关键词： Communication and computation cooperation   unified offloading decision making   resource allocation   ME-RAN   mobile edge computing  

摘要： In order to support communication and computation cooperation, we propose a mobile edge cloud-radio access network (ME-RAN) architecture, which consists of mobile edge cloud (ME) as the computation provision platform and radio access network (RAN) as the communication interface. A cooperative offloading framework is proposed to achieve the following tasks: first, to increase user equipment' (UE') computing capacity by triggering offloading action, especially for the UE, which cannot complete the computation locally;second, to reduce the energy consumption for all the UEs by considering limited computing and communication resources. Based on above mentioned objectives, we formulate the energy consumption minimization problem, which is shown to be a non-convex mixed-integer programming. First, decentralized local decision algorithm is proposed for each UE to estimate the possible local resource consumption and decide if offloading is in its interest. This operation will reduce the overhead and signalling in the later stage. Then, centralized decision and resource allocation algorithm (CAR) is proposed to conduct the decision making and resource allocation in ME-RAN. Moreover, two low-complexity algorithms, i.e., UE with largest saved energy consumption accepted first (CAR-E) and UE with smallest required data rate accepted first (CAR-D) are proposed. Simulations show that the performance of the proposed algorithms is very close to the exhaustive search but with much less complexity.

关键词： Virtual reality   group gaming   mobile edge computing   service placement   model predictive control   graph cut  

摘要： To realize mobile virtual reality (VR) group gaming services which are currently hampered by the prohibitive bandwidth and the stringent delay requirements, we investigate the problem of provisioning such services using the emerging mobile edge cloudlet (MEC) networks with a distributed content rendering architecture. The underlying dynamic rendering-module placement problem requires to optimize the service's operational cost and the users' end-to-end performance, involving multiple intertwined conflicting system objectives that are discrete, nonconvex, and higher degree polynomial functions with coupled decisions and arbitrary user dynamics over time. We solve this online placement problem by leveraging model predictive control (MPC) and overcoming the aforementioned challenges over each prediction window. We explore the connection between the placement problem and the minimal s-t cut problem in graph theory and solve the former via solving a series of instances of the latter. We formally prove the performance guarantee of our approach. We also conduct extensive trace-driven evaluations and demonstrate the superior practical performance of our MPC-based approach compared to the de facto practices and the state-of-the-art alternatives.

关键词： Online computation offloading   blockchain   mobile edge computing   deep reinforcement learning  

摘要： Offloading computation-intensive tasks (e.g., blockchain consensus processes and data processing tasks) to the edge/cloud is a promising solution for blockchain-empowered mobile edge computing. However, the traditional offloading approaches (e.g., auction-based and game-theory approaches) fail to adjust the policy according to the changing environment and cannot achieve long-term performance. Moreover, the existing deep reinforcement learning-based offloading approaches suffer from the slow convergence caused by high-dimensional action space. In this paper, we propose a new model-free deep reinforcement learning-based online computation offloading approach for blockchain-empowered mobile edge computing in which both mining tasks and data processing tasks are considered. First, we formulate the online offloading problem as a Markov decision process by considering both the blockchain mining tasks and data processing tasks. Then, to maximize long-term offloading performance, we leverage deep reinforcement learning to accommodate highly dynamic environments and address the computational complexity. Furthermore, we introduce an adaptive genetic algorithm into the exploration of deep reinforcement learning to effectively avoid useless exploration and speed up the convergence without reducing performance. Finally, our experimental results demonstrate that our algorithm can converge quickly and outperform three benchmark policies.

关键词： mobile edge computing   trusted cooperative mechanism   D2D   social trust   reputation   authentication techniques  

摘要： Mobile edge computing (MEC) effectively integrates wireless network and Internet technologies and adds computing, storage, and processing functions to the edge of cellular networks. This new network architecture model can deliver services directly from the cloud to the very edge of the network while providing the best efficiency in mobile networks. However, due to the dynamic, open, and collaborative nature of MEC network environments, network security issues have become increasingly complex. Devices cannot easily ensure obtaining satisfactory and safe services because of the numerous, dynamic, and collaborative character of MEC devices and the lack of trust between devices. The trusted cooperative mechanism can help solve this problem. In this paper, we analyze the MEC network structure and device-to-device (D2D) trusted cooperative mechanism and their challenging issues and then discuss and compare different ways to establish the D2D trusted cooperative relationship in MEC, such as social trust, reputation, authentication techniques, and intrusion detection. All these ways focus on enhancing the efficiency, stability, and security of MEC services in presenting trustworthy services.

关键词： Mobile edge computing  

摘要： In this paper, we investigate a mobile edge computing (MEC) architecture with the assistance of an unmanned aerial vehicle (UAV). The UAV acts as a computing server to help the user equipment (UEs) compute their tasks as well as a relay to further offload the UEs' tasks to the access point (AP) for computing. The total energy consumption of the UAV and UEs is minimized by jointly optimizing the task allocation, the bandwidth allocation and the UAV's trajectory, subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints, and the UAV's trajectory constraints. The formulated optimization problem is nonconvex, and we propose an alternating algorithm to optimize the parameters iteratively. The effectiveness of the algorithm is verified by the simulation results, where great performance gain is achieved in comparison with some practical baselines, especially in handling the computation-intensive and latency-critical tasks. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing   Smart Home   Stackelberg game   one-to-many matching   computing resource allocation  

摘要： Mobile edge computing has attracted great interests in the popularity of fifth-generation (5G) networks and Internet of Things. It aims to supply low-latency and high-interaction services for delay-sensitive applications. Utilizing mobile edge computing with Smart Home, which is one of the most important fields of Internet of Things, is a method to satisfy users' demand for higher computing power and storage capacity. However, due to limited computing resource, how to improve efficiency of resource allocation is a challenge. In this article, we propose a hierarchical architecture in Smart Home with mobile edge computing, providing low-latency services and promoting edge process for smart devices. Based on that, a Stackelberg Game is designed in order to allocate computing resource to devices efficiently. Then, one-to-many matching is established to handle resource allocation problems. It is proved that the allocation strategy can optimize the utility of mobile edge computing server and improve allocating efficiency. Simulation results show the effectiveness of the proposed strategy compared with schemes based on auction game, and present performance with different changing system parameters.

关键词： Service migration   Mobile edge computing   Deep reinforcement learning  

摘要： Mobile edge computing (MEC) is a new network architecture that puts computing capabilities and storage resource at the edges of the network in a distributed manner, instead of a kind of centralized cloud computing architecture. The computation tasks of the users can be offloaded to the nearby MEC servers to achieve high quality of computation experience. As many applications' users have high mobility, such as applications of autonomous driving, the original MEC server with the offloaded tasks may become far from the users. Therefore, the key challenge of the MEC is to make decisions on where and when the tasks had better be migrated according to users' mobility. Existing works formulated this problem as a sequential decision making model and using Markov decision process (MDP) to solve, with assumption that mobility pattern of the users is known ahead. However, it is difficult to get users' mobility pattern in advance. In this paper, we propose a deep Q-network (DQN) based technique for task migration in MEC system. It can learn the optimal task migration policy from previous experiences without necessarily acquiring the information about users' mobility pattern in advance. Our proposed task migration algorithm is validated by conducting extensive simulations in the MEC system. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   caching   communication   video profit  

摘要： With the development of communication technology and the explosive growth of video traffic brought by the rapid growth of mobile devices (such as smartphones and wearable devices), great business opportunities have been brought to video service providers. In this paper, we make full use of the cache and computing capacity of edge cloud. Considering the multi bitrate of video, we design the video caching and processing model that offers maximized profit to video service provider. Specifically, we model this problem as the 0-1 optimization problem and design the learning-based online upper confidence bound algorithm based on multi-arm bandit theory. This algorithm can design the corresponding cache and process strategy in real time according to the users' request to video. Furthermore, this strategy can maximize the profit of video provider and satisfy the service quality for users. Finally, experimental results show that our proposed video caching and processing scheme is superior to other schemes.

关键词： Cloudlet   mobile edge computing   big data   mobile cloud computing   avatar   handoff   replica  

摘要： In a traditional big data network, data streams generated by User Equipments (UEs) are uploaded to the remote cloud (for further processing) via the Internet. However, moving a huge amount of data via the Internet may lead to a long End-to-End (E2E) delay between a UE and its computing resources (in the remote cloud) as well as severe traffic jams in the Internet. To overcome this drawback, we propose a cloudlet network to bring the computing and storage resources from the cloud to the mobile edge. Each base station is attached to one cloudlet and each UE is associated with its Avatar in the cloudlet to process its data locally. Thus, the E2E delay between a UE and its computing resources in its Avatars is reduced as compared to that in the traditional big data network. However, in order to maintain the low E2E delay when UEs roam away, it is necessary to hand off Avatars accordingly-it is not practical to hand off the Avatars' virtual disks during roaming as this will incur unbearable migration time and network congestion. We propose the LatEncy Aware Replica placemeNt (LEARN) algorithm to place a number of replicas of each Avatar's virtual disk into suitable cloudlets. Thus, the Avatar can be handed off among its cloudlets (which contain one of its replicas) without migrating its virtual disk. Simulations demonstrate that LEARN reduces the average E2E delay. Meanwhile, by considering the capacity limitation of each cloudlet, we propose the LatEncy aware Avatar hanDoff (LEAD) algorithm to place UEs' Avatars among the cloudlets such that the average E2E delay is minimized. Simulations demonstrate that LEAD maintains the low average E2E delay.

关键词： Mobile cloud computing   mobile edge computing   communication networks   offloading   application partitioning  

摘要： Application partitioning that splits the executions into local and remote parts, plays a critical role in high-performance mobile offloading systems. Optimal partitioning will allow mobile devices to obtain the highest benefit from Mobile Cloud Computing (MCC) or Mobile Edge Computing (MEC). Due to unstable resources in the wireless network (network disconnection, bandwidth fluctuation, network latency, etc.) and at the service nodes (different speeds of mobile devices and cloud/edge servers, memory, etc.), static partitioning solutions with fixed bandwidth and speed assumptions are unsuitable for offloading systems. In this paper, we study how to dynamically partition a given application effectively into local and remote parts while reducing the total cost to the degree possible. For general tasks (represented in arbitrary topological consumption graphs), we propose a Min-Cost Offloading Partitioning (MCOP) algorithm that aims at finding the optimal partitioning plan (i.e., to determine which portions of the application must run on the mobile device and which portions on cloud/edge servers) under different cost models and mobile environments. Simulation results show that the MCOP algorithm provides a stable method with low time complexity which significantly reduces execution time and energy consumption by optimally distributing tasks between mobile devices and servers, besides it adapts well to mobile environmental changes.