关键词： Mobile edge computing  

摘要： —In this paper, we propose a more refined video segment based Mobile Edge Computing (MEC) enhanced cache update strategy, which takes into account the client’s playback status and transmission state, MEC cache capacity and the popularity of each segment, to improve the quality of experience (QoE) of clients and the use ratio of MEC cache. In each cache update period, the segments which cannot bring significant QoE improvement will be deleted and meanwhile more suitable representations of the segments will be cahed instead. First, we divide the MEC cache space into three parts based on segment popularity and segment importance level. In addition, the size of different cache parts can be transformed to each other to further exploit the MEC cache space. For different parts, the corresponding deleted strategy and caching strategy are formulated, where the deleted strategy combines the requested times of segment and transmission capability of clients, and the caching strategy utilizes the playback state of clients to calculate the caching priority. Furthermore, we formulate the cache update problem to maximize the utility function of clients subject to the constraint of MEC cache size and transmission capacity. The brand and branch method is emplyed to obatain the optimal solution. Simulation results show that our proposed algorithm can improve system throughput and hit ratio of video segment, and at the same time decrease playback frozen time and system backhaul traffic compared with other existing algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Analog radio-over-fiber   mobile fronthaul   analog optical signal processing   M-QAM   silicon photonics   wavelength division multiplexing   optical filters   reconfigurable optical add   drop multiplexer   mobile edge computing   URLLC services  

摘要： A Wavelength Division Multiplexing (WDM)-enabled analog mobile fronthaul architecture is presented supporting enhanced flexibility by exploiting the fine filtering properties of a fully reconfigurable, silicon photonics-based filtering element. Numerical studies are presented emphasizing on the flexibility, low-energy consumption, and the compatibility of our proposed chip-scale implementations with the analog Radio over Fiber (RoF)-based mobile transport. Proof-of-concept analog photonic processing of 1 Gbaud Quadrature Phase Shift Keying (QPSK)-modulated radio is successfully demonstrated through RoF transmission experiments. The integration of the analog mobile fronthauling segment in a WDM-enabled, Reconfigurable Optical Add/Drop Multiplexers (ROADM)-assisted fronthaul/midhaul infrastructure supporting low-latency edge computing is also discussed.

关键词： computation offloading   mobile edge computing   energy harvesting   lyapunov optimization  

摘要： Recently, mobile edge computing (MEC) technology was developed to mitigate the overload problem in networks and cloud systems. An MEC system computes the offloading computation tasks from resource-constrained Internet of Things (IoT) devices. In addition, several convergence technologies with renewable energy resources (RERs) such as photovoltaics have been proposed to improve the survivability of IoT systems. This paper proposes an MEC integrated with RER system, which is referred to as energy-harvesting (EH) MEC. Since the energy supply of RERs is unstable due to various reasons, EH MEC needs to consider the state-of-charge (SoC) of the battery to ensure system stability. Therefore, in this paper, we propose an offloading scheduling algorithm considering the battery of EH MEC as well as the service quality of experience (QoE). The proposed scheduling algorithm consists of a two-stage operation, where the first stage consists of admission control of the offloading requests and the second stage consists of computation frequency scheduling of the MEC server. For the first stage, a non-convex optimization problem is designed considering the computation capability, SoC, and request deadline. To solve the non-convex problem, a greedy algorithm is proposed to obtain approximate optimal solutions. In the second stage, based on Lyapunov optimization, a low-complexity algorithm is proposed, which considers both the workload queue and battery stability. In addition, performance evaluations of the proposed algorithm were conducted via simulation. However, this paper has a limitation in terms of verifying in a real-world scenario.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) deploys edge servers to the side of wireless LAN and offloads some computing-intensive tasks to edge cloud servers, which can shorten the distance between computing services and mobile devices and reduce the cost of data *** paper considers the problem of computing task offloading in the mobile edge computing environment. By exploring the heterogeneity of user experience sensitivity, a ternary combination model of the number of CPU cycles, data size and price is established, and a computing task allocation strategy based on contract theory is established to maximize the profit of cloud service providers and ensure the non-negative benefit of mobile users. The optimal contract design strategies for complete information scenarios and statistical information scenarios are discussed, respectively. Simulation results show that the proposed schemes can effectively realize the offloading of computing tasks. © 2019, Editorial Office of Control and Decision. All right reserved.

关键词： mobile edge computing   low-latency network   orchestrator   processor sharing   simulation  

摘要： Most of latency-sensitive mobile applications depend on computational resources provided by a cloud computing service. The problem of relying on cloud computing is that, sometimes, the physical locations of cloud servers are distant from mobile users and the communication latency is long. As a result, the concept of distributed cloud service, called mobile edge computing (MEC), is being introduced in the 5G network. However, MEC can reduce only the communication latency. The computing latency in MEC must also be considered to satisfy the required total latency of services. In this research, we study the impact of both latencies in MEC architecture with regard to latency-sensitive services. We also consider a centralized model, in which we use a controller to manage flows between users and mobile edge resources to analyze MEC in a practical architecture. Simulations show that the interval and controller latency trigger some blocking and error in the system. However, the permissive system which relaxes latency constraints and chooses an edge server by the lowest total latency can improve the system performance impressively.

关键词： autonomous driving   mobile edge computing   task scheduling   location prediction   autopilot task classification  

摘要： With the rapid development of various new types of services, autonomous driving has received extensive attention. Due to the dense traffic flow, the limited battery life and computing power of the vehicles, intelligent vehicles are unable to support some computationally intensive and urgent tasks. Autonomous driving imposes strict requirements on the response time of the task. Due to the strong computing power and proximity to the terminal of mobile edge computing (MEC) and the arrival of 5G, the task can be unloaded to MEC, and data can be exchanged in milliseconds, which can reduce the task execution time. However, the resources of the MEC server are still very limited. Therefore we proposed a scheduling algorithm that takes into account the special task of the autopilot. Tasks will select the appropriate edge cloud execution and schedule the execution sequence on the edge cloud by the scheduling algorithm. At the same time, we take the mobility of high-speed vehicles into consideration. The position of the vehicle can be obtained by the prediction algorithm, and the task results are returned to the vehicle by means of other edge clouds. The experimental results show that with the increase of the task amount, the algorithm can effectively schedule more tasks to be completed within the specified time, and in different time slots;it can also predict the location of the vehicle and return the result to the vehicle.

关键词： Mobile edge computing   blockchain   smart contracts   video transcoding   resource allocation  

摘要： The blockchain-based video systems are designed to build a decentralized and flexible video ecosystem by enabling a direct interaction among users, video providers (VPs), and service providers. In blockchain-based video systems, the heterogeneous qualities and formats of the video streams usually require massive computational resources to transcode them into different versions and formats to meet distinct requirements of users. However, current blockchains cannot handle massive and heterogeneous video streaming due to limited computing capacity and long transaction times. To deal with this issue, in this paper, leveraging mobile edge computing (MEC) technology, we propose a blockchain-based MEC architecture, where small base stations (SBSs) allocate their computation as well as communication resources for providing video streaming in a distributed and secure manner. Moreover, to improve the operation efficiency, we use a series of smart contracts to enable a self-organized video transcoding and delivery service without a centralized controller. Then, users, SBSs, and VP could adjust their strategies according to the transactional information on blockchain. Moreover, we formulate the video transcoding and delivery problem as a three-stage Stackelberg game. We analyze the sub-game equilibrium in each stage and the interplays of the three-stage game. Last, we propose an iterative algorithm to solve the problem. Simulation results show that the proposed approach could obtain the good performance in terms of average time to finality (TTF), average access delay, and network cost.

关键词： task offloading   mobile edge computing   latency-classification   latency-aware  

摘要： In this study, we consider an edge cloud server in which a lightweight server is placed near a user device for the rapid processing and storage of large amounts of data. For the edge cloud server, we propose a latency classification algorithm based on deadlines and urgency levels (i.e., latency-sensitive and latency-tolerant). Furthermore, we design a task offloading algorithm to reduce the execution time of latency-sensitive tasks without violating deadlines. Unlike prior studies on task offloading or scheduling that have applied no deadlines or task-based deadlines, we focus on a comprehensive deadline-aware task scheduling scheme that performs task offloading by considering the real-time properties of latency-sensitive tasks. Specifically, when a task is offloaded to the edge cloud server due to a lack of resources on the user device, services could be provided without delay by offloading latency-tolerant tasks first, which are presumed to perform relatively important functions. When offloading a task, the type of the task, weight of the task, task size, estimated execution time, and offloading time are considered. By distributing and offloading latency-sensitive tasks as much as possible, the performance degradation of the system can be minimized. Based on experimental performance evaluations, we prove that our latency-based task offloading algorithm achieves a significant execution time reduction compared to previous solutions without incurring deadline violations. Unlike existing research, we applied delays with various network types in the MEC (mobile edge computing) environment for verification, and the experimental result was measured not only by the total response time but also by the cause of the task failure rate.

关键词： Task analysis   Servers   Mobile handsets   Delays   Edge computing   Resource management   Cloud computing   Mobile edge computing   computing sharing   task offloading   computing allocation  

摘要： The underutilized resources from nearby wired devices can be shared to compute for the restricted mobile devices at the edge of network. In this letter, we propose the share-based edge computing (SEC) paradigm with the mobile-to-wired (M2W) offloading computing. By sharing the idle resources under control of SEC servers, tasks from mobile devices can be computed on wired devices. Moreover, a well-designed computing allocation and sharing (CAS) algorithm is proposed to ensure the low fail ratio and the high utilization ratio of the SEC paradigm. Numerical results demonstrate the SEC paradigm has a better performance compared to local computing and mobile edge computing (MEC).

关键词： mobile edge computing   vehicular platooning   task offloading   Lyapunov optimization  

摘要： Due to limited computation resources of a vehicle terminal, it is impossible to meet the demands of some applications and services, especially for computation-intensive types, which not only results in computation burden and delay, but also consumes more energy. Mobile edge computing (MEC) is an emerging architecture in which computation and storage services are extended to the edge of a network, which is an advanced technology to support multiple applications and services that requires ultra-low latency. In this paper, a task offloading approach for an MEC-assisted vehicle platooning is proposed, where the Lyapunov optimization algorithm is employed to solve the optimization problem under the condition of stability of task queues. The proposed approach dynamically adjusts the offloading decisions for all tasks according to data parameters of current task, and judge whether it is executed locally, in other platooning member or at an MEC server. The simulation results show that the proposed algorithm can effectively reduce energy consumption of task execution and greatly improve the offloading efficiency compared with the shortest queue waiting time algorithm and the full offloading to an MEC algorithm.