关键词： Intelligent information services   mobile edge computing   intelligent algorithms  

摘要： In this paper, we investigate mobile edge computing (MEC) networks for intelligent information services, where there are N users equipped with K antennas and one access point (AP). The users have some computational tasks, and some of them can be decoupled by the AP, at the cost of a fee charged by the AP. For the considered system, we firstly consider two important metrics of interest: latency and fee. Then, we formulate a stochastic game to model the interaction between users and the AP. In this game, the AP sets prices to maximize its profit, while users devise the offloading strategy to reduce both the latency and charge. We further optimize the system by applying the array signal processing schemes on the users, in order to reduce the transmission latency. Simulation results are finally presented to verify the effectiveness of the stochastic game, and it is shown that the array signal processing scheme can help reduce the transmission latency significantly.

关键词： Servers   Task analysis   Delays   Energy consumption   Optimization   Mobile handsets   Quality of service   Mobile edge computing   Internet of Things   delay   service cache   task offloading  

摘要： As more and more compute-intensive and delay-sensitive applications are deployed on smart mobile devices, mobile edge computing is considered an effective way to solve the limited computing ability of smart mobile devices (SMDs). At present, latency has become the most critical indicator of the quality of service (QoS), and more and more studies focus on this aspect. Unlike previous work, our work fully takes into account the limited storage and computing ability of edge servers. To effectively reduce the delay of SMDs and improve QoS, we propose a Delay Control Strategy Joint Service Caching and Task Offloading (DCS-OCTO) in a three-tier mobile edge computing (MEC) system consist of multi-user, multi-edge server and remote cloud servers. Some of the key challenges include service heterogeneity, unknown system dynamics, spatial demand coupling, and decentralized coordination. In particular, a very compelling but rarely studied issue is the dynamic service caching in the three-tier MEC system. The DCS-OCTO strategy is proposed based on Lyapunov optimization and Gibbs sampling. It works online without requiring prior information and achieves provable near-optimal performance. Finally, simulation results show that the strategy effectively reduces the overall system delay while ensuring low energy consumption.

关键词： Internet of vehicles   mobile edge computing   deep reinforcement learning   resource allocation   delay tolerance  

摘要： The advancement of 5G technology has brought the prosperous development of Internet of Vehicles (IoV). IoV services are not only computational intensive but also extremely sensitive to the delay. As a promising computing paradigm, mobile edge computing (MEC) can be applied to IoV scenarios. However, due to the limited resources of a single MEC server, it is difficult to cope with the suddenly increased computation loads caused by emergencies, or the intensive resource requests from busy regions. Therefore, we propose a novel regional intelligent management vehicular system with dual MEC planes, in which MEC servers in the same region cooperate with each other to achieve resource sharing. We classify computing tasks into different types according to their delay tolerances and focus on the optimization problem of resource allocation for different type tasks. And then, we design a resource allocation algorithm based on deep reinforcement learning, which can adapt to the changeable MEC environment to process high-dimensional data. Simulation results confirm that our proposed scheme is feasible and effective.

关键词： Mobile edge computing  

摘要： Exploiting the idle computation resources of mobile devices in mobile edge computing (MEC) system can achieve both channel diversity and computing diversity as mobile devices can offload their computation tasks to nearby mobile devices in addition to MEC server embedded access point (AP). In this paper, we propose a non-orthogonal multiple-access (NOMA)-A ided cooperative computing scheme in a basic three-node MEC system consisting of a user, a helper, and an AP. In particular, we assume that the user can simultaneously offload data to the helper and the AP using NOMA, while the helper can locally compute data and offload data to the AP at the same time. We study two optimization problems, energy consumption minimization and offloading data maximization, by joint communication and computation resource allocation of the user and helper. We find the optimal solutions for the two non-convex problems by some proper mathematical methods. Simulation results are presented to demonstrate the effectiveness of the proposed schemes. Some useful insights are provided for practical designs. Copyright © 2019, The Authors. All rights reserved.

关键词： latency-sensitive applications   Lyapunov optimization   mobile edge computing   service deployment   task scheduling  

摘要： Currently, Mobile edge computing (MEC) is facing a great challenge that is how to make full use of edge resources to provide a seamless support for compute-intensive latency-sensitive applications. Prior studies often make a simple assumption that tasks can be executed upon every edge server, but the assumption does not hold in practical scenarios. Because a specific application task often corresponds to a certain service that provides the corresponding running environment, whereas an edge server only has limited resources and cannot offer too many services. How to decide service deployment of so many types of services among multiple edge servers is also a big challenge. To address the challenge, we study dynamic service deployment for latency-sensitive applications. We first model the long-term budget-constrained latency minimization problem as a multi-slot latency minimization problem based on the Lyapunov framework. By doing this, the hardness of a problem is significantly reduced, since we never require future information to solve the long-term optimization. Furthermore, we extend our study by joining the task scheduling optimization, where every edge server is fully utilized in an even more efficient collaborative manner. Our extensive experiments show that the proposed algorithms can bring short latency with low cost.

关键词： Mobile edge computing  

摘要： We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wireless medium. We propose a multi-agent deep reinforcement learning algorithm, where each server is equipped with an agent, observing the status of its associated users and selecting the best user for offloading at each step. We consider computation time (i.e., task completion time) and system lifetime as two key performance indicators, and we numerically demonstrate that our approach outperforms baseline algorithms in terms of the trade-off between computation time and system lifetime. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing   Edge content delivery   Edge content update   Edge server   Cache pool   Content server  

摘要： Content delivery network (CDN) has gained increasing popularity in recent years for facilitating content delivery. Most existing CDN-based works upload the content generated by mobile users to the cloud data center firstly. Then, the cloud data center delivers the content to the proxy server. Finally, the mobile users request the required content from the proxy server. However, uploading all the collected content to the cloud data center increases the pressure on the core network. In addition, it also wastes a lot of bandwidth resources because most of the content does not have to be uploaded. To make up for the shortcomings of existing CDN-based works, this article proposes an edge content delivery and update (ECDU) framework based on mobile edge computing architecture. In the ECDU framework, we deploy a number of content servers to store raw content collected from mobile users, and cache pools to store content that frequently requested at the edge of the network. Thus, it is not necessary to upload all content collected by mobile users to the cloud data center, thereby alleviating the pressure of the core network. Based on content popularity and cache pool ranking, we also propose edge content delivery (ECD) and edge content update (ECU) schemes. The ECD scheme is to deliver content from cloud data center to cache pool, and the ECU scheme is to mitigate the content to appropriate cache pools in terms of its request frequency and cache pool ranking. Finally, a representative case study is provided and several open research issues are discussed.

关键词： Mobile edge computing  

摘要： —To fulfill the requirements of various emerging applications, the future sixth generation (6G) mobile network is expected to be an innately intelligent, highly dynamic, ultradense heterogeneous network that interconnects all things with extremely low-latency and high speed data transmission. It is believed that artificial intelligence (AI) will be the most innovative technique that can achieve intelligent automated network operations, management and maintenance in future complex 6G networks. Driven by AI techniques, device-to-device (D2D) communication will be one of the pieces of the 6G jigsaw puzzle. To construct an efficient implementation of intelligent D2D in future 6G, we outline a number of potential D2D solutions associating with 6G in terms of mobile edge computing, network slicing, and Non-orthogonal multiple access (NOMA) cognitive Networking.I. INTRODUCTION Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing   Wireless energy transfer   NOMA   IoT  

摘要： The mobile edge computing (MEC) is combined with wireless energy transfer (WET) to enhance the performance of wireless devices in Internet of Things (IoT) systems, which is called wireless powered mobile edge computing (WP-MEC). The sustainable energy supply and adequate computing capabilities are provided for wireless devices in WP-MEC. In this paper, we propose an online energy consumption minimization (OECM) algorithm for multiple IoT devices with latency constraints in WP-MEC environment. The nonorthogonal multiple access (NOMA) technique is used to address the preamble collision where two or more devices may select the same preamble. Based on Lyapunov optimization framework, optimal results of the time allocation for energy transfer and data transmission, the proportions of offloading tasks, data transmit powers and CPU-cycle frequencies are achieved in each time block by an iterative algorithm which leverages a convex approximation approach. The theoretical performance analysis shows that there is an upper bound of the system energy consumption. Simulation results verify that our proposed OECM algorithm outperforms the scheme without wireless energy transfer and other benchmark algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： 5G   Cooperative offloading   FiWi enhanced HetNets   Mobile edge computing  

摘要： Novel 5G applications will require the processing of large amounts of computation-intensive, delay-sensitive tasks. Mobile devices, however, are subject to limited hardware resources (e.g., battery life, CPU, and storage) and therefore may fall short of satisfying the stringent latency requirements of complex tasks. To fill the gap between the increasing demands of computation-intensive, delay-sensitive tasks driven by emerging 5G applications and the availability of limited resources on smart mobile devices, we introduce our proposed architecture of multi-access computing (MEC) enabled Fiber-Wireless (FiWi) LTE enhanced HetNets, enabling the coexistence and cooperation between conventional (remote) cloud and MEC servers. After presenting our analytical framework to evaluate the energy-delay performance, we propose our distributed cooperative offloading strategy with the objective of minimizing the average response time for mobile users.