关键词： 5G networks   mobile edge computing   edge devices   reliability  

摘要： Mobile Edge Computing (MEC) and fog computing are the key technologies in fifth generation (5 G) networks. In an MEC system, the data of terminal devices can be processed at the edge nodes also known as fog nodes, which can reduce the data transmission from the terminal devices to the cloud, thus reducing the latency and pressure of network traffic. Due to the huge amount of users' data, a large number of edge nodes need to be deployed. Therefore, we study how to optimally deploy the edge devices on 5G-based small cells (SC) networks based on many-objective evolutionary algorithm (MaOEA). Our goal is to optimize the deployment of edge devices to maximize service quality and reliability, while minimizing cost and energy consumption. This is an NP-hard problem with many objectives. To solve this problem, we propose an improved optimization algorithm named grouping-based many-objective evolutionary algorithm (GMEA). We also compare the performance of GMEA with the state-of-the-art algorithms, and the experimental results demonstrate that GMEA performs better than the other methods in both visualization results and hypervolume (HV) indicators.

关键词： Unified modeling language   Disaster management   Atmospheric modeling   Task analysis   Wireless communication   Edge computing   Computational modeling   UAV technology   mobile edge computing   disaster management   markov chain  

摘要： After disasters, network communication is highly susceptible to disruption. In this case, we may need solutions without original architectures to meet the requirements of connectivity and communication. As a research hotspot, existing studies and practices in disaster management are often costly and may have to rely on differentiated strategies to deal with actual situations. In this paper, we choose UAVs as edge node carriers and LoRaWAN (Long Range Wide Area Networking) as a communication method in coping with mobile edge computing (MEC) for disaster management. Here we propose UAV-mounted MEC task management strategies to achieve emergency communication enabled by LoRaWAN. The system model includes two parts, air-to-ground and remote-to-air, in which we choose LoS/NLoS path loss model and log-distance to describe the connections. The experiment results show that our strategy can achieve low-cost, long-range MEC service, which can be quickly deployed in the affected area after disasters. We also choose path loss, SNR (signal-noise ratio), and channel capacity as performance metrics and prove that our solutions can increase the channel capacity while maintaining the same level of path loss and SNR.

关键词： Mobile edge computing   Wireless power transfer   Cooperation   UAV   Subcarrier allocation  

摘要： Mobile edge computing (MEC) is a prospective technology to enhance the computing power of mobile devices by offloading the tasks to MEC server. Due to the limited size of mobile devices, the insufficient of battery becomes a serious problem. Wireless power transfer (WPT) and mobile devices cooperation can extend the endurance of mobile devices. Integrating unmanned aerial vehicle (UAV) into wireless powered MEC system, the energy collection efficiency can be improved with the short direct path power transfer. However, mobile devices will interfere with each other by utilizing the same bandwidth during the devices cooperation. In this paper, we proposed an anti-interference UAV powered cooperative mobile edge computing scheme, in which mobile devices utilizes different subcarriers to transmit the information. Moreover, a resource optimization problem is formulated to minimize the transmit energy of the UAV through joint subcarriers and power allocation of mobile devices with the constraints of delay and computing tasks size. Simulation results demonstrate the performance of the proposed scheme. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   deep reinforcement learning   digital twin   digital twin edge network  

摘要： 6G is envisioned to empower wireless communication and computation through the digitalization and connectivity of everything, by establishing a digital representation of the real network environment. Mobile edge computing (MEC), as one of the key enabling factors, meets unprecedented challenges during mobile offloading due to the extremely complicated and unpredictable network environment in 6G. The existing works on offloading in MEC mainly ignore the effects of user mobility and the unpredictable MEC environment. In this paper, we present a new vision of Digital Twin Edge Networks (DITEN) where digital twins (DTs) of edge servers estimate edge servers' states and DT of the entire MEC system provides training data for offloading decision. A mobile offloading scheme is proposed in DITEN to minimize the offloading latency under the constraints of accumulated consumed service migration cost during user mobility. The Lyapunov optimization method is leveraged to simplify the long-term migration cost constraint to a multi-objective dynamic optimization problem, which is then solved by Actor-Critic deep reinforcement learning. Simulations results show that our proposed scheme effectively diminishes the average offloading latency, the offloading failure rate, and the service migration rate, as compared with benchmark schemes, while saving the system cost with DT assistance.

关键词： Vehicular networks   Mobile edge computing   Reinforcement learning  

摘要： In recent years, with the rapid development of Internet of Things (IoTs) and artificial intelligence, vehicular networks have transformed from simple interactive systems to smart integrated networks. The accompanying intelligent connected vehicles (ICVs) can communicate with each other and connect to the urban traffic information network, to support intelligent applications, i.e., autonomous driving, intelligent navigation, and in-vehicle entertainment services. These applications are usually delay-sensitive and compute-intensive, with the result that the computation resources of vehicles cannot meet the quality requirements of service for vehicles. To solve this problem, vehicular edge computing networks (VECNs) that utilize mobile edge computing offloading technology are seen as a promising paradigm. However, existing task offloading schemes lack consideration of the highly dynamic feature of vehicular networks, which makes them unable to give time-varying offloading decisions for dynamic changes in vehicular networks. Meanwhile, the current mobility model cannot truly reflect the actual road traffic situation. Toward this end, we study the task offloading problem in VECNs with the synchronized random walk model. Then, we propose a reinforcement learning-based scheme as our solution, and verify its superior performance in processing delay reduction and dynamic scene adaptability.

关键词： 3D connectivity   3D services   5G   6G   B5G   high-altitude platform stations   mobile edge computing   non-terrestrial communications   satellite   unmanned aerial vehicle  

摘要： Sixth generation will exploit satellite, aerial, and terrestrial platforms jointly to improve radio access capability and unlock the support of on-demand edge cloud services in three-dimensional (3D) space, by incorporating mobile edge computing (MEC) functionalities on aerial platforms and low-orbit satellites. This will extend the MEC support to devices and network elements in the sky and forge a space-borne MEC, enabling intelligent, personalized, and distributed on-demand services. End users will experience the impression of being surrounded by a distributed computer, fulfilling their requests with apparently zero latency. In this paper, we consider an architecture that provides communication, computation, and caching (C3) services on demand, anytime, and everywhere in 3D space, integrating conventional ground (terrestrial) base stations and flying (non-terrestrial) nodes. Given the complexity of the overall network, the C3 resources and management of aerial devices need to be jointly orchestrated via artificial intelligence-based algorithms, exploiting virtualized network functions dynamically deployed in a distributed manner across terrestrial and non-terrestrial nodes.

关键词： Prediction algorithms   Delays   Optimization   Cloud computing   Edge computing   5G mobile communication   Heuristic algorithms   Mobile edge computing   predictive service placement   two-timescale Lyapunov optimization  

摘要： Mobile edge computing (MEC) is emerging to support delay-sensitive 5G applications at the edge of mobile networks. When a user moves erratically among multiple MEC nodes, the challenge of how to dynamically migrate its service to maintain service performance (i.e., user-perceived latency) arises. However, frequent service migration can significantly increase operational cost, incurring the conflict between improving performance and reducing cost. To address these mis-aligned objectives, this paper studies the performance optimization of mobile edge service placement under the constraint of long-term cost budget. It is challenging because the budget involves the future uncertain information (e.g., user mobility). To overcome this difficulty, we devote to leveraging the power of prediction and advocate predictive service placement with predicted near-future information. By using two-timescale Lyapunov optimization method, we propose a T-slot predictive service placement (PSP) algorithm to incorporate the prediction of user mobility based on a frame-based design. We characterize the performance bounds of PSP in terms of cost-delay trade-off theoretically. Furthermore, we propose a new weight adjustment scheme for the queue in each frame named PSP-WU to exploit the historical queue information, which greatly reduces the length of queue while improving the quality of user-perceived latency. Rigorous theoretical analysis and extensive evaluations using realistic data traces demonstrate the superior performance of the proposed predictive schemes.

关键词： Micro-service   Mobile edge computing   Network resource consumption   Latency   Mashup  

摘要： Internet of Things (IoT) applications introduce a set of stringent requirements (e.g., low latency, high bandwidth) to network and computing paradigm. 5G networks are faced with great challenges for supporting IoT services. The centralized cloud computing paradigm also becomes inefficient for those stringent requirements. Only extending spectrum resources cannot solve the problem effectively. Mobile edge computing offers an IT service environment at the Radio Access Network edge and presents great opportunities for the development of IoT applications. With the capability to reduce latency and offer an improved user experience, mobile edge computing becomes a key technology toward 5G. To achieve abundant sharing, complex IoT applications have been implemented as a set of lightweight micro-services that are distributed among containers over the mobile edge network. How to produce the optimal collocation of suitable micro-service for an application in mobile edge computing environment is an important issue that should be addressed. To address this issue, we propose a latency-aware micro-service mashup approach in this paper. Firstly, the problem is formulated into an integer nonlinear programming. Then, we prove the NP-hardness of the problem by reducing it into the delay constrained least cost problem. Finally, we propose an approximation latency-aware micro-service mashup approach to solve the problem. Experiment results show that the proposed approach achieves a substantial reduction in network resource consumption while still ensuring the latency constraint.

关键词： Mobile edge computing   Deep reinforcement learning   Computation offloading   Multi-user MIMO  

摘要： Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and improve the quality of computation experience. In this paper, an MEC enabled multi-user multi-input multi-output (MIMO) system with stochastic wireless channels and task arrivals is considered. In order to minimize long-term average computation cost in terms of power consumption and buffering delay at each user, a deep reinforcement learning (DRL)-based dynamic computation offloading strategy is investigated to build a scalable system with limited feedback. Specifically, a continuous action space-based DRL approach named deep deterministic policy gradient (DDPG) is adopted to learn decentralized computation offloading policies at all users respectively, where local execution and task offloading powers will be adaptively allocated according to each user's local observation. Numerical results demonstrate that the proposed DDPG-based strategy can help each user learn an efficient dynamic offloading policy and also verify the superiority of its continuous power allocation capability to policies learned by conventional discrete action space-based reinforcement learning approaches like deep Q-network (DQN) as well as some other greedy strategies with reduced computation cost. Besides, power-delay tradeoff for computation offloading is also analyzed for both the DDPG-based and DQN-based strategies.

关键词： Mobile edge computing  

摘要： In this paper, we study resource allocation algorithm design for multi-user orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) in mobile edge computing (MEC) systems. To meet the stringent end-to-end delay and reliability requirements of URLLC MEC systems, we propose joint uplink-downlink resource allocation and finite blocklength transmission. Furthermore, we employ a partial time overlap between the uplink and downlink frames to minimize the end-to-end delay, which introduces a new time causality constraint. The proposed resource allocation algorithm is formulated as an optimization problem for minimization of the total weighted power consumption of the network under a constraint on the number of URLLC user bits computed within the maximum allowable computation time, i.e., the end-to-end delay of a computation task. Despite the non-convexity of the formulated optimization problem, we develop a globally optimal solution using a branch- and-bound approach based on discrete monotonic optimization theory. The branch-and-bound algorithm minimizes an upper bound on the total power consumption until convergence to the globally optimal value. Furthermore, to strike a balance between computational complexity and performance, we propose two efficient suboptimal algorithms based on successive convex approximation and second-order cone techniques. Our simulation results reveal that the proposed resource allocation algorithm design facilitates URLLC in MEC systems, and yields significant power savings compared to three baseline schemes. Moreover, our simulation results show that the proposed suboptimal algorithms offer different trade-offs between performance and complexity and attain a close-to-optimal performance at comparatively low complexity. Copyright © 2020, The Authors. All rights reserved.