关键词： Mobile edge computing   mobile cloud computing   latency minimization   joint resource allocation   task splitting strategy   collaborative cloud and edge computing  

摘要： By performing data processing at the network edge, mobile edge computing can effectively overcome the deficiencies of network congestion and long latency in cloud computing systems. To improve edge cloud efficiency with limited communication and computation capacities, we investigate the collaboration between cloud computing and edge computing, where the tasks of mobile devices can be partially processed at the edge node and at the cloud server. First, a joint communication and computation resource allocation problem is formulated to minimize the weighted-sum latency of all mobile devices. Then, the closed-form optimal task splitting strategy is derived as a function of the normalized backhaul communication capacity and the normalized cloud computation capacity. Some interesting and useful insights for the optimal task splitting strategy are also highlighted by analyzing four special scenarios. Based on this, we further transform the original joint communication and computation resource allocation problem into an equivalent convex optimization problem and obtain the closed-form computation resource allocation strategy by leveraging the convex optimization theory. Moreover, a necessary condition is also developed to judge whether a task should be processed at the corresponding edge node only, without offloading to the cloud server. Finally, simulation results confirm our theoretical analysis and demonstrate that the proposed collaborative cloud and edge computing scheme can evidently achieve a better delay performance than the conventional schemes.

关键词： Mobile edge computing   Mobile edge applications   Clouds   Resource allocation   Scheduling algorithm  

摘要： 5G networks are anticipated to provide service for vertical industries. Mobile Edge Computing (MEC) framework is a physical infrastructure improvement to host mobile applications for the introduction of new service provisioning in 5G networks. Application Orchestration is responsible for scheduling user sessions to application instances. In this paper, we propose four scheduling algorithms to assign sessions in a system with multiple MEC clouds. Results show that scheduling based on the occupancy level of MECs is quite robust in various scenarios and can be applied with the fractional guard channel policy to provide service.

关键词： Autonomous driving   Mobile edge computing   Service deployment   QoS prediction   IoT  

摘要： Mobile edge computing for autonomous driving needs to manage heterogeneous resources and process large amounts of data or multi-purpose payload. There needs to be deploying, scheduling and migrating tasks on edge nodes to ensure the reliability of tasks or maximize the utilization of resources. However, applying autonomous learning methods on autonomous driving is exceptionally difficult, due to the complexity of multi-dimensional context and the sensitivity to hyperparameters. In this paper, we propose a learning approach to quality-of-service (QoS) prediction of services via multi-dimensional context, and develop a stable approach for service deployment that requires minimal hyperparameter tuning and a modest number of trials to learn multilayer neural network policies. This approach can automatically trades off exploration against exploitation by automatically tuning hyperparameter based on maximum entropy reinforcement learning. We then demonstrate that this approach achieves state-of-the-art performance on Autoware benchmark environments.

关键词： Mobile edge computing  

摘要： In this paper, we investigate the scheduling design of a mobile-edge computing (MEC) system, where the random arrival of mobile devices with computation tasks in both spatial and temporal domains is considered. The binary computation offloading model is adopted. Every task is indivisible and can be computed at either the mobile device or the MEC server. We formulate the optimization of task offloading decision, uplink transmission device selection and power allocation in all the frames as an infinite-horizon Markov decision process (MDP). Due to the uncertainty in device number and location, conventional approximate MDP approaches to addressing the curse of dimensionality cannot be applied. A novel low-complexity suboptimal solution framework is then proposed. We first introduce a baseline scheduling policy, whose value function can be derived analytically. Then, one-step policy iteration is adopted to obtain a sub-optimal scheduling policy whose performance can be bounded analytically. Simulation results show that the gain of the sub-optimal policy over various benchmarks is significant. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) can enhance the computing capability of mobile devices, and nonorthogonal multiple access (NOMA) can provide high data rates. Combining these two technologies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task completion time minimization in NOMA multiuser MEC networks, where multiple users can offload their tasks simultaneously via the same frequency band. We adopt the partial offloading, in which each user can partition its computation task into offloading computing and locally computing parts. We aim to minimize the maximum task latency among users by optimizing their tasks partition ratios and offloading transmit power. By considering the energy consumption and transmitted power limitation of each user, the formulated problem is quasi-convex. Thus, a bisection search (BSS) iterative algorithm is proposed to obtain the minimum task completion time. To reduce the complexity of the BSS algorithm and evaluate its optimality, we further derive the closed-form expressions of the optimal task partition ratio and offloading power for two-user NOMA MEC networks based on the analysed results. Simulation results demonstrate the convergence and optimality of the proposed a BSS algorithm and the effectiveness of the proposed optimal derivation. Copyright © 2019, The Authors. All rights reserved.

关键词： differential privacy   Hilbert curve   location-based service   mobile edge computing   privacy aware   quadtree  

摘要： In the era of edge computing, real-time data preprocessing on the edge node has the potential to improve computational efficiency and data accuracy. However, a significant challenge is private data disclosure, particularly in the case of location-based services. To address this challenge, in this paper, by leveraging differential privacy, we propose a privacy-aware framework for mobile edge computing called MEPA to protect the location privacy in which the edge node is regarded as an anonymous central server. The proposed framework can provide computing services without deploying special infrastructure. To be specific, in order to solve the problem of constrained computing resources in the edge nodes, the algorithm of Quadtree Differential Privacy based on Hilbert curve division (QTDP-H) two-dimensional spatial data query transmission is proposed. First, a noise quadtree is established and the privacy budget is divided according to the tree level. Then, the constructed quadtree is represented by quanternary, so that the partition based on Hilbert curve can be established and the two-dimensional data in the area can be converted into one-dimensional, which can greatly improve the retrieval efficiency. The effectiveness of the proposed algorithm in terms of time complexity and retrieval accuracy has been verified by extensive experimental results. Compared with traditional methods of (D,epsilon) - LP, the average runtime can be reduced by 15%-20%, and the average relative error is reduced by 20%.

关键词： Mobile edge computing  

摘要： By allowing a mobile device to offload computation intensive tasks to a base station, mobile edge computing (MEC) is a promising solution for saving the mobile device's energy. In real applications, the offloading may span multiple fading blocks. In this paper, we investigate energy-efficient offloading over multiple fading blocks with random channel gains. An optimization problem is formulated, which optimizes the amount of data for offloading to minimize the total expected energy consumption of the mobile device. Although the formulated optimization problem is non-convex, we prove that the objective function of the problem is piecewise convex, and accordingly develop an optimal solution for the problem. Numerical results verify the correctness of our findings and the effectiveness of our proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： —Consider a mobile edge computing system in which users wish to obtain the result of a linear inference operation on locally measured input data. Unlike the offloaded input data, the model weight matrix is distributed across wireless Edge Nodes (ENs). ENs have non-deterministic computing times, and they can transmit any shared computed output back to the users cooperatively. This letter investigates the potential advantages obtained by coding model information prior to ENs’ storage. Through an information-theoretic analysis, it is concluded that, while generally limiting cooperation opportunities, coding is instrumental in reducing the overall computation-plus-communication latency. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing   computation offloading   resource allocation   deep reinforcement learning  

摘要： To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services,the mobile edge computing(MEC)has been drawing increased attention from both industry and academia *** paper focuses on mobile users’computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making *** wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown,we use the modelfree reinforcement learning(RL)framework to formulate and tackle the computation offloading *** mobile user learns through interactions with the environment and the estimate of its performance in the form of value function,then it chooses the overhead-aware optimal computation offloading action(local computing or edge computing)based on its *** state spaces are high-dimensional in our work and value function is unrealistic to ***,we use deep reinforcement learning algorithm,which combines RL method Q-learning with the deep neural network(DNN)to approximate the value functions for complicated control applications,and the optimal policy will be obtained when the value function reaches *** results showed that the effectiveness of the proposed method in comparison with baseline methods in terms of total overheads of all mobile users.

关键词： Mobile edge computing   Wireless energy transfer   Multi-user offloading   Lyapunov optimization  

摘要： The combination of mobile edge computing (MEC) and wireless energy transfer (WET) has been recognized as a prominent solution to liberate massive wireless devices from limited battery capacities and compute-intensive workloads in the Internet of Things (IoT) environment. This new paradigm is called wireless powered mobile edge computing (WP-MEC). In this paper, an online computation rate maximization (OCRM) algorithm for multi-user WP-MEC systems is proposed by jointly managing the radio, computational resources, allocating time for energy transfer and data transmission. In each time block, the optimal bandwidth allocation and data transmit powers for offloading are achieved by Bisection method with linear time complexity. In addition, the closed-form solutions of optimal time allocation and CPU-cycle frequencies of wireless devices are obtained. The theoretical performance analysis shows that the time average sum computation rate and task queue backlog obey an [O(1/V), O(V)] tradeoff. Numerical results verify that, our proposed OCRM algorithm outperforms the baseline schemes without joint optimization, which dramatically improves the computation rate while making the system stable.