关键词： Bidding auctions   Mobile edge computing   Resource allocation   Social welfare maximization   Vickrey-Clarke-Groves  

摘要： 5G NR-Lite technology still suffers from inadequate response to low latency, high speed and mass connectivity scenarios in high mobility conditions. Building on 4G and 5G, 6G networks will work to more fully enable the Internet of Things (IoT) paradigm, working to provide secure wireless computing for the digital world. In order to ensure the resource utilization of edge computing, it is usually necessary to allocate resources among mobile devices. Many scholars have applied auction theory to resource allocation in mobile edge networks and achieved specific results in recent years. However, most existing studies unilaterally consider the utility of users or edge servers, and few jointly consider social welfare and user selection priorities, which cannot maximize the overall utility of mobile edge networks. This paper proposes a multi-round auction algorithm that pursues maximizing social welfare in a multi-user-multi-server network. The algorithm combines combinatorial auction with double auction, allowing users and servers to adjust their bids and asking prices in each auction round. To enhance the generalizability of the study, the algorithm takes full advantage of the flexibility of the auction mechanism and considers the users' demand for different servers, the variability of bidding strategies, and the variability of the total resources of edge servers so that the users can obtain resources with higher priority and improve social welfare. To ensure that social welfare is maximized, we adopt the Vickrey-Clarke-Groves (VCG) payment mechanism and consider the effect of network externalities. Extensive comparative experiments show that the algorithm can effectively improve social welfare and maximize the overall benefits of the mobile edge network.(c) 2022 Elsevier B.V. All rights reserved.

关键词： mobile edge computing   proactive self-healing   autonomous computing   fault tolerance   cyber security  

摘要： The widespread use of technology has made communication technology an indispensable part of daily life. However, the present cloud infrastructure is insufficient to meet the industry's growing demands, and multi-access edge computing (MEC) has emerged as a solution by providing real-time computation closer to the data source. Effective management of MEC is essential for providing high-quality services, and proactive self-healing is a promising approach that anticipates and executes remedial operations before faults occur. This paper aims to identify, evaluate, and synthesize studies related to proactive self-healing approaches in MEC environments. The authors conducted a systematic literature review (SLR) using four well-known digital libraries (IEEE Xplore, Web of Science, ProQuest, and Scopus) and one academic search engine (Google Scholar). The review retrieved 920 papers, and 116 primary studies were selected for in-depth analysis. The SLR results are categorized into edge resource management methods and self-healing methods and approaches in MEC. The paper highlights the challenges and open issues in MEC, such as offloading task decisions, resource allocation, and security issues, such as infrastructure and cyber attacks. Finally, the paper suggests future work based on the SLR findings.

关键词： neural network model   indoor autonomous driving   LiDAR sensor   resource usage   mobile edge computing  

摘要： Mobile edge computing has been proposed as a solution for solving the latency problem of traditional cloud computing. In particular, mobile edge computing is needed in areas such as autonomous driving, which requires large amounts of data to be processed without latency for safety. Indoor autonomous driving is attracting attention as one of the mobile edge computing services. Furthermore, it relies on its sensors for location recognition because indoor autonomous driving cannot use a GPS device, as is the case with outdoor driving. However, while the autonomous vehicle is being driven, the real-time processing of external events and the correction of errors are required for safety. Furthermore, an efficient autonomous driving system is required because it is a mobile environment with resource constraints. This study proposes neural network models as a machine-learning method for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the range data measured with the LiDAR sensor. We designed six neural network models to be evaluated according to the number of input data points. In addition, we made an autonomous vehicle based on the Raspberry Pi for driving and learning and an indoor circular driving track for collecting data and performance evaluation. Finally, we evaluated six neural network models in terms of confusion matrix, response time, battery consumption, and driving command accuracy. In addition, when neural network learning was applied, the effect of the number of inputs was confirmed in the usage of resources. The result will influence the choice of an appropriate neural network model for an indoor autonomous vehicle.

关键词： Software-defined networking   mobile edge computing   5G   approximation algorithm  

摘要： The software-defined network (SDN) enabled mobile edge network greatly facilitates network resource management and promotes many emerging applications. However, user mobility may cause the SDN controller to set flow rules frequently, introduce additional flow setup latency, cause delay jitter, and undermine latency-sensitive services. Proactive flow setup is an effective way to eliminate flow setup latency, but existing work fails to maximize the flow setup hit ratio, a metric for evaluating the quality of proactive flow setup decisions, which is critical for latency-sensitive services. In this paper, we study how to proactively set flow rules to maximize the flow setup hit ratio under limited available network resources to eliminate the flow setup latency as much as possible. Then, we formalize the proactive flow setup problem as two integer linear programming problems under two typical routing strategies, default routing and dynamic routing. Both problems are proved to be NP-hard. To tackle these two problems, we propose a linear programmingbased polynomial-time approximation algorithm for the default routing case and a greedy-based heuristic algorithm for the dynamic routing case. Extensive trace-driven experimental and simulation results verify that our algorithms can improve the flow setup hit ratio by up to 30.99% compared to existing solutions.

关键词： Mobile edge computing   Multi-user service migration   Deep reinforcement learning   Partially observable Markov decision processes  

摘要： With the continuous development of mobile edge computing, people are more willing to offload tasks to edge servers that are closer to users than cloud services for a better user experience. Due to problems such as user mobility and limited coverage of edge servers, how to ensure service quality and prevent service interruption is a problem we have to consider. This article explores the problem of service migration to decide when, where, and how to migrate an ongoing service from the current edge server to the target edge server. At the same time, we will consider the premise that edge servers can only obtain partial user information, and model the service migration problem as a partially observable Markov decision process. To minimize user delay and system energy consumption, we propose a service migration decision algorithm based on Deep recurrent Q-learning (DRQNSM). Multiple experiments show that our algorithm has a better performance than some classical reinforcement learning algorithms.

关键词： Mobile edge computing   Offloading decisions   Deep reinforcement learning   Dimension explosion   Clustering algorithm  

摘要： In many IoT scenarios, the resources of terminal devices are limited, and it is difficult to provide services with low latency and low energy consumption. Mobile edge computing is an effective solution by offloading computing tasks to edge server processing. There are some problems in the existing offloading decision algorithms: the offloading decision method based on heuristic algorithms cannot dynamically adjust the policy in the changing environment;the offloading algorithm based on deep reinforcement learning will lead to slow convergence and poor exploration effect due to the problem of dimension explosion. To solve the above problems, this paper designs an offloading decision algorithm to make dynamic decisions in a mobile edge computing network with multi-device access. The algorithm comprehensively considers the energy consumption of terminal equipment, offloading overhead, average delay and success rate of task completion, aiming to achieve the highest total revenue of the whole system in a period of time. In this work, the online offloading problem is abstracted as a Markov decision process. Based on the Double Dueling Deep Q-Network (D3QN) algorithm, the offloading decision is designed to adapt to the highly dynamic environment of the edge computing network and solve the problem of high state space complexity. In addition, this paper innovatively introduces a clustering algorithm into deep reinforcement learning (DRL) to preprocess the action space and solve the explosion problem of the action space dimension caused by the increase of terminal devices. The experimental results show that the proposed algorithm is superior to the baseline strategies such as Deep Q-Network (DQN) algorithm in convergence speed and total reward.

关键词： Multi-access edge computing   Downlink   Servers   Uplink   Resource management   Task analysis   Computational modeling   Heterogeneous cellular networks (HCNs)   mobile device (MD) association   mobile edge caching   mobile edge computing   resource allocation  

摘要： To meet the need of computation-sensitive (CS) and high-rate (HR) communications, the mobile edge computing and caching framework has been widely regarded as a promising solution. When such a framework is implemented in heterogeneous cellular networks (HCNs), it is a key and open topic how to assign mobile edge computing and caching servers to mobile devices (MDs) with CS and HR communications. In this article, under the resource partitioning and resource constraints, joint MD association and resource allocation is performed to minimize the total delay of MDs. In such a formulated problem, the MD association and resource allocation parameters are in a coupling form. In addition, it is also in nonlinear mixed-integer one and hard to tackle. To solve it, we try to develop an alternating optimization algorithm according to the coalitional game and convex optimization theorems. As for the designed algorithm, we provide some detailed convergence, stability, parallel implement, and computation complexity analyses. At last, in the simulation, we introduce other association algorithms for comparison, and investigate the impacts of MD density, time, and frequency partitioning factors on the delay of MDs. Simulation results show that the designed algorithm can achieve lower network delay than existing algorithms in general.

关键词： Computation offloading   Downlink and uplink decoupling   Heterogeneous networks   Mobile edge computing   Resource allocation  

摘要： Mobile edge computing (MEC) is a key player in low latency 5G networks with the task to resolve the conflict between computationally-intensive mobile applications and resource-limited mobile devices (MDs). As such, there has been intense interest in this topic, especially in multi-user single-server and homogeneous multi-server scenarios. However, the research in the heterogeneous multi-server scenario is limited, where the servers are located at small base-stations (SBSs), macro base-stations (MBSs), or the cloud with different computing and communication capabilities. On the other hand, computational-tasks offloading is limited by the type of MD-BS association with almost all previous works focusing on offloading the MD's computational tasks to the MEC servers/cloudlets at its serving BS. However, in multi-BS association, or downlink/uplink decoupled (DUDe) scenarios, an MD can be served by multiple BSs and hence has multiple offloading choices. Motivated by this, we proposed a joint BS association and subchannel allocation algorithm based on a student-project allocation (SPA) matching approach to minimize the network sum-latency, which breaks the constraint that one MD must connect to the same BS in the UL and DL, and jointly consider the communication and computational disparity of SBS and MBS cloudlets in heterogeneous MEC networks. Moreover, an optimal power allocation scheme is proposed to optimize the system performance subject to the predefined quality of service constraints. Our results show that the proposed scheme is superior to benchmark techniques in enabling the effective use of computational and communication resources in heterogeneous MEC networks.

关键词： Mobile edge computing   Non-orthogonal multiple access   Successful computation probability   Genetic algorithm   Particle swarm optimization   Hybrid GA PSO  

摘要： Recent years have witnessed the computing process gradually move to the edge network, close to the physical data source, to serve applications that require large computations with very little latency. However, the termi-nal wireless devices' limited computing and energy resources pose obstacles to the practical implementation of these applications. Mobile Edge Computing (MEC) based non-orthogonal multiple access (NOMA) technology is solving this problem well thanks to its ability to serve many users with high data rates and spectrum utilization efficiency. This study investigates the performance and optimization of MEC surveillance systems using NOMA. Specifically, two camera units (CUs) perform the monitoring task to be accomplished by the MEC access point (AP) through Rayleigh fading wireless links. We then proposed the four-phase protocol for this system. Accordingly, we derive the closed-form exact expressions of the successful computation probability (SCP) and study the impact of the network parameters on the system performance. Furthermore, we propose and compare three meta-heuristic-based algorithms, namely MSCP-GA, MSCP-PSO, and MSCP-HGAPSO, to find the optimal parameters set to help the proposed system achieve the maximum SCP. The results show that the proposed algorithms can significantly improve the system's performance by 40% higher than when the optimal algorithm is not used. Insights into the pros and cons of different algorithms are also discussed in this study. Finally, we use the Monte-Carlo simulation to verify the correctness of this study.

关键词： mobile edge computing   unmanned aerial vehicle   task migration   deep reinforcement learning   federated learning  

摘要： At present, with the intelligence that has been achieved in computer and communication technologies, vehicles can provide many convenient functions to users. However, it is difficult for a vehicle to deal with computationally intensive and latency-sensitive tasks occurring in the vehicle environment by itself. To this end, mobile edge computing (MEC) services have emerged. However, MEC servers (MECSs), which are fixed on the ground, cannot flexibly respond to temporal dynamics where tasks are temporarily increasing, such as commuting time. Therefore, research has examined the provision of edge services using additional unmanned aerial vehicles (UAV) with mobility. Since these UAVs have limited energy and computing power, it is more important to optimize energy efficiency through load balancing than it is for ground MEC servers (MECSs). Moreover, if only certain servers run out of energy, the service coverage of a MEC server (MECS) may be limited. Therefore, all UAV MEC servers (UAV MECSs) need to use energy evenly. Further, in a high-mobility vehicle environment, it is necessary to have effective task migration because the UAV MECS that provides services to the vehicle changes rapidly. Therefore, in this paper, a federated deep Q-network (DQN)-based task migration strategy that considers the load deviation and energy deviation among UAV MECSs is proposed. DQN is used to create a local model for migration optimization for each of the UAV MECSs, and federated learning creates a more effective global model based on the fact that it has common spatial features between adjacent regions. To evaluate the performance of the proposed strategy, the performance is analyzed in terms of delay constraint satisfaction, load deviation, and energy deviation.