关键词： Mobile edge computing   vehicular ad hoc network   computation offloading   resource allocation   bat algorithm  

摘要： The emergence of computation-intensive vehicle applications poses a significant challenge to the limited computation capacity of on-board equipments. Mobile edge computing has been recognized as a promising paradigm to provide high-performance vehicle services by offloading the applications to edge servers. However, it is still a challenge to efficiently utilize the available resources of vehicle nodes. In this article, we introduce mobile edge computing technology to vehicular ad hoc network to build a vehicular edge computing system, which provides a wide range of reliable services by utilizing the computing resources of vehicles on the road. Then, we study the computation offloading decision problem in this system and propose a novel multi-objective vehicular edge computing task scheduling algorithm which jointly optimizes the allocation of communication and computing resources. Extensive performance evaluation demonstrates that the proposed algorithm can effectively shorten the task execution time and has high reliability.

关键词： Mobile edge computing  

摘要： Existing works on task offloading in mobile edge computing (MEC) networks often assume a task be executed once at a single edge node (EN). Downloading the computed result from the EN back to the mobile user may suffer long delay if the downlink channel experiences strong interference or deep fading. This paper exploits the idea of computation replication in MEC networks to speed up the downloading phase. Computation replication allows each user to offload its task to multiple ENs for repetitive execution so as to create multiple copies of the computed result at different ENs which can then enable transmission cooperation and hence reduce the communication latency for result downloading. Yet, computation replication may also increase the communication latency for task uploading, despite the obvious increase in computation load. The main contribution of this work is to characterize asymptotically an order-optimal upload-download communication latency pair for a given computation load in a multi-user multi-server MEC network. Analysis shows when the computation load increases within a certain range, the downloading time can decrease in an inversely proportional way if it is binary offloading or decrease linearly if it is partial offloading, both at the expense of increasing the uploading time linearly. Copyright © 2019, The Authors. All rights reserved.

关键词： mobile edge computing   computation offloading   deep reinforcement learning  

摘要： This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs' energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm.

关键词： Computation offloading   Deep reinforcement learning   Mobile edge computing  

摘要： This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs’ energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Mobile edge computing   device-to-device communications   computation capacity   task offloading   resource allocation   cellular networks  

摘要： The future 5G wireless networks aim to support high-rate data communications and high-speed mobile computing. To achieve this goal, the mobile edge computing (MEC) and device-to-device (D2D) communications have been recently developed, both of which take advantage of the proximity for better performance. In this paper, we integrate the D2D communications with MEC to further improve the computation capacity of the cellular networks, where the task of each device can be offloaded to an edge node and a nearby D2D device. We aim to maximize the number of devices supported by the cellular networks with the constraints of both communication and computation resources. The optimization problem is formulated as a mixed integer non-linear problem, which is not easy to solve in general. To tackle it, we decouple it into two subproblems. The first one minimizes the required edge computation resource for a given D2D pair, while the second one maximizes the number of supported devices via optimal D2D pairing. We prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. Then, the optimal algorithm to the original problem is developed by solving two subproblems, and some insightful results, such as the optimal transmit power allocation and the task offloading strategy, are also highlighted. Our proposal is finally tested by extensive numerical simulation results, which demonstrate that combining D2D communications with MEC can significantly enhance the computation capacity of the system.

关键词： Internet of Things   Mobile code offloading   Mobile edge computing   Software defined networks  

摘要： Mobile code offloading is a popular technique for enhancing the apparent capabilities of resource constrained devices. It is of particular interest for developers trying to push ever more complex algorithms into embedded devices that belong to the Internet of Things (loT). Although there is much research into this area in the realm of smartphones and tablets, the same cannot be said for loT devices, for which very few code offloading frameworks exist. This paper presents MobiCOP-loT, an extension of the MobiCOP mobile code offloading solution for the smartphone and tablet ecosystem, that supports embedded devices and Google's newest loT initiative, Android Things. MobiCOP-loT allows developers to deploy surrogates on both distant clouds and proximate nodes located on the edge. As such, MobiCOP-loT enabled applications can take advantage of the benefits of mobile edge computing to further enhance their capabilities. We tested MobiCOP-loT in a variety of scenarios and we found improvements of up to 9 times in terms of performance and energy for centralized cloud deployments, and up to 16 times in the case of edge scenarios. Moreover, we show the platform is capable of automatic horizontal scaling, which allows it to adapt to varying network traffic conditions;and multitenancy, wherein a single server instance is capable of attending multiple clients' requests with minimal loss in performance. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Cooperative task offloading   mobile edge computing   cloud computing   ADMM   wireless network  

摘要： The deployment of cloud and edge computing forms a three-tier mobile computing network, where each task can be processed locally, by the edge nodes, or by the remote cloud server. In this paper, we consider a cooperative three-tier computing network by leveraging the vertical cooperation among devices, edge nodes and cloud servers, as well as the horizontal cooperation between edge nodes. In this network, we jointly optimize the offloading decision and the computation resource allocation to minimize the average task duration subject to the limited battery capacity of devices. However, the formulated problem is a large-scale mixed integer non-linear optimization problem with the growing number of base stations and devices, which is NP-hard in general. To develop an efficient offloading scheme with low complexity, we conduct a series of reformulation based on reformulation linearization technology and further propose a parallel optimization framework by utilizing alternating direction method of multipliers (ADMM) method and difference of convex functions (D.C.) programming. The proposed scheme decomposes the large-scale problem into some smaller subproblems, which are done across the multiple computation units in a parallel fashion to speed up the computation process. Simulation results demonstrate that the proposed scheme can obtain a near-optimal performance with low complexity, and can reduce up to 24% of the task duration compared with other schemes. Simulation also shows how much the vertical and horizontal computation cooperations affect the system performance under different network parameters.

关键词： Autonomous driving   Mobile edge computing   Task scheduling   Earliest deadline first  

摘要： Autonomous driving has received widespread attention in recent years, while the limited battery life and computing capability of autonomous vehicles cannot support some necessary computation-intensive and urgent tasks with strict response time requirements. The results of the tasks would be useless and may cause serious safety hazards if the given time constraints are exceeded. On the other side, mobile edge computing (MEC) offers the possibility of autonomous vehicles to complete these time-constraint tasks due to its proximity and strong computing capabilities, with the faster 5G wireless networks to enable vehicles and MEC servers to exchange data in milliseconds. Then, it is a key issue to make the MEC servers to execute and complete these time-constraint autonomous-driving tasks as many as possible. So, we propose a task scheduling algorithm that can consider characteristics of autonomous-driving tasks and select more suitable MEC servers with task migration, based on an improved earliest deadline first algorithm through the replacement and recombination of tasks. From the experimental results, it can be concluded that the algorithm can schedule more tasks and benefit the urgent tasks effectively with the increase of the task amounts.

关键词： Mobile edge computing   Energy efficiency   Computation offloading   Resource allocation   Distributed algorithm  

摘要： Mobile edge computing is an emerging computing paradigm to augment computational capabilities of mobile devices by offloading computation-intensive tasks from resource-constrained smart mobile device onto edge clouds nearby with potential computation capability. However, in general, edge clouds have limited computation resource and energy. Thus it is critical to achieve high energy efficiency while ensuring satisfactory user experience. In this paper, we first formulate the computation offloading problem into the system energy-efficiency cost minimization problem by taking into account the completion time and energy. Then, by solving the optimization problem, we propose a distributed algorithm consisting of clock frequency configuration, transmission power allocation, channel rate scheduling and offloading strategy selection. In this algorithm, the clock frequency for local execution and the transmission power in edge cloud execution are optimized jointly. Furthermore, to optimize the queue delay, we formulate an M/M/n queue model with individual capacity between different windows and propose an algorithm for optimal task offloading rate. Finally, the experimental results show that our algorithm can achieve energy-efficient offloading performance compared to other existing algorithms. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Mobile edge computing   partial offloading   computation energy efficiency  

摘要： This paper proposes a new algorithm to evaluate the performance of the mobile edge computing system. Specifically, a new metric called computation efficiency is defined as the number of calculated data bits divided by the corresponding energy consumption. In order to compute the required data timely, we combine two schemes, local computing and data offloading, into a joint computation algorithm. An optimization problem is formulated with the objective to maximize the sum of computation efficiency among users with weighting factors. With iterative and gradient descent methods, the problem can be solved efficiently. Simulation results show that the proposed scheme outperforms traditional approaches. In addition, the tradeoff study between local computing and data offloading reveals that when data size is small, local computing plays a more important role, but when the size grows, data offloading becomes preferable.