关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) pushes computing resources to the edge of the network and distributes them at the edge of the mobile network. Offloading computing tasks to the edge instead of the cloud can reduce computing latency and backhaul load simultaneously. However, new challenges incurred by user mobility and limited coverage of MEC server service arise. Services should be dynamically migrated between multiple MEC servers to maintain service performance due to user movement. Tackling this problem is nontrivial because it is arduous to predict user movement, and service migration will generate service interruptions and redundant network traffic. Service interruption time must be minimized, and redundant network traffic should be reduced to ensure service quality. In this paper, the container live migration technology based on prediction is studied, and an online prediction method based on map data that does not rely on prior knowledge such as user trajectories is proposed to address this challenge in terms of mobility prediction accuracy. A multitier framework and scheduling algorithm are designed to select MEC servers according to moving speeds of users and latency requirements of offloading tasks to reduce redundant network traffic. Based on the map of Beijing, extensive experiments are conducted using simulation platforms and real-world data trace. Experimental results show that our online prediction methods perform better than the common strategy. Our system reduces network traffic by 65% while meeting task delay requirements. Moreover, it can flexibly respond to changes in the user's moving speed and environment to ensure the stability of offload service.

关键词： Mobile edge computing  

摘要： Resource allocation is investigated for offloading computational-intensive tasks in multi-hop mobile edge computing (MEC) system. The envisioned system has both the cooperative access points (AP) with the computing capability and the MEC servers. A user-device (UD) therefore first uploads a computing task to the nearest AP, and the AP can either locally process the received task or offload to MEC server. In order to utilize the radio resource blocks (RRBs) in the APs efficiently, we exploit the non-orthogonal multiple access for offloading the tasks from the UDs to the AP(s). For the considered NOMA-enabled multi-hop MEC computing system, our objective is to minimize both the latency and energy consumption of the system jointly. Towards this goal, a joint optimization problem is formulated by taking the offloading decision of the APs, the scheduling among the UDs, RRBs, and APs, and UDs' transmit power allocation into account. To solve this problem efficiently, (i) a conflict graphbased approach is devised that solves the scheduling among the UDs, APs, and RRBs, the transmit power control, and the APs' computation resource allocation jointly, and (ii) a low-complexity pruning graph-based approach is also devised. The efficiency of the proposed graph-based approaches over several benchmark schemes is verified via extensive simulations. © 2021, CC BY.

关键词： Task-offloading   Particle swarm optimization   Internet of things   Mobile edge computing   Internet of Vehicle  

摘要： As a technology integrated with Internet of things, mobile edge computing (MEC) can provide real-time and low-latency services to the underlying network and improve the storage and computation ability of the networks instead of central cloud infrastructure. In mobile edge computing-based Internet of Vehicle (MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for MEC-IoV is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

关键词： Vehicular edge computing   Mobile edge computing   Vehicular cloud computing   Vehicular adhoc network   Task offloading  

摘要： Vehicular edge computing (VEC) is a potential field that distributes computational tasks between VEC servers and local vehicular terminals, hence improve vehicular services. At present, vehicles' intelligence and capabilities are rapidly improving, which will likely support many new and exciting applications. The network resources are well-utilized by exploiting neighboring vehicles' available resources while mitigating the VEC server's heavy burden. However, due to the vehicles' mobility, network topology, and the available computing resources change rapidly, which are difficult to predict. To tackle this problem, we investigate the task offloading schemes by utilizing vehicle to vehicle and vehicle to infrastructure communication modes and exploiting the vehicle's under-utilized computation and communication resources, and taking the cost and time consumption into account. We present a promising relay task-offloading scheme in vehicular edge computing (RVEC). According to this scheme, the tasks are offloaded in a vehicle to vehicle relay for computation while being transmitted to VEC servers. Numerical results illustrate that the RVEC scheme substantially enhances the network's overall offloading cost.

关键词： Mobile edge computing   Edge service   Service instance selection   Genetic algorithm   Response time  

摘要： Mobile edge computing (MEC) has been proposed to significantly reduce the response time of service invocations for end users. In MEC environment, a service provider can create multiple instances from a service and deploy them to different hired edge servers, where the deployed instances can be selected and invoked to decrease the network latency by nearby users. However, service instance selection in MEC is a challenging research problem from threefold aspects. First, the limitations of an edge server in terms of computation capacity and coverage range result in serving for only a certain number of users at the same time. Second, due to variable geographical locations from user mobility paths in MEC, the mobility of edge users is highly related to data transmission rate and affects the delay of service invocations. Furthermore, when many users in an edge server covered region request the same service instance at the same time, they interfere with each other and may reduce the experience of service invocations if there is no effective strategy to distribute these requests to appropriate instances deployed on different edge servers. To improve the user experience on service invocations with a lower response time, we take the above three factors into account and model the service instance selection problem (SISP) in MEC as an optimization problem, and propose a novel genetic algorithm-based approach with a response time-aware mutation operation with normalization for service instance selection called GASISMEC to find approximately optimal solution. Extensive experiments are conducted on two widely-used real-world datasets. The results demonstrate that our approach significantly outperforms the six baseline competing approaches. (C) 2021 Elsevier B.V. All rights reserved.

关键词： utility   approximation algorithm   quality of experience   mobile edge computing  

摘要： Mobile-edge computing casts the computation-intensive and delay-sensitive applications of mobile devices onto network *** offloading incurs extra communication latency and energy cost,and extensive efforts have focused on offloading *** metrics of the system utility are defined to achieve satisfactory quality of ***,most existing works overlook the balance between throughput and *** study investigates the problem of finding an optimal offloading scheme in which the objective of optimization aims to maximize the system utility for leveraging between throughput and *** on Karush-Kuhn-Tucker condition,the expectation of time complexity is analyzed to derive the optimal scheme.A gradient-based approach for utility-aware task offloading is ***,we provide an increment-based greedy approximation algorithm with 1+1/(e-1)*** results show that the proposed algorithms can achieve effective performance in utility and accuracy.

关键词： utility   approximation algorithm   quality of experience   mobile edge computing  

摘要： Mobile-edge computing casts the computation-intensive and delay-sensitive applications of mobile devices onto network *** offloading incurs extra communication latency and energy cost,and extensive efforts have focused on offloading *** metrics of the system utility are defined to achieve satisfactory quality of ***,most existing works overlook the balance between throughput and *** study investigates the problem of finding an optimal offloading scheme in which the objective of optimization aims to maximize the system utility for leveraging between throughput and *** on Karush-Kuhn-Tucker condition,the expectation of time complexity is analyzed to derive the optimal scheme.A gradient-based approach for utility-aware task offloading is ***,we provide an increment-based greedy approximation algorithm with 1+1/(e-1)*** results show that the proposed algorithms can achieve effective performance in utility and accuracy.

关键词： Mobile edge computing  

摘要： In the big data cloud environment, more and more attention has been paid to the security problems faced by wireless sensor networks based on edge computing and the server service processing. This paper focuses on the deployment of a wireless edge network edge node server, business local distribution problem, and the security policy of an edge network base station node server based on the trust mechanism. In this paper, we synthesize the relevant achievements and models of data security on the existing wireless edge computing network, analyze the existing research results, and make use of the advantages of analytical methods. As for the security problem between the wireless edge network node servers, we establish a complete trust mechanism for the base station node based on mobile edge computing, which provides a certain scientific theoretical basis as well as guidance for mobile edge computing applications.

关键词： Internet of things   5G   mobile edge computing   software-defined networking   device-to-device   scalability  

摘要： The Internet of things (IoT) is the third evolution of the traditional Internet that enables interaction and communication among machines. Many IoT platforms and networks have been developed, and recently, market sectors have started to develop specific IoT applications and services. Integrating heterogeneous IoT networks with the existing ones, mainly with the cellular networks, is a great demand. IoT represents one of the main use cases of the fifth-generation (5G) cellular system as announced by the 3rd Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU). Integrating IoT networks with 5G networks face many challenges related to dense deployment and a massive number of expected connected devices. Thus, IoT network availability and scalability are the main requirements that should be achieved. To this end, this work provides a framework for integrating heterogeneous IoT networks with the 5G networks. The proposed system considers dense deployment and system scalability and availability requirements as announced by ITU and 3GPP. Our proposed structure deploys three main communication paradigms;mobile edge computing (MEC), device-to-device communications (D2D), and software-defined networking (SDN). Our proposed system is evaluated over a reliable environment for various deployment scenarios, and the results validate the proposed structure. The proposed IoT/5G reduces the percentage of blocked tasks by an average of 30% than other traditional IoT networks. This increases the overall system availability and scalability since IoT networks can have more devices and tasks than existing IoT networks. Furthermore, our proposed structure reduces the overall consumed energy by an average of 20% than existing IoT networks, which is an effective metric for IoT networks.

关键词： Deep reinforcement learning   Mobile edge computing   Physical layer security  

摘要： Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. (C) 2021 Elsevier B.V. All rights reserved.