关键词： Training   Servers   Processor scheduling   Convergence   Scheduling   Optimization   Energy consumption   Stability analysis   Correlation   System performance   Mobile edge computing   task scheduling   low latency   energy consumption   deep deterministic policy gradient  

摘要： The Mobile Edge Computing (MEC) system is crucial in modern networks, particularly for applications requiring low latency and high energy efficiency. Efficient task scheduling in MEC environments to balance these requirements is challenging. This paper presented a method that improves the Deep Deterministic Policy Gradient (DDPG) algorithm for better performance in MEC systems. Stability is enhanced through a soft update mechanism for target network parameters and an experience replay buffer to store past experiences, reducing sample correlation and improving training efficiency. It also proposed a reward function considering both latency and energy consumption, comparing the effects of linear and nonlinear reward functions on system performance. The task scheduling process includes state representation, action selection, environment feedback, network updates, and target network soft updates. The experimental results indicate that by analyzing the performance of different reward functions, a nonlinear reward function has been determined to achieve faster convergence. The improved DDPG algorithm outperforms traditional task scheduling algorithms in terms of latency and energy consumption under different task loads, and also has significant advantages in robustness. The ablation experiment further verified the positive contribution of each improved part to the overall performance. At Task load 10, the latency of DDPG, DDPG+Soft update, DDPG+Experience reply buffer, and this paper model were 267 ms, 263 ms, 260 ms, and 250 ms, respectively, with energy consumption of 988J, 974 J, 969 J, and 965 J. The improved DDPG algorithm in this paper not only effectively solves the task scheduling problem in MEC systems, but also provides important references for future research.

关键词： Internet of things   Mobile edge computing   Quality of service   Quality of service evaluation   Quality of service optimization  

摘要： With the evolvement of the Internet of things(IoT), mobile edge computing(MEC) has emerged as a promising computing paradigm to support IoT data analysis and processing. In MEC for IoT, the differentiated requirements on quality of service(QoS) have been growing rapidly, making QoS a multi-dimensional concept including several attributes, such as performance, dependability, energy efficiency, and economic factors. To guarantee the QoS of IoT applications, theories and techniques of multi-dimensional QoS evaluation and optimization have become important theoretical foundations and supporting technologies for the research and application of MEC for IoT,which have attracted significant attention from both academia and industry. This paper aims to survey the existing studies on multi-dimensional QoS evaluation and optimization of MEC for IoT, and provide insights and guidance for future research in this field. This paper summarizes the multi-dimensional and multi-attribute QoS metrics in Io T scenarios, and then several QoS evaluation methods are presented. For QoS optimization, the main research problems in this field are summarized, and optimization models as well as their corresponding solutions are elaborated. We take notice of the booming of edge intelligence in artificial intelligence-empowered Io T scenarios, and illustrate the new research topics and the state-of-the-art approaches related to QoS evaluation and optimization. We discuss the challenges and future research directions.

关键词： Task analysis   Resource management   Cloud computing   Servers   Computational modeling   Optimization   Mobile handsets   Multi-access edge computing   Mobile edge computing   task offloading   resource allocation   multi-objective optimization   estimation-of-distribution model  

摘要： With the rapid growth of the mobile computing techniques, a wide variety of mobile edge computing (MEC) applications have emerged recently, aiming to provide computationally intensive and delay-sensitive network services. Through MEC, various complex tasks of mobile devices can be offloaded to the edge of network system for execution by edge servers, which greatly reduces the local computing burden. However, how to effectively allocate computational and communication resources in edge-cloud remains a challenging task, especially when multiple mobile users and edge servers are involved. In this paper, we propose a decomposition-based multi-objective optimization algorithm based on estimation-of-distribution models (MOEA/D-EoD) to deal with the task offloading and resource allocation problem in MEC. Especially, considering the features of multi-user and multi-server cloud-edge-end collaboration wireless MEC system, we construct a joint optimization model of task offloading and resource allocation, where limited communication and computational resource constraints are considered. To deal with the optimization model, we design an efficient decomposition-based algorithm, which incorporates two novel estimation-of-distribution models to deal with discrete and continuous decision variables of the problem. Experimental results obtained from benchmark test suites DTLZ and ZDT demonstrate that the proposed method exhibits significantly superior performance compared to other comparative algorithms. The proposed model and algorithm are simulated and tested on different test instances, and experimental results show the effectiveness and efficiency of our proposed method.

关键词： Reconfigurable intelligence surface   mobile edge computing   spectral efficiency   spectral efficiency   energy efficiency   energy efficiency   penalty based approach   penalty based approach   semidefinite programming   semidefinite programming   semidefinite programming  

摘要： Mobile edge computing (MEC) is emerging as a critical technology for supporting latency-sensitive and computation-intensive services-however, random wireless channel fading limits offloading rates, posing a significant challenge to MEC performance. In MEC systems, effective energy management and high-speed communication links between user devices and MEC servers are essential for supporting services that require low latency and high computation power. Reconfigurable intelligent surfaces (RIS) have been proposed as a promising solution to enhance the quality of communication links between users and MEC servers by dynamically reconfiguring the wireless propagation environment to overcome these challenges. We formulate a trade-off optimization problem to balance SE and EE in RIS-aided MEC systems, which is crucial due to limited system resources and the need for dynamic adaptation to varying network requirements-aimed at joint optimization of transmission power, phase-shift matrix, and MEC offloading and computation delays. Given the problem's intractability, we develop an alternating optimization-based iterative algorithm incorporating quadratic transformation and successive convex approximation techniques to obtain sub-optimal solutions. Firstly, we address the minimum delay power allocation and task offloading by using quadratic transformations for fractional problems and closed-form solutions. Afterward, we optimize the phase shifts through semidefinite programming and a penalty-based approach. Simulation results validate the effectiveness of the proposed framework, demonstrating significant improvements in SE and EE compared to conventional systems without RIS or with static RIS configurations.

关键词： mobile edge computing   MEC   processor sharing server   ultra low latency communication augmented reality  

摘要： In wireless communication, ultra low-latency communications (ULLC) services offer short packets that may coincide alongside enhanced mobile broadband (eMBB) services which send lengthy packets. In a mobile edge computing architecture with eMBB and URLLC services, we explore how to evaluate latency and enhance shorter packet offloading methods. Using eMBB and URLLC functions, we examine how to evaluate latency and enhance shorter packet offloading methods in mobile edge computing (MEC) approach. In the MEC system, a server called processor sharing (PS) is utilised to reduce computation delay for short packets by distributing the server's whole processing power equally to all packets. The server ignores long packets in favour of shorter ones. With a small packet size, a closed-form formula for the complementary latency distribution function may be developed. To minimise the short packet's end-to-end (E2E) latency, offloading probabilities are adjusted based on the dependable demand. In tests with short and long packets, the processor-sharing server outperforms two types of first-come, first-serve servers.

关键词： Mobile edge computing  

摘要： Reality digital twin (DT) model needs to be updated periodically, then the physical entity can be maintained efficiently. Since some physical entities may not be able to upload the entire status information actively, and also considering the universality of practical applications, we propose to use unmanned ground vehicle (UGV) as an information collector to assist in updating the reality DTs, where the UGV iterates over each target point to gather information by on-board sensors. Furthermore, considering the large amount of updating data and the limited computing resources on the UGV, we leverage mobile edge computing (MEC) technology to collaboratively process the data. In addition, to quantify the freshness of DTs, we propose the concept age of digital twins (AoDT) as a metric to quantify the freshness of DT model. Thus, an AoDT minimization problem is established, which jointly optimizes offloading decisions, UGV waypoints selections and target points' visiting orders, while also taking the obstacle avoidance into account. Considering the difficulty of the problem, we propose a novel low-complexity iterative algorithm to solve it. During which, a modified traveling salesman problem (TSP) solution is also proposed by taking into consideration the additional distance required to bypass the obstacles on each inter-waypoints path. Finally, extensive simulation results show that the proposed algorithm can effectively reduce the AoDT, comparing to the benchmark algorithms. © 2014 IEEE.

关键词： Robots   Microservice architectures   Task analysis   Cloud computing   Millimeter wave communication   5G mobile communication   Image edge detection   Containerization   ground robot   millimeter wave   mobile edge computing   mobile edge learning   object detection   orchestration   smart city   software defined networking  

摘要： The seamless integration of multiple radio access technologies (multi-RAT) and cloud/edge resources is pivotal for advancing future networks, which seek to unify distributed and heterogeneous computing and communication resources into a cohesive continuum system, tailored for mobile applications. Many research projects and focused studies are proposing solutions in this area, the impact of which is undoubtedly increased by moving from theoretical and simulation studies to experimental validations. To this aim, this paper proposes a testbed architecture that combines contemporary communication and cloud technologies to provide microservice-based mobile applications with the ability to offload part of their tasks to cloud/edge data centers connected by multi-RAT cellular networks. The testbed leverages Kubernetes, Istio service mesh, OpenFlow, public 5G networks, and IEEE 802.11ad mmWave (60 GHz) Wi-Fi access points. The architecture is validated through a use case in which a ground robot autonomously follows a moving object by using an artificial intelligence-driven computer vision application. Computationally intensive navigation tasks are offloaded by the robot to microservice instances, which are executed on demand within cloud and edge data centers that the robot can exploit during its journey. The proposed testbed is flexible and can be reused to assess communication and cloud innovations focusing on multi-RAT cloud continuum scenarios.

关键词： Unmanned aerial vehicles   High-altitude balloons   Mobile edge computing   Task splitting   Block coordinate descent  

摘要： Since the computing capacity and battery energy of unmanned aerial vehicle(UAV) are constrained,UAV as aerial user is hard to handle the high computational complexity and time-sensitive *** paper investigates a cellular-connected multi-UAV network supported by mobile edge *** UAVs carrying tasks fly from a given initial position to a termination position within a specified *** handle the large number of tasks carried by UAVs,we propose a energy cost of all UAVs based problem to determine how many tasks should be offloaded to high-altitude balloons(HABs) for computing,where UAV-HAB association,the trajectory of UAV,and calculation task splitting are jointly ***,the formulated problem has nonconvex ***,an efficient iterative algorithm by applying successive convex approximation and the block coordinate descent methods is put ***,in each iteration,the UAV-HAB association,calculation task splitting,and UAV trajectory are alternately ***,for the nonconvex UAV trajectory optimization problem,an approximate convex optimization problem is *** numerical results indicate that the scheme of this paper proposed is guaranteed to converge and also significantly reduces the entire power consumption of all UAVs compared to the benchmark schemes.

关键词： Internet of Vehicles   Delays   Servers   Routing   Handover   Information-centric networking   Smart cities   Edge-based content provisioning   information-centric networking   mobile edge computing   point of attachment  

摘要： Over the years, the Internet of Vehicles (IoV) and Information-Centric Networking (ICN) has gained significant popularity due to emergence of content provisioning in smart environments. The content provisioning in mobile and seamless environments is challenging due to frequent change in Point of Attachement (PoA) or handovers across the networks. In Edge supported IoV based ICN, the consumer's interest packets are not satisfied until the producer re-connects to the new PoA and informs its current location to the next content router. The idea is to satisfy the interest packets by providing the requested data content to the consumer node at its current PoA. The existing ICN architectures without edge support within the zone do not handle and support mobility aware content provisioning to the consumers in the IoV environment. In this paper, we propose Mobility-Aware Content Provisioning in Edge-based content-centric Internet of Vehicles (MACPE). That manages the network in zones with content routers for up-to-date content and producer mobility information. The simulation results show that the proposed MACPE scheme outperforms GeoZone scheme in terms of benchmark parameters. i.e. packet delivery ratio, average end-to-end delay, handover delay, average hop count and throughput during producer mobility scenarios in intra and inter-edge-based zones.

关键词： Convolutional neural network   cyber physical system   firefly algorithm   intelligent transportation system   mobile edge computing  

摘要： In the present scenario, the transportation Cyber Physical System (CPS) improves the reliability and efficiency of the transportation systems by enhancing the interactions between the physical and cyber systems. With the provision of better storage ability and enhanced computing, cloud computing extends transportation CPS in Mobile Edge Computing (MEC). By inspecting the existing literatures, the cloud computing cannot fulfill the requirements in transportation CPS like lower context-awareness and latency. For enhancing the context-awareness and reducing the latency in a realistic MEC environment, an efficient portable deep learning model: Convolutional Neural Network (CNN) with Chaotic Levy Flight based Firefly Algorithm (CLFFA) is implemented in this article. In the CNN model, the CLFFA selects the appropriate hyper-parameters or reduces the redundant parameters that results in minimal model size and inference latency than the traditional CNN models. Additionally, the CNN-CLFFA model significantly outperformed the existing models by means of recall, accuracy, F1-score, and precision on the benchmark datasets like German Traffic Sign Recognition Benchmark (GTSRB), MIOvision Traffic Camera Dataset (MIO-TCD) classification, and VCifar-100 datasets. The numerical analysis demonstrates that the CNN-CLFFA model obtained maximum accuracy of 99.02%, 99.11%, and 99.03% on the VCifar-100, MIO-TCD, and GTSRB-T datasets, which are superior to the traditional models.