关键词： FiWi   mobile edge computing   vehicular edge computing networks   SDN   load balancing  

摘要： Recently, the rapid advance of vehicular networks has led to the emergence of diverse delay-sensitive vehicular applications such as automatic driving, auto navigation. Note that existing resource-constrained vehicles cannot adequately meet these demands on low / ultra-low latency. By offloading parts of the vehicles' compute-intensive tasks to the edge servers in proximity, mobile edge computing is envisioned as a promising paradigm, giving rise to the vehicular edge computing networks (VECNs). However, most existing works on task offloading in VECNs did not take the load balancing of the computation resources at the edge servers into account. To address these issues and given the high dynamics of vehicular networks, we introduce fiber-wireless (FiWi) technology to enhance VECNs, due to its advantages on centralized network management and supporting multiple communication techniques. Aiming to minimize the processing delay of the vehicles' computation tasks, we propose a software-defined networking (SDN) based load-balancing task offloading scheme in FiWi enhanced VECNs, where SDN is introduced to provide supports for the centralized network and vehicle information management. Extensive analysis and numerical results corroborate that our proposed load-balancing scheme can achieve superior performance on processing delay reduction by utilizing the edge servers' computation resources more efficiently.

关键词： Congestion control   initial congestion window   deep reinforcement learning   mobile edge computing   software-defined networking  

摘要： Mobile edge computing provides users with low response time and avoids unnecessary data transmission. Due to the deployment of 5G, the emerging edge systems can provide gigabit bandwidth. However, network protocols have not evolved together. In TCP, the initial congestion window (IW) is such a low value that most short flows still stay in slow start phase when finishing, and do not fully utilize available bandwidth. Naively increasing IW may result in congestion, which causes long latency. Moreover, since the network environment is dynamic, we have a challenging problem-how to adaptively adjust IW such that flow completion time is optimized, while congestion is minimized. In this paper, we propose an adaptive online decision method to solve the problem, which learns the best policy using deep reinforcement learning stably and fast. In addition, we propose an approach to further improve the performance by supervised learning, using data collected during online learning. We also propose to adopt SDN to address the challenges in implementing our method in MEC systems. To evaluate our method, we build an MEC simulator based on ns3. Our simulations demonstrate that our method performs better than existing methods. It can effectively reduce FCT with little congestion caused.

关键词： Handover   Task analysis   Servers   Uplink   Computational modeling   Trajectory   Mobile edge computing   offloading   random mobility   vehicular networks   handover  

摘要： We study the mobility-aware computation offloading design for mobile edge computing (MEC) based vehicular wireless networks which considers random mobility of the vehicle and its potential handover during the offloading process. The computation offloading decision is made by comparing the average total costs due to the local and remote computation task executions at the vehicle and the MEC servers, respectively. Toward this end, we propose an analytical model to determine the average total cost due to remote task execution. We evaluate the performance of the proposed design by comparing its achievable cost with that due to an average mobility-based approximation. Simulation results show a cost reduction of up to 17% compared to the baseline.

关键词： Mobile edge computing  

摘要： Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computation efficiency maximization problems are formulated in wireless-powered MEC networks under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated for offloading. The energy harvesting time, the local computing frequency, and the offloading time and power are jointly optimized to maximize the computation efficiency under the max-min fairness criterion. Two iterative algorithms and two alternative optimization algorithms are respectively proposed to address the non-convex problems formulated in this paper. Simulation results show that the proposed resource allocation schemes outperform the benchmark schemes in terms of user fairness. Moreover, a tradeoff is elucidated between the achievable computation efficiency and the total number of computed bits. Furthermore, simulation results demonstrate that the partial computation offloading mode outperforms the binary computation offloading mode and NOMA outperforms TDMA in terms of computation efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： 5G   LSTM   Mobile edge computing   Task offloading   Unmanned axial vehicle  

摘要： Mobile edge computing (MEC) network provides near-users computing and communication functions and has become a potential 5G evolutionary architecture. In order to overcome the shortcomings of the existing MEC network in fixed base stations and limited computing resources, unmanned anal vehicle (UAV) is introduced as a relay edge computing node and UAV-enabled MEC networks are proposed. However, UAVs have limited energy. Thus, energy consumption would be an optimal target during the information interaction. Therefore, an energy efficiency optimization algorithm based on a three-layer computation offloading strategy is proposed in this paper by combining the UAV position optimization algorithm and the LSTM-based task prediction algorithm. The experiments show that the computation offloading strategy of the UAV-enabled MEC network can be dynamically programmed with the proposed algorithm and architecture, according to the required delay, UAV height, and data size in order to effectively reduce the energy consumption of the UAV.

关键词： Mobile edge computing  

摘要： With the advent of the modern mobile traffic, e.g., online gaming, augmented reality delivery and etc., a novel bidirectional computation task model where the input data of each task consists of two parts, one generated at the mobile device in real-time and the other originated from the Internet proactively, is emerging as an important use case of 5G. In this paper, for ease of analytical analysis, we consider the homogeneous bidirectional computation task model in a mobile edge network which consists of one mobile edge computing (MEC) server and one mobile device, both enabled with computing and caching capabilities. Each task can be served via three mechanisms, i.e., local computing with local caching, local computing without local caching and computing at the MEC server. To minimize the average bandwidth, we formulate the joint caching and computing optimization problem under the latency, cache size and average power constraints. We derive the closed-form expressions for the optimal policy and the minimum bandwidth. The tradeoff among communications, computing and caching is illustrated both analytically and numerically, which provides insightful guideline for the network designers. Copyright © 2020, The Authors. All rights reserved.

关键词： edge cloud   IoT   LPWAN   mobile edge computing   network control  

摘要： Recently, low-power wide area network (LPWAN) has attracted attention as a wireless network for long-range Internet of Things (IoT) services. IoT devices in an LPWAN are managed by a network server in the cloud. Various data are delivered from the IoT devices to the network server, and the role of the centralized network server in the cloud has become important for efficient data transfer over an LPWAN. However, control by the network server concentrates the control load on the network server and is vulnerable in terms of response time and bandwidth utilization. Therefore, this paper takes the approach of moving the control of the LPWAN to the edge cloud, which provides computing and storage environments at base stations. An LPWAN gateway is then integrated with the edge cloud, and LPWAN data are cached at the edge cloud in the gateway for LPWAN control. LPWAN control such as report interval control for data, transmission power control, and data aggregation is applied to improve the efficiency of data transmission. Network control is performed by learning using cached data in the edge cloud. Compared with the existing LPWAN control approach, the proposed approach exhibits improved performance for IoT data transmission. The simulation results show the proposed approach's efficiency.

关键词： Road-Side Unit   Software Defined Network   Reinforcement Learning   Mobile Edge Computing  

摘要： The growing number of IoT devices and the different computing and communication capabilities of IoT devices require an efficient offloading scheme. This offloading scheme need to consider the mobility of IoT devices and helps to intelligently select the optimal server for offloading. An efficient offloading scheme need to take in consideration important factors such as mobility of the IoT user device, speed and direction of the IoT user device as well as the computational capabilities of the user mobile device and the load of nearby servers. Unbalanced load of data or task offloading lead to high latency and poor services. An optimal selection of offloading server will clearly improve latency and QoS. Some new architecture of cellular network suggest the deployment of small-cell base stations (SBS) [1], [2] with a certain computing capabilities which can help offloading task of IoT mobile device or of their nearby SBS. In smart city environment, the mobile IoT device user needs to choose an SBS from several available SBSs within the its communication proximity. In this paper, we propose a Smart Ranking based Task Offloading approach for selecting an SBS and to improve the Quality of Service. This approach uses Q-Learning for SBS selection which will be modelled in Software Defined Network controller to deal with the problem of choosing the SBS in an intelligent way for Task offloading.

关键词： Road-Side Unit   Software Defined Network   Reinforcement Learning   Mobile Edge Computing  

摘要： The evolution of wide variety of applications that are used by vehicular users includes a lot of data hungry applications. This increases the workload on the cellular networks, thereby delivering poor service to the users. We can overcome this problem by sharing this workload with open wireless networks. As such, this improves the Quality of Service provided by cellular networks. Road-Side Units (RSU) are a wireless network which plays a major role in data offloading. Our approach discusses switching the communication network from cellular to RSU whenever there is an opportunity for a vehicle to offload vehicles data. Busy roads/urban traffic consists of several RSUs with many users. In urban environment, the vehicular user needs to choose an RSU from several available RSUs within the vehicle communication proximity. For seamless connectivity, the delay in network communication because of selecting the best RSU and frequent switching of connection between vehicles and RUSs must be minimized. In this paper, we propose a Smart Ranking based Data Offloading (SRDO) algorithm for selecting an RSU and to improve the Quality of Service. In SRDO algorithm, Q-Learning is utilized for RSU selection. This algorithm is modelled in Software Defined Network controller to deal with the problem of choosing the RSU in an intelligent way for data offloading.

关键词： Computation offloading   Mobile edge computing   Processing time optimization   Simulated annealing  

摘要： In recent years, the importance of the mobile edge computing (MEC) paradigm along with the 5G, the Internet of Things (IoT) and virtualization of network functions is well noticed. Besides, the implementation of computation-intensive applications at the mobile device level is limited by battery capacity, processing capabalities and execution time. To increase the batteries life and improve the quality of experience for computationally intensive and latency-sensitive applications, offloading some parts of these applications to the MEC is proposed. This paper presents a solution for a hard decision problem that jointly optimizes the processing time and computing resources in a mobile edge-computing node. Hence, we consider a mobile device with an offloadable list of heavy tasks and we jointly optimize the offloading decisions and the allocation of IT resources to reduce the latency of tasks’ processing. Thus, we developped a heuristic solution based on the simulated annealing algorithm, which can improve the offloading rate and reduce the total task latency while meeting short decision time. We performed a series of experiments to show its efficiency. Finally, the obtained results in terms of full-time treatrement are very encouraging. In addition, our solution makes offloading decisions within acceptable and achievable deadlines. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.