关键词： Internet of Vehicles (IoV)   mobile edge computing   cooperation   resource management   low latency   energy efficiency  

摘要： To overcome the inherent defect of centralized data processing in cloud computing, the mobile edge computing (MEC) brings data storage and computing capacities, to the edge closer to end users. However, the uneven distribution of access vehicles, as well the volume of computing data, cause the workload diversity among various mobile edge computing servers (MECSs). In this paper, we propose a hierarchical model with quality of service (QoS)-aware and power-aware resource management for the cooperative edge-computing-based intelligent vehicular network (CEC-IoV), and the system latency and energy efficiency at MECSs are respectively optimized. Specifically, considering the changing response times versus MECSs' workloads, the Minimum Latency with Migration Loads (MLML) scheme is developed for workload balance among multiple MECSs. By selecting the appropriate response time threshold and migration loads from overloading MECSs to idle MECSs simultaneously, the load-balancing problem can be efficiently solved for multiple MECSs with unbalanced workloads. On the other hand, through performing workload redistribution and dynamic reconfiguration of virtual machines (VMs) instantiated onto the parallel computing platform at one MECS, the energy-efficiency can he also optimized while guaranteeing the QoS requirement on the processing delay. With the latency constraint, the power minimization problem is formulated to be a convex one, and the semi-closed forms for optimal solutions of VMs' workloads and processing rates are provided using KKT conditions. Compared with the performance obtained by benchmark schemes, numerical results exhibit that our resource management schemes gain lower system latency and higher energy efficiency.

关键词： mobile edge computing   workflow tasks   offloading strategy   genetic algorithm  

摘要： Mobile edge computing (MEC) sinks the functions and services of cloud computing to the edge of the network to provide users with storage and computing resources. For workflow tasks, the interdependency and the sequence constraint being among the tasks make the offloading strategy more complicated. To obtain the optimal offloading and scheduling scheme for workflow tasks to minimize the total energy consumption of the system, a workflow task offloading and scheduling scheme based on an improved genetic algorithm is proposed in an MEC network with multiple users and multiple virtual machines (VMs). Firstly, the system model of the offloading and scheduling of workflow tasks in a multi-user and multi-VMs MEC network is built. Then, the problem of how to determine the optimal offloading and scheduling scheme of workflow to minimize the total energy consumption of the system while meeting the deadline constraint is formulated. To solve this problem, the improved genetic algorithm is adopted to obtain the optimal offloading strategy and scheduling. Finally, the simulation results show that the proposed scheme can achieve a lower energy consumption than other benchmark schemes.

关键词： Non-orthogonal multiple access   mobile edge computing   scheduling  

摘要： This letter investigates the cooperative computing in a non-orthogonal multiple access (NOMA)-enabled mobile edge computing (MEC) system consisting of one edge server, one user equipment (UE) and M full-duplex helpers. For this system, we design an adaptive cooperative computing strategy that operates in one of two modes: the cooperative NOMA-MEC mode and the non-cooperative orthogonal multiple access (OMA)-MEC mode. For this strategy, we propose a novel two-stage fullduplex helper scheduling scheme to improve the successful completion probability (SCP). Theoretical analysis is carried out to derive a tightly approximate SCP and further demonstrate that the UE achieves a full diversity in high main-to-loop-selfinterference ratio regime. Simulation results verify the accuracy of the theoretical analysis and demonstrate the advantages of the proposed strategy.

关键词： Mobile edge computing   Big data   Location-based services   Differential privacy   Cluster analysis   Privacy-preserving location-based services   Data stream privacy  

摘要： The advancements in positioning technologies have led to the emergence of various location-based services, resulting in a drastic increase in location-based data generation, producing big-data. Location data are often linked with user privacy, as they can reveal sensitive information such as the places visited by a person. Moreover, most location-based services involve resource-constrained devices, needing lightweight data processing approaches. Due to these reasons, privacy and efficiency have been two of the primary components of location-based data processing. The existing approaches do not study both issues in the same setting. Consequently, current methods fail to provide efficient privacy preservation solutions towards location-based data stream processing. To address these issues, we investigate the effective integration of edge computing, cloud computing and differential privacy for location-based data clustering, which is an essential area in service recommendations (e.g. recommending the closest hotels to a particular location). In the proposed setup, we use local differential privacy to ensure user privacy. Next, we apply edge-based clustering on the differentially private input data using mobile edge devices. Next, the centroids of the clusters are collected at a cloud server to generate final clustering in a privacy-preserving manner. Our experiments show that the proposed approach provides maximum accuracy of 90% on lower privacy budgets (e.g. epsilon = 0.45-0.5).(c) 2021 Elsevier Ltd. All rights reserved.

关键词： Task analysis   Energy harvesting   Delays   Servers   Energy consumption   Computational modeling   Central Processing Unit   Computation offloading   deep reinforcement learning   energy consumption   mobile edge computing   offloading strategy   wireless information and power transmission  

摘要： Computation offloading is an effective method in mobile edge computing (MEC) to relieve user equipment (UE) from the limited computation resource and battery capacity. Meanwhile, simultaneous wireless information and power transmission (SWIPT) can be applied to MEC to extend the operating time of the equipment. However, in multi-user network environment, diverse computation task requirements and changeable network channel states make it challenging to obtain offloading strategy timely and accurately. To address the issue, we propose an intelligent computation offloading scheme (iCOS) based on enhanced priority deep deterministic policy gradient (EPDDPG) algorithm to minimize the energy consumption of all the UEs by jointly optimizing the offloading decision, the central processing unit (CPU) frequency and the power split ratio in a dynamic SWIPT-MEC network. In particular, we improve the traditional fully-connected network structure to obtain both discrete and continuous action outputs, and accelerate neural network parameter updates by using prioritized experience tuples. Furthermore, we use dynamic voltage and frequency scaling (DVFS) technology to dynamically adjust the CPU frequency of local computing, and employ SWIPT technology to balance the charging and communication according to the obtained strategy. Simulation results show that the algorithm proposed in this paper can effectively reduce the energy cost of UEs, and complete more computation tasks within the delay limit.

关键词： computation offloading   resource allocation   energy efficient   unmanned aerial vehicle (UAV)   mobile edge computing   optimization  

摘要： With the emergence of various new Internet of Things (IoT) devices and the rapid increase in the number of users, enormous services and complex applications are growing rapidly. However, these services and applications are resource-intensive and data-hungry, requiring satisfactory quality-of-service (QoS) and network coverage density guarantees in sparsely populated areas, whereas the limited battery life and computing resources of IoT devices will inevitably become insufficient. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is one of the most promising solutions that ensures the stability and expansion of the network coverage area for these applications and provides them with computational capabilities. In this paper, computation offloading and resource allocation are jointly considered for multi-user multi-UAV-enabled mobile edge-cloud computing systems. First, we propose an efficient resource allocation and computation offloading model for a multi-user multi-UAV-enabled mobile edge-cloud computing system. Our proposed system is scalable and can support increases in network traffic without performance degradation. In addition, the network deploys multi-level mobile edge computing (MEC) technology to provide the computational capabilities at the edge of the radio access network (RAN). The core network is based on software-defined networking (SDN) technology to manage network traffic. Experimental results demonstrate that the proposed model can dramatically boost the system performance of the system in terms of time and energy.

关键词： mobile edge computing   future internet application   optimization problem   task allocation   heuristic algorithm   queueing theory  

摘要： For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers.

关键词： Autonomous aerial vehicles   Trajectory   Wireless communication   Task analysis   Resource management   Energy consumption   Computational modeling   Unmanned aerial vehicle (UAV)   mobile edge computing   user mobility   stochastic data arrivals   Lyapunov optimization   online algorithm  

摘要： The recent development of unmanned aerial vehicle (UAV) and mobile edge computing (MEC) technologies provides flexible and resilient computation services to mobile users out of the terrestrial computing service coverage. In this paper, we consider a UAV-enabled MEC platform that serves multiple mobile ground users with random movements and task arrivals. We aim to minimize the average weighted energy consumption of all users subject to the average UAV energy consumption and data queue stability constraints. We formulate the problem as a multi-stage stochastic optimization, and adopt Lyapunov optimization to convert it into per-slot deterministic problems with fewer optimizing variables. We design two reduced-complexity methods that solve the resource allocation and the UAV movement either in two sequential steps or jointly in one step. Both methods can guarantee to satisfy the average UAV energy and queue stability constraints, meanwhile achieving a tradeoff between the user energy consumption and the length of queue backlog. Simulation results show that the two methods significantly outperform the other benchmark methods including a learning-based method in reducing the energy consumption of ground users. In between, the proposed joint optimization method achieves better performance than the two-stage method at the cost of higher computational complexity.

关键词： Mobile edge computing   6G   caching   reinforcement learning   autonomous driving  

摘要： The rapid development of 6G can help to bring autonomous driving closed to the reality. Drivers and passengers will have more time for work and leisure spending in the vehicles, further generating a lot of data requirements. However, edge resources from small base stations are insufficient to match the wide variety of services of the future vehicular networks. Besides, due to the high-speed nature of the vehicles, users have to switch the connections among different base stations, whereas such way will cause external latency during the data request. Therefore, it is vital to enable the local cache of vehicle users to realize the reliable autonomous driving. In this paper, we consider caching the contents in the local cache, small base station, and edge server. In practice, the request preference of some single users may he different from a whole region. To maximize the efficiency of content cache, we design a strategy that uses reinforcement learning algorithm to optimize cache schemes on different devices. The experimental results demonstrate that our strategy can enhance the cache hit ratio by 10%-20% compared with the well-known counterparts.

关键词： Diverse service   mobile edge computing   resource allocation   multi-objective reinforcement learning   multi-objective proximal policy optimization  

摘要： Mobile services are becoming more diverse, making them have different demands on communications, computing, and caching (3C) resources in mobile systems. Unlike the traditional work that considers only one type of service, this paper designs a unified framework to characterize the different kinds of services, and jointly optimizes the 3C resources of the base station (BS) and mobile devices to provide differentiated quality of service (QoS) for diverse services. In the proposed framework, we model the task required by the mobile device to be generated at the BS, the mobile device, or both of them, which means the requested tasks are served through different paths, consuming different bandwidth, computing and caching resources. Since diverse services have different QoS, we formulate a multi-objective programming (MOP) to optimize the allocation of the 3C resources for minimizing the total delay while maximizing the number of executed tasks requested by the mobile devices. We transform the MOP problem as a multi-objective Markov decision process (MO-MDP) and design a multi-objective proximal policy optimization (MO-PPO) algorithm to solve the MO-MDP. The proposed MO-PPO first trains two sub-policies separately for the two objectives, and then combines them to search for Pareto dominating solutions. By alternately perform the separate training and the combination, we can finally obtain a set of Pareto optimal solutions and the corresponding Pareto front. Simulation results show that the proposed MO-PPO outperforms traditional methods in finding a higher-quality set of Pareto optimal solutions and can more appropriately allocate 3C resources to different types of services.