关键词： Mobile edge computing   Computation offloading   Data caching   Optimization  

摘要： Mobile edge computing can augment the capabilities of mobile terminals (MTs) by enabling computing and caching functionalities for base stations (BSs). Because BSs have only limited computation and storage resources compared with cloud servers, they need to efficiently manage the computation offloading and data caching for MTs. In this paper, a novel scheme for efficient computation offloading and data caching assisted by mobile-edge-computing-enabled BSs (MEC-BSs) is proposed. To maximize the MT benefits in terms of reduced time and energy consumption, our scheme determines the probability that each MT offloads each type of its tasks to the MEC-BS and indicates whether the cloud data for each type of MT task is cached at the MEC-BS. A balance factor is used to flexibly adjust the tendency of the optimization between the minimization of time and energy consumption. Based on the stable probabilistic characteristics of MT tasks and the MEC-BS service, the optimization algorithm of our scheme can be executed independently and concurrently without deteriorating the system performance. The simulation results demonstrate that our scheme can largely improve the system performance and that it always outperforms other reference schemes in scenarios with multiple criteria.

关键词： Mobile edge computing   Container migration   Multi-user mobility   Deep reinforcement learning   Aware delay   Energy consumption  

摘要： Recently, mobile edge computing(MEC) has attracted considerable research effort, because mobile users can offload some tasks to edge servers that are closer to users than cloud servers for a better computing experience, which can bring enormous potential in next-generation wireless networks(5G). However, when users are moving, they may be far away from the edge server that undertakes the offloading task, resulting in unavoidable service discontinuity and degrading the user experience. Service migration mechanism is very crucial in 5G mobile Internet. In this paper, we propose a novel service migration scheme to support mobility. Our scheme is realized from three aspects: (1) we consider mobile user services can be deployed container virtual machine in corresponding edge servers, and develop container migration strategies to satisfy the trade-off between the users' aware delay and system energy consumption;(2) we further propose a deep reinforcement learning algorithm (DRL) based such multi-user server migration strategy (DRLMSM) to effectively achieve fast decision-making;(3) we build the architecture in Software Defined Network (SDN) framework to verify the practicality and effectiveness of DRLMSM. We conduct extensive experiments, which shows that our DRLMSM scheme outperforms the classical reinforcement learning(RL) algorithm and some other baseline algorithm. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Device-to-device communication   Relays   Cloud computing   Processor scheduling   Wireless communication   Uplink   Dynamic scheduling   Mobile edge computing   D2D communication   partial offloading   cooperative scheduling   computation allocating  

摘要： By fully exploiting the cooperative communication capacities among mobile terminals (MTs), the MTs can adapt the offloading designs well to the practical network with dynamic features. In this paper, joint multi-user cooperative partial offloading, transmission scheduling and computation allocating is discussed for device-to-device (D2D) underlay mobile edge computing (MEC). By considering stochastic application requests, unpredictable MTs states, time-varying channel states and computation resources, a customized application offloading model, which aims to minimize the network-wide response latency and energy consumption simultaneously, is formulated. In order to solve this non-convex and non-smooth optimization problem, an online resource coordinating and allocating scheme (ORCAS) is proposed by exploiting Lyapunov optimization theory, variable substitution technique and resource provisioning priority mechanism. Both theoretical analyses and simulation results demonstrate that the proposed ORCAS can 1) drive the application response cost converge to the minimum;2) achieve superior performance (e.g., the average network-wide response cost under ORCAS is approximately 19.14% lower than that under partial offloading directly);3) adapt to dynamic situations in terms of stochastic user demands and channel states.

关键词： Servers   Wireless communication   Wireless sensor networks   Downlink   Minimization   Receivers   Edge computing   Mobile edge computing   wireless power transfer   latency minimization  

摘要： This paper investigates a wireless powered mobile edge computing (MEC) network where a hybrid access point (H-AP) serves energy-constrained devices. For such a wireless powered MEC network, the H-AP first transmits wireless information and energy simultaneously in the downlink. Then, by utilizing the harvested energy, the device processes both its local data and the received data from the H-AP. In this system, we minimize the latency by jointly optimizing the power splitting ratio of rectifiers, the data offloading power, the local computing frequency, and the data offloading ratio at the device. By applying a polyblock outer approximation procedure, we propose an algorithm which guarantees the optimal solution. Simulation results verify the efficiency of the proposed algorithm compared to conventional schemes.

关键词： Task analysis   Delays   Quality of service   Wireless communication   Servers   Computational modeling   Resource management   Mobile edge computing   task offloading   deep reinforcement learning   customization   speed  

摘要： Recently, the task offloading and resource allocation (TORA) issues in mobile edge computing system have attracted much attention. However, the impact of different movement speeds on TORA problem and customized services for different service requirements have been largely ignored. These may severely influence the user's quality of service (QoS). Accordingly, a speed-aware and customized TORA scheme is proposed to minimize total service latency (consisted of execution delay and migration cost) for mobile users. First, the Advantage Actor Critic (A2C) algorithm is applied to dynamically decide on which computation node to execute the tasks. Second, we assign the user's priorities based on movement speeds and tolerable delay to allocate the wireless and computation resources respectively. Finally, the migration cost is introduced into objective model for further weakening the impact of service migration on user's QoS. Simulation results demonstrate the superiority and effectiveness of the proposed algorithm compared with the baselines.

关键词： Energy optimization   Femtocell   Heterogeneous networks   Macrocell   Mobile Edge Computing   small cells  

摘要： One of the main attributes of the heterogeneous cellular network is the composition of different cell sizes. From such heterogeneity, expanded network capabilities had sprouted with extensive computing power consumption and ultra-low latency constraints. Using a heterogeneous network will provide multiple paths in which the users' data can flow through the network depending on the users' available resources, remaining energy, etc. In this paper, we study the heterogeneous network model, which contains MeNB, SeNB, and Femtocells, and we propose a matching subcarrier resource allocation and offloading decision (MSRAOD) algorithm. MSRAOD aims at recourse allocation optimization to minimize the total energy consumption of mobile users' devices with acceptable latency requirements of the applications. We have evaluated MSRAOD through simulation, and when compared to non-optimized data offloading, the MSRAOD algorithm significantly enhances the average energy consumption of mobile users. Such results provide a promising roadmap towards the implementation of such an algorithm in offloading-heavy applications © 2021, International Journal of Communication Networks and Information Security. All Rights Reserved.

关键词： Task analysis   Servers   Optimization   Internet of Things   Resource management   Edge computing   Energy consumption   Mobile edge computing   delay-sensitive IoT applications   task partitioning   user association  

摘要： Mobile edge computing (MEC) is a promising solution to support emerging delay-sensitive mobile applications, such as self-driving, augment/virtual reality, and various Internet of Things (IoT) applications. By deploying MEC servers at network edge, e.g., close to cellular base stations (BSs), the computational tasks generated by these applications can be offloaded to edge nodes (ENs) and be quickly executed there. At the same time, with the projected large number of IoT devices, the communication and computational resources allocated to each user can be quite limited, making it challenging to provide low-latency MEC services. In this paper, we investigate the problem of task partitioning and user association in an MEC system, aiming to minimize the average latency of all users. We assume that each task can be partitioned into multiple subtasks that can be executed on local devices (e.g., vehicles), MEC servers, and/or cloud servers;each user can be associated with one of the nearby ENs. The subtasks can be independent of or dependent on each other. For each case, we formulate the joint optimization of task partitioning ratios and user association as a mixed integer programming problem. Each problem is solved by decomposing it into two subproblems. The lower-level subproblem is task partitioning under a given user association, which can be solved optimally. The higher-level subproblem is user association, we propose a dual decomposition-based approach and a matching-based approach to derive near-optimal solutions. Simulation results show that compared to benchmark schemes, the proposed schemes reduce the average latency by about 50% and 40% for the cases of independent and dependent subtasks, respectively.

关键词： Internet of things   Mobile edge computing   Deep learning   Image recognition   Feature extraction   Hierarchical discriminant analysis  

摘要： This article aims to improve the accuracy of real-time image recognition in the context of the Internet of Things (IoT), reduce the core network pressure of the IoT and the proportion of IoT broadband, and meet people's demand for internet image transmission. An intelligent image fusion system based on mobile edge computing (MEC) and deep learning is proposed, which can extract the features of images and optimize the sum of intra-class distance and inter-class distance relying on the hierarchical mode of deep learning, and realize distributed computing with the edge server and base station. Through comparison with other algorithms and strategies on the text and character data sets, the effectiveness of the constructed system is verified in the performance of the algorithm and the IoT. The results reveal that the application of the unsupervised learning hierarchical discriminant analysis (HDA) has better accuracy and recall in various databases compared with conventional image recognition algorithms. When the sum intra-class and inter-class distance K is 2, the accuracy of the algorithm can be as high as 98%. The combination of MEC and layered algorithms effectively reduces the pressures of core network and shortens the response time, greatly reduces the broadband occupancy ratio. The performance of IoT is increased by 37.03% compared with the general extraction and common cloud computing. Image recognition based on the MEC architecture can reduce the amount of network transmission and reduce the response time under the premise of ensuring the recognition rate, which can provide a theoretical basis for the research and application of user image recognition under the IoT.

关键词： Task analysis   Resource management   Servers   Quality of experience   Delays   Computational modeling   Approximation algorithms   Mobile edge computing   service caching   computation offloading   resource allocation   computation cost   delay cost  

摘要： Mobile Edge Computing (MEC) brings abundant cloud resources to the edge of the network and provides great opportunities to improve user's quality of experience. While many recent studies have investigated the problem of computation offloading, service caching is also an important design topic of MEC. Service caching stores application-related databases or libraries in advance and enables corresponding user tasks to be offloaded. Due to the limited resources in the edge server, service caching decisions have to be made judiciously to maximize the system performance. In this paper, we study the problem of joint service caching, computation offloading, transmission and computing resource allocation in a general scenario of multiple users with multiple tasks. We aim to minimize the overall computation and delay costs for all users and formulate the optimization problem as a quadratically constrained quadratic program (QCQP) which is non-convex and NP-hard. To solve this challenging problem, we propose an efficiently approximate algorithm based on semidefinite relaxation (SDR) approach and alternating optimization which always computes a locally optimal solution. Moreover, we extend the study to the scenario where each user has a computation cost constraint. Simulation results show that our algorithm can minimize the system cost effectively by utilizing the available system resources.

关键词： Task analysis   Delays   Edge computing   Optimization   Unmanned aerial vehicles   Cloud computing   Time division multiple access   Mobile edge computing   tethered UAV   delay minimization   UAV placement   resource allocation  

摘要： In this correspondence, we study a tethered unmanned aerial vehicle (TUAV)-assisted mobile edge computing (MEC) system, in which tasks of several ground mobile users (MUs) are served by ground edge cloud (GEC) with superior computing capability and poor communication links and TUAV-mounted cloudlet with lower computing capability and better communication links. We aim to minimize weighted-sum system delay of MUs by jointly optimizing TUAV position, time slot allocation, and task splitting ratio. We first decompose the optimization problem into three subproblems, and then to solve non-convex subproblem related to TUAV position, we reformulate it by the first-order Taylor series approximation and apply the successive convex approximation (SCA) method. Then, we tackle the overall problem by applying the alternative optimization algorithm. Numerical results verify that our proposed cooperative sky-ground mobile edge computing system (CoSMoS) algorithm can achieve a better performance than other benchmark schemes.