, computationally expensive, or ignore task dependency. As a result, we propose an improved Gorilla Troops Algorithm (IGTA) to offload dependent tasks in the MEC environments with three objectives: 1-Minimizing the execution latency of the application, 2-energy consumption of the light devices, 3-the used cost of the MEC resources. Furthermore, it is supposed that each MEC supports many charge levels to provide more flexibility to the system. Additionally, we have extended the operation of the standard Gorilla Troops Algorithm (GTO) by adopting a customized crossover operation to improve its search strategy. A Max-To-Min (MTM) load-balancing strategy was also implemented in IGTA to improve the offloading operation. Relative to GTO, IGTA has reduced latency by 33%, energy consumption by 93%, and cost usage by 34.5%. We compared IGTA with other Optimizers in this problem, and the results showed the superiority of IGTA.

Services Management and Distributed Multihop Requests Routing in Mobile Edge Networks

Multi-access Edge Computing (MEC) is an emerging computing architecture to release the resource burden of the centralized cloud and reduce the mobile application latency. Services management and MEC requests routing is a major problem in MEC systems. Existing works mainly focus on the one-hop centralized request routing strategies. However, the centralized one-hop routing method is not suitable enough since the MEC network is a distributed system, and the number of MEC requests increases dramatically. In this paper, we have proposed an online problem. In such problem, we jointly consider the mobile edge service management and the distributed multi-hop requests routing in an MEC network in which the MEC requests randomly generate. We prove that such problem is NP-Hard even in the off-line scenario. Furthermore, we propose an approximation algorithm to manage the MEC services and two distributed online algorithms to route MEC requests. The approximation ratio and competitive ratio of these algorithms have been analyzed. Experiments are carried out to evaluate the performance of the algorithms and simulation results imply that these algorithms are effective and efficient.

Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs

With the explosive increment of computation requirements, the multiaccess edge computing (MEC) paradigm appears as an effective mechanism. Besides, as for the Internet of Things (IoT) in disasters or remote areas requiring MEC services, unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) are available to provide aerial computing services for these IoT devices. In this article, we develop the hierarchical aerial computing framework composed of HAPs and UAVs, to provide MEC services for various IoT applications. In particular, the problem is formulated to maximize the total IoT data computed by the aerial MEC platforms, restricted by the delay requirement of IoT and multiple resource constraints of UAVs and HAPs, which is an integer programming problem and intractable to solve. Due to the prohibitive complexity of the exhaustive search, we handle the problem by presenting the matching game theory-based algorithm to deal with the offloading decisions from IoT devices to UAVs, as well as a heuristic algorithm for the offloading decisions between UAVs and HAPs. The external effect affected by the interplay of different IoT devices in the matching is tackled by the externality elimination mechanism. Besides, an adjustment algorithm is also proposed to make the best of aerial resources. The complexity of proposed algorithms is analyzed and extensive simulation results verify the efficiency of the proposed algorithms, and the system performances are also analyzed by the numerical results.

International revenue share fraud prediction on the 5G edge using federated learning

Edge computing and multi-access edge computing (MEC) are two recent paradigms of distributed computing that are growing due to the rise of the fifth-generation (5G) of broadband cellular networks. The development of edge computing and MEC architectures involves the hosting of applications close to the end-users, allowing: an improved privacy, given that critical data is not shared with other systems; a reduced communication latency; an improved application speed; and a more efficient energy use. However, many applications are challenged by edge computing and MEC. In the case of machine learning (ML) applications, there can be privacy rules that do not allow data to be shared among distinct edges. Additionally, the devices used to train ML models might present lower computational capabilities than traditional computers. In this work, we present a Federated ML architecture that uses decentralized data and light ML training techniques to fit ML models on the 5G Edge. Our system consists of edge nodes that train models using local data and a centralized node that aggregates the results. As a case study, an international revenue share fraud task is addressed by considering two real-world datasets obtained from a commercial provider of Telecom analytics solutions. We test our architecture using two iterations of a Federated ML method, then compare it with a centralized ML model that is currently adopted by the provider. The results show that the Federated Learning decentralized approach produces an excellent level of class discrimination and that the main models maintain the performance across two rounds of decentralized training and even surpass the existing centralized model. After validating the results with the Telecom provider, we have built a prototype technological architecture that can be deployed in a real-world MEC scenario.

Cloud service selection in IoFT-enabled Multi-access Edge Computing: a Game Theoretic approach

Cloud service selection in IoFT-enabled Multi-access Edge Computing: a Game Theoretic approach