关键词： Mobile edge computing   computation offloading   task dependency   optimization problem   convex optimization  

摘要： This paper studies a single-user Mobile Edge Computing (MEC) system where mobile device (MD) includes an application consisting of multiple computation components or tasks with dependencies. MD can offload part of each computation-intensive latency-sensitive task to the AP integrated with MEC server. In order to accomplish the application faultlessly, we calculate out the optimal task offloading strategy in a time-division manner for a predetermined execution order under the constraints of limited computation and communication resources. The problem is formulated as an optimization problem that can minimize the energy consumption of mobile device while satisfying the constraints of computation tasks and mobile device resources. The optimization problem is equivalently transformed into solving a nonlinear equation with a linear inequality constraint by leveraging the Lagrange Multiplier method. And the proposed dual Bi-Section Search algorithm Bi-JOTD can efficiently solve the nonlinear equation. In the outer Bi-Section Search, the proposed algorithm searches for the optimal Lagrangian multiplier variable between the lower and upper boundaries. The inner Bi-Section Search achieves the Lagrangian multiplier vector corresponding to a given variable receiving from the outer layer. Numerical results demonstrate that the proposed algorithm has significant performance improvement than other baselines. The novel scheme not only reduces the difficulty of problem solving, but also obtains less energy consumption and better performance.

关键词： Mobile edge computing  

摘要： We consider the latency minimization problem in a task-offloading scenario, where multiple servers are available to the user equipment for outsourcing computational tasks. To account for the temporally dynamic nature of the wireless links and the availability of the computing resources, we model the server selection as a multi-armed bandit (MAB) problem. In the considered MAB framework, rewards are characterized in terms of the end-to-end latency. We propose a novel online learning algorithm based on the principle of optimism in the face of uncertainty, which outperforms the state-of-the-art algorithms by up to ∼1 s. Our results highlight the significance of heavily discounting the past rewards in dynamic environments. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing   Cloud computing   Collaborative caching   Internet of vehicles   Resource allocation   Deep deterministic policy gradient   Convergence speed  

摘要： In a high-speed free-flow scenario, a joint optimization scheme for content caching and resource allocation is proposed based on mobile edge computing in Internet of Vehicles. Vehicle trajectory prediction provides the basis for the realization of vehicle-cloud collaborative cache. By pre-caching the business data of requesting vehicles to edge cloud networks and oncoming vehicles, requesting vehicles can obtain data through V2V link and V2I link at the same time, which reduces the data acquisition delay. Therefore, this paper considers the situation where bandwidth of V2I and V2V link and the total amount of edge cloud caches are limited. Then, the bandwidth and cache joint allocation strategy to minimize the weighted average delay of data acquisition is studied. An edge cooperative cache algorithm based on deep deterministic policy gradient is further developed. Different from Q-learning and deep reinforcement learning algorithms, the proposed cache algorithm can be well applied to variable continuous bandwidth allocation action space. Besides, it effectively improves the convergence speed by using interactive iteration of value function and strategy function. Finally, the simulation results of vehicle driving path at the start and stop are obtained by analyzing real traffic data. Simulation results show that the proposed scheme can achieve better performance than several other newer cooperative cache schemes.

关键词： Mobile edge computing  

摘要： —Mobile edge computing (MEC) is emerging to support delay-sensitive 5G applications at the edge of mobile networks. When a user moves erratically among multiple MEC nodes, the challenge of how to dynamically migrate its service to maintain service performance (i.e., user-perceived latency) arises. However, frequent service migration can significantly increase operational cost, incurring the conflict between improving performance and reducing cost. To address these mis-aligned objectives, this paper studies the performance optimization of mobile edge service placement under the constraint of long-term cost budget. It is challenging because the budget involves the future uncertain information (e.g., user mobility). To overcome this difficulty, we devote to leveraging the power of prediction and advocate predictive service placement with predicted near-future information. By using two-timescale Lyapunov optimization method, we propose a T -slot predictive service placement (PSP) algorithm to incorporate the prediction of user mobility based on a frame-based design. We characterize the performance bounds of PSP in terms of cost-delay trade-off theoretically. Furthermore, we propose a new weight adjustment scheme for the queue in each frame named PSP-WU to exploit the historical queue information, which greatly reduces the length of queue while improving the quality of user-perceived latency. Rigorous theoretical analysis and extensive evaluations using realistic data traces demonstrate the superior performance of the proposed predictive schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： With the development of smart vehicles, computing-intensive tasks are widely and rapidly generated. To alleviate the burden of on-board CPU, connected vehicles can offload tasks to or make request from nearby edge server thanks to the emerging Mobile Edge Computing (MEC). However, such approach may sharply increase the workload of an edge server, and cause network congestion, especially in rural and mountain areas where there are few edge servers. To this end, a UAV-assisted MEC system is proposed in this paper, and joint optimization algorithm of the deployment and resource allocation of UAVs (JOAoDR) is proposed to decide the location and balance the resource and rewards of the UAVs. We solve a long-term profit maximization problem in terms of the operator. Numerical results demonstrated that our algorithm outperforms other benchmarks algorithm, and validated our solution. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) and next-generation mobile networks are set to disrupt the way intelligent and autonomous systems are interconnected. This will have an effect on a wide range of domains, from the Internet of Things to autonomous mobile robots. The integration of such a variety of MEC services in a inherently distributed architecture requires a robust system for managing hardware resources, balancing the network load and securing the distributed applications. Blockchain technology has emerged a solution for managing MEC services, with consensus protocols and data integrity checks that enable transparent and efficient distributed decision-making. In addition to transparency, the benefits from a security point of view are evident. Nonetheless, blockchain technology faces significant challenges in terms of scalability. In this chapter, we review existing consensus protocols and scalability techniques in both well-established and next-generation blockchain architectures. From this, we evaluate the most suitable solutions for managing MEC services and discuss the benefits and drawbacks of the available alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Vehicular edge computing   Task offloading   Mobility   Mobile edge computing   Vehicular networks  

摘要： Vehicular edge computing (VEC) is a promising paradigm to offload resource-intensive tasks at the network edge. Owing to time-sensitive and computation-intensive vehicular applications and high mobility scenarios, cost-efficient task offloading in the vehicular environment is still a challenging problem. In this paper, we study the partial task offloading problem in vehicular edge computing in an urban scenario. Where the vehicle computes some part of a task locally, and offload the remaining task to a nearby vehicle and to VEC server subject to the maximum tolerable delay and vehicle's stay time. To make it cost-efficient, including the cost of the required communication and computing resources, we consider to fully exploit the vehicular available resources. We estimate the transmission rates for the vehicle to vehicle and vehicle to infrastructure communication based on practical assumptions. Moreover, we present a mobility-aware partial task offloading algorithm, taking into account the task allocation ratio among the three parts given by the communication environment conditions. Simulation results validate the efficient performance of the proposed scheme that not only enhances the exploitation of vehicular computation resources but also minimizes the overall system cost in comparison to baseline schemes.

关键词： resource allocation   convex programming   power control   computational complexity   iterative methods   mobile computing   task offloading   load balancing   MEC networks   smart mobile devices   SMDs   low-latency tasks   mobile edge computing   nearby MEC servers   optimisation problem   weighted sum   energy consumption   multiMECS-multiSMD network   multidimensional optimisation   low complexity heuristic algorithm   offloading strategies   computation resource allocation subproblem   transmit power control subproblem   convex programming problem   joint optimisation   energy reduction  

摘要： To prolong the time duration of smart mobile devices (SMDs) or enable low-latency tasks, mobile edge computing (MEC) has emerged as a promising paradigm by offloading tasks to nearby MEC servers (MECSs). In this study the authors propose an optimisation problem to minimise the weighted sum of the total delay and energy consumption of all SMDs in a multi-MECS-multi-SMD network via multi-dimensional optimisation on offloading strategy making, load balancing, computation resource allocation and transmit power control. Since the problem is NP-hard, the authors decompose it into three subproblems to solve. First, they propose a low complexity heuristic algorithm to obtain the offloading strategies while guaranteeing load balancing between the multiple MECSs. Then they solve computation resource allocation subproblem using Lagrange dual decomposition. Finally, employing fractional programming, the authors transform the transmit power control subproblem into a convex programming problem where the closed-form solution is obtained. The proposed simulation results verify the convergence of the proposed iterative algorithms, and demonstrate that the proposed joint optimisation could achieve good performance in both delay and energy reduction.

关键词： Energy efficiency   Mobile edge computing   Offloading   Unmanned aerial vehicle  

摘要： The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different

关键词： Hate content   Mobile edge computing   Framework   Regulation   Social media  

摘要： Mobile devices with social media applications are the prevalent user equipment to generate and consume digital hate *** objective of this paper is to propose a mobile edge computing architecture for regulating and reducing hate content at the user's *** this regard,the profiling of hate content is obtained from the results of multiple studies by quantitative and qualitative *** resulted in different categories of hate content caused by gender,religion,race,and *** on this information,an architectural framework is developed to regulate and reduce hate content at the user's level in the mobile computing *** proposed architecture will be a novel idea to reduce hate content generation and its impact.