关键词： Mobile cloud gaming   Online provisioning   Mobile edge computing   QoE impairment  

摘要： Mobile cloud gaming (MCG), which is proposed to deliver high-quality gaming experience to users anywhere and anytime, suffers from tremendous wide-area traffic and long network delays. In order to shorten the delays and provide the gaming services in close proximity to end users, the mobile edge computing (MEC) is envisioned as a promising approach to enable related computations at the edge of the network. Unfortunately, the performance of MEC-enabled MCG highly depends on the provisioning of both gaming services and bandwidth, since the quality of gaming experience (QoE) is easily impaired by both delays and video frame rates. Furthermore, due to the erratic mobility of users, migrating the services accordingly actually decreases the QoE impairment, but incurs extra system cost, leading to the performance-cost tradeoff. In order to address these challenges, we jointly investigate adaptive service placement and bandwidth allocation for MEC-enabled MCG. Considering the system dynamics, we propose to minimize the QoE impairment in a long-term scope, under the constrained cost for migrations. We design an online two-layer iterative algorithm (OnTrial) to solve the problem. Theoretical analysis demonstrates that OnTrial achieves a near-optimal performance and the potential violation of the migration constraint is bounded. Extensive experiments with real-world traces confirm that OnTrial outperforms other algorithms regarding the long-term QoE impairment for games.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) provides physical resources closer to end users, becoming a good complement to cloud computing. The booming MEC brings many multiobjective optimization problems. The paper proposes a multiobjective optimization (MOO) algorithm called SAMOACO(MV), which provides a new choice for solving MOO problems of MEC. We improve the ACO(MV) algorithm that is only suitable for solving mixed-variable single-objective optimization (SOO) problems and propose a MOACO(MV) algorithm suitable for solving mixed-variable MOO problems. And aiming at the dependence of MOACO(MV) algorithm performance on parameter setting, we proposed the SAMOACO(MV) algorithm using a self-adaptive parameter setting scheme. Furthermore, the paper also designs some mixed-variable MOO benchmark problems for the purpose to test and compare the performance of the SAMOACO(MV) algorithm. The experiments indicate that the SAMOACO(MV) algorithm has excellent comprehensive performance and is an ideal choice for solving mixed-variable MOO problems.

关键词： Artificial intelligence   mobile edge computing   unmanned aerial vehicles   deep learning   optimization  

摘要： Recent trends in communication technologies and unmanned aerial vehicles(UAVs)find its application in several areas such as healthcare,surveillance,transportation,***,the integration of Internet of things(IoT)with cloud computing environment offers several benefits for the UAV *** the same time,aerial scene classification is one of the major research areas in UAV-enabledMEC *** UAV aerial imagery,efficient image representation is crucial for the purpose of scene *** existing scene classification techniques generate mid-level image features with limited representation capabilities that often end up in producing average ***,the current research work introduces a new DL-enabled aerial scene classificationmodel *** presented model enables theUAVs to capture aerial imageswhich are then transmitted to MEC for further ***,CapsuleNetwork(CapsNet)-based feature extraction technique is applied to derive a set of useful feature vectors from the aerial *** is important to have an appropriate hyperparameter tuning strategy,since manual parameter tuning of DL model tend to produce several configuration *** order to achieve this and to determine the hyperparameters of CapsNetmodel,Shuffled Shepherd Optimization(SSO)algorithm is ***,Backpropagation Neural Network(BPNN)classification model is applied to determine the appropriate class labels of aerial *** performance of SSO-CapsNet model was validated against two openly-accessible datasets namely,UC Merced(UCM)Land Use dataset andWHU-RS *** proposed SSO-CapsNet model outperformed the existing state-of-the-art methods and achieved maximum accuracy of 0.983,precision of 0.985,recall of 0.982,and F-score of 0.983.

关键词： mobile edge computing   probabilistic offloading strategy   multi-source fluid queue   birth-and-death process   cumulative distribution function  

摘要： Mobile edge computing (MEC) is one of the key technologies to achieve high bandwidth, low latency and reliable service in fifth generation (5G) networks. In order to better evaluate the performance of the probabilistic offloading strategy in a MEC system, we give a modeling method to capture the stochastic behavior of tasks based on a multi-source fluid queue. Considering multiple mobile devices (MDs) in a MEC system, we build a multi-source fluid queue to model the tasks offloaded to the MEC server. We give an approach to analyze the fluid queue driven by multiple independent heterogeneous finite-state birth-and-death processes (BDPs) and present the cumulative distribution function (CDF) of the edge buffer content. Then, we evaluate the performance measures in terms of the utilization of the MEC server, the expected edge buffer content and the average response time of a task. Finally, we provide numerical results with some analysis to illustrate the feasibility of the stochastic model built in this paper.

关键词： multi-UAV cooperation   mobile edge computing   Internet-of-Things   UAV assistance  

摘要： Introducing multi-UAV network with flexible deployment into mobile edge computing(MEC)can effectively improve the quality of service of Internet-of-Things services,reduce the coverage cost and resource waste rate of edge nodes,and also bring some *** paper first introduces the current situation and pain points of mobile edge computing,then analyzes the significance and value of using multi-UAV network to assist mobile edge computing,and summarizes its key technologies and typical *** the end,some open research problems and technology prospects of multi-UAV network assisted intelligent edge computing are put forward,which provide new ideas for the future development of this field.

关键词： Media Access Protocol   Merging   Protocols   Vehicle dynamics   Time division multiple access   Safety   Roads   collision prediction   slot assignment   medium access control   mobile edge computing   VANETs  

摘要： Vehicular ad hoc networks (VANETs) have been widely recognized as a promising solution to improve traffic safety and efficiency for the ability to provide situation awareness even though the potential dangers and traffic anomalies are out of the visual range. In VANETs, time-division multiple access (TDMA) based overlay protocols can prevent transmission collisions and play an important role in providing an efficient communication channel. However, due to high vehicle mobility and time-varying traffic flow, the existing TDMA-based slot allocation approaches cannot fully utilize the channel resources, resulting in high transmission delay and packet collision. To overcome these shortcomings, we propose a collision prediction and avoidance MAC (CPA-MAC) protocol that utilizes the capability of mobile edge computing (MEC) and machine learning in this paper. Specifically, we propose a new slot assignment method that aims to guarantee the high channel utilization and low delay of safety message under dynamic traffic conditions. Furthermore, we propose a new same-direction collisions prediction algorithm that combines the V2R communication and LSTM-based trajectory prediction algorithm. Finally, we conduct extensive experiments to demonstrate the effectiveness of the proposed protocol.

关键词： Mobile edge computing   Vertical offloading   Horizontal offloading  

摘要： Mobile Edge Computing architecture is one of the most promising architectures that can satisfy different quality of Services required by various applications. In this paper, we model mobile edge computing architecture with queue-length thresholds at user equipments and edges to determine whether the task is offloaded or not in federated cloud and edge computing systems. We propose two models as vertical default & vertical (VDV) model and vertical default & horizontal shortest (VDHS) model. The former only does vertical offloading, meaning that the edge can offload tasks to the cloud, while the latter does vertical offloading and horizontal offloading, meaning that the edge can offload tasks to other edges. However, it is very difficult to directly derive the performance metrics in our models, so we approximate them. Based on these approximations, we determine the optimal queue-length thresholds of UEs and edges. Experiment results show that analytical and simulation results match very well. Also VDHS can reduce the mean task sojourn time by 30% at most and increase delay satisfaction ratio by 11% at most compared with VDV.

关键词： dynamic service placement   delay optimization   cost efficiency   mobile edge computing  

摘要： Mobile edge computing is an emerging paradigm that supplies computation, storage, and networking resources between end devices and traditional cloud data centers. With increased investment of resources, users demand a higher quality-of-service (QoS). However, it is nontrivial to maintain service performance under the erratic activities of end-users. In this paper, we focus on the service placement problem under the continuous provisioning scenario in mobile edge computing for multiple mobile users. We propose a novel dynamic placement framework based on deep reinforcement learning (DSP-DRL) to optimize the total delay without overwhelming the constraints on physical resources and operational costs. In the learning framework, we propose a new migration conflicting resolution mechanism to avoid the invalid state in the decision module. We first formulate the service placement under the migration confliction into a mixed-integer linear programming (MILP) problem. Then, we propose a new migration conflict resolution mechanism to avoid the invalid state and approximate the policy in the decision modular according to the introduced migration feasibility factor. Extensive evaluations demonstrate that the proposed dynamic service placement framework outperforms baselines in terms of efficiency and overall latency.

关键词： Servers   Vehicle dynamics   Privacy   Optimization   Training   Reinforcement learning   Wireless networks   Edge caching   mobile edge computing   privacy preservation   distributed reinforcement learning   federated learning  

摘要： Mobile edge computing (MEC) is a prominent computing paradigm which expands the application fields of wireless communication. Due to the limitation of the capacities of user equipments and MEC servers, edge caching (EC) optimization is crucial to the effective utilization of the caching resources in MEC-enabled wireless networks. However, the dynamics and complexities of content popularities over space and time as well as the privacy preservation of users pose significant challenges to EC optimization. In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks. Specifically, we consider the fact that content popularities are dynamic, complicated and unobservable, and formulate the maximization of cache hit rates on devices as distributed problems under the constraints of privacy preservation. In particular, we convert the distributed optimizations into distributed model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction. Subsequently, a P2D3PG algorithm is developed based on distributed reinforcement learning to solve the distributed problems. Simulation results demonstrate the superiority of the proposed approach in improving EC hit rate over the baseline methods while preserving user privacy.

关键词： Servers   Delays   Computational modeling   Cloud computing   Costs   Energy consumption   Collaboration   Mobile edge computing   cloud computing   QoS   computation offloading   DNN partition  

摘要： With the rapid development of the Internet of Things (IoT) and communication technology, Deep Neural Network (DNN) applications like computer vision, can now be widely used in IoT devices. However, due to the insufficient memory, low computing capacity, and low battery capacity of IoT devices, it is difficult to support the high-efficiency DNN inference and meet users' requirements for Quality of Service (QoS). Worse still, offloading failures may occur during the massive DNN data transmission due to the intermittent wireless connectivity between IoT devices and the cloud. In order to fill this gap, we consider the partitioning and offloading of the DNN model, and design a novel optimization method for parallel offloading of large-scale DNN models in a local-edge-cloud collaborative environment with limited resources. Combined with the coupling coordination degree and node balance degree, an improved Double Dueling Prioritized deep Q-Network (DDPQN) algorithm is proposed to obtain the DNN offloading strategy. Compared with existing algorithms, the DDPQN algorithm can obtain an efficient DNN offloading strategy with low delay, low energy consumption, and low cost under the premise of ensuring "delay-energy-cost" coordination and reasonable allocation of computing resources in a local-edge-cloud collaborative environment.