Algorithm design on energy efficiency maximization for UAV-assisted edge computing

For the unmanned aerial vehicle (UAV)-assisted edge computing system,a two-stage alternative algorithm was proposed to solve the formulated complex non-convex problem.Firstly,the formulated non-linear fractional programming problem was reformulated to the equivalent parametric problem by using Dinkelbach method.Secondly,two sub-problems were further considered based on it.By employing the Lagrange duality method,the closed-form solutions for the central processing unit frequencies and the number of data bits were derived.Finally,based on the solutions obtained,the conditions that the source node prefers to offload/share its data and the relay chooses to forward the computation results,as well as the approaches to achieve high energy efficiency were revealed.Numerical results demonstrate that the proposed design can achieve a performance improvement of up to 20 times over the conventional schemes.     

大规模无线通信网络移动边缘计算和缓存研究

面向未来6G移动通信的大规模网络移动边缘计算与缓存技术,首先,介绍了大规模无线网络下移动边缘计算和缓存的架构与原理,并阐释了移动边缘计算和缓存技术在大规模无线网络中的必要性和普适性.接着,从计算卸载、边缘缓存、多维资源分配、用户关联和隐私保护这5个关键问题出发,综述和分析了移动边缘计算和缓存赋能大规模无线网络时会引入的...     

User-centric energy efficiency fairness in backscatter-assisted wireless powered communication network

In order to address the unfair user-centric energy efficiency (EE) problem caused by channel difference in the backscatter-assisted wireless powered communication network,a resource allocation scheme was proposed.Firstly,a mixed integer nonconvex fractional programming problem was formulated to maximize the minimum user-centric EE,subject to the quality of service and energy-causality constraints.Based on the generalized fractional programming theory,the original problem was transformed into a mixed integer nonconvex subtraction problem.With the aid of the slack variable,the proof by contradiction,the auxiliary variable and the mixed integer nonconvex subtraction problem were further transformed into an equivalent convex problem.Finally,an iterative algorithm was proposed to obtain the optimal solutions.Computer simulations validated the quick convergence of the proposed iterative algorithm,and that the developed resource allocation scheme efficiently guarantees the fairness among users in terms of EE.     

车联网场景下移动边缘计算协作式资源分配策略

为了有效利用边缘云的计算资源,尽可能降低任务卸载时的平均等待时延,提出了一种满足边缘计算服务器容限阈值和任务卸载成功率约束条件下的多个边缘计算服务器相互协作的资源分配方案,通过单位时间总代价指标优化边缘计算服务器个数.将此方案建模为一个整数优化问题,之后设计了一种最小代价算法求解此优化问题,得到约束条件下的单位时间总代...     

Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing

The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance.     

Service function chain embedding algorithm with wireless multicast in mobile edge computing network

To resolve the excessive system overhead and serious traffic congestion in user-oriented service function chain (SFC) embedding in mobile edge computing (MEC) networks,a content-oriented joint wireless multicast and SFC embedding algorithm was proposed for the multi-base station and multi-user edge networks with MEC servers.By involving four kinds of system overhead,including service flow,server function sustaining power,server function service power and wireless transmission power,an optimization model was proposed to jointly design SFC embedding with multicast beamforming.Firstly,with Lagrangian dual decomposition,the problem was decoupled into two independent subproblems,namely,SFC embedding and multicast beamforming.Secondly,with the L_p norm penalty term-based successive convex approximation algorithm,the integer programming-based SFC embedding problem was relaxed to an equivalent linear programming one.Finally,the non-convex beamforming optimization problem was transformed into a series of convex ones via the path following technique.Simulation results revealed that the proposed algorithm has good convergence,and is superior to both the optimal SFC embedding with unicasting and random SFC embedding with multicasting in terms of system overhead.     

一种基于无线携能通信的MEC与用户匹配算法

多接入边缘计算（Multi-access Edge Computing,MEC）和无线携能通信可有效提高用户的服务质量和体验。在计算、通信和能量等资源的约束条件下,用户匹配是优化MEC任务卸载时系统效用的重要方法。针对无线携能通信的MEC网络结构,综合考虑用户的需求差异性和多元化能量供给,建立了基于计算资源、传输资源和能量资源的系统效用函数;以系统效用最大化为目标,采用基于多维背包理论的多轮拍卖,提出了一种适用于多用户和多网络边缘服务器的用户匹配算法。仿真验证了所提用户匹配算法的有效性与可靠性,结果表明所提匹配算法可优化系统资源配置,有效提高整体性能。     

无线mesh网络中可信协同信道资源分配策略

为了有效提升无线mesh网络信道资源的利用率和网络服务质量,提出基于可信协同的信道资源分配策略.针对节点自适应特点,引入博弈理论、建立节点的信誉机制以实现节点可信协同并优化信道分配结果.仿真实验分别对节点服务等级、网络收益结果作相应评价.实验结果发现节点服务等级对节点网络收益有直接影响,当协同服务等级达到3时,网络收益状况最佳,此时节点跳数与服务等级呈协同关系;对比经典协同算法,在相同网络拓扑环境下,可信协同信道资源分配策略分别是UACRR算法、DMP-MBA算法的1.04倍、1.069倍,明显占优.     

无线网络虚拟化中资源共享的功率分配算法

针对传统无线网络中功率不能动态分配共享的问题,采用无线网络虚拟化,设计了一种基于博弈的两阶段功率分配方法(G2SPA, game theory based two steps power allocation scheme for wireless network virtualization),首先利用买卖博弈模拟了服务提供商(SP, service provide)和移动用户(MUE, mobile user equipment)之间的相互影响,提出了基于斯坦博格均衡(SE, stackelberg equilibrium)的报价策略。然后,利用拍卖理论对空闲下行功率资源进行再分配,采取McAfee机制保证拍卖的诚实性。通过仿真实验证明G2SPA算法的正确性和有效性。     

基于拉格朗日的计算迁移能耗优化策略

随着移动网络技术的发展和智能终端的普及应用,移动边缘计算已成为云计算的一个重要应用。计算迁移策略已成为移动边缘计算服务的关键问题之一。以移动终端总的计算时间和移动终端能耗最小化为目标,将移动终端的计算迁移资源划分问题建模为一个凸优化问题,运用拉格朗日乘子法进行求解,提出基于阈值的迁移优化策略模型。仿真实验表明,本迁移优化策略模型能有效平衡本地计算和迁移计算之间的关系,为移动边缘计算中执行计算密集型应用提供保障。     

Enhancing live virtual machine migration process via optimized resource allocation in next generation mobile edge network: A hybrid evolutionary approach

The evolution of 5th Generation wireless technology introduced Mobile Edge Computing, where edge servers are placed at the edge of the network, and are associated with evolved Node Base Stations (eNBs). This enables mobile users to offload their resource‐intensive tasks to these servers and improve network performance by reducing end‐to‐end delay. However, frequent user mobility leads to frequent re‐planning of network and increases network load. This demands dynamic Virtual Machine (VM) migration in Mobile Edge paradigm for an improved Quality of Service (QoS). For an enhanced VM migration process, an optimal pair of migrating VMs and destination edge servers needs to be chosen. In this paper, we propose an optimized decision‐making policy that chooses such optimal pairs. Several decision parameters such as average wait time, processing delay, migration delay, transmission power, and processing power are modeled. A profit function is developed using these modeled decision parameters that chooses the optimal pairs. This function is maximized using the proposed hybrid evolutionary algorithm, which combines the advantages of PSO and GA. The pairs are chosen in such a manner, that the selection guarantees high network throughput, reduced service delay, and energy consumption which is reflected in the simulation.     

Method of ciphertext retrieval in mobile edge computing based on block segmentation

Aiming at the efficiency of cloud computing ciphertext retrieval scheme,a method of ciphertext retrieval in mobile edge computing based on block segmentations was proposed.Firstly,the edge server was introduced to calculate the document similarity score,thereby the computational cost of cloud server was reduced and the processing efficiency of cloud server was improved.Secondly,most keywords that are not related to the query were filtered out by a method of block segmentations based on the MRSE scheme,thereby the efficiency of calculating the document similarity score was improved.Theoretical analysis and experimental results show that the solution is safe under the known background threat model.Compared with the existing scheme,the proposed scheme has the same security and higher retrieval efficiency.     

Proactive content caching by exploiting transfer learning for mobile edge computing

To address the vast multimedia traffic volume and requirements of user quality of experience in the next‐generation mobile communication system (5G), it is imperative to develop efficient content caching strategy at mobile network edges, which is deemed as a key technique for 5G. Recent advances in edge/cloud computing and machine learning facilitate efficient content caching for 5G, where mobile edge computing can be exploited to reduce service latency by equipping computation and storage capacity at the edge network. In this paper, we propose a proactive caching mechanism named learning‐based cooperative caching (LECC) strategy based on mobile edge computing architecture to reduce transmission cost while improving user quality of experience for future mobile networks. In LECC, we exploit a transfer learning‐based approach for estimating content popularity and then formulate the proactive caching optimization model. As the optimization problem is NP‐hard, we resort to a greedy algorithm for solving the cache content placement problem. Performance evaluation reveals that LECC can apparently improve content cache hit rate and decrease content delivery latency and transmission cost in comparison with known existing caching strategies.     

PSMECS: A provably secure ID-based communication in mobile edge computing

Human-centered systems play an important role in the modern world, for example, driverless car, autonomous and smart vehicles, drones, and robotics. The internet of things environment demands a faster real-time response depending on the applications processed in a particular duration. Mobile edge computing (MEC) allows a user to get a real-time response as compared with cloud computing (CC), although ensuring a number of security attributes in MEC environment remains challenging. In this article, a protocol is designed to achieve mutual authentication, anonymous communication, and security against traceability, as these are very crucial factors to ensure the security of data and user's privacy. Moreover, the proposed scheme ensures mutual authentication between a mobile user and an edge server along with the user's anonymity and untraceability. The proof of security and evaluation of performance of the scheme validates that it ensures security attributes and improves efficiency in terms of communication and computation overheads.     

Study on virtual resource allocation optimization in cloud computing environment

Most existing researches of resource allocation in data center did not take into full consideration how to decrease energy consumption.The energy efficiency virtual resource allocation for cloud computing as a multi-objective optimization problem was formulated,which was then solved by intelligent optimization algorithm.The simulation results reveal that the strategy can successfully generate schedule scheme of different numbers of servers-VM with diverse characteristics and decrease the total operating energy of data center effectively.     

基于移动边缘计算的虚拟现实压缩与传输系统

针对基于云服务的虚拟现实对数据传输速率的高要求以及对传输时延的敏感性问题,提出并设计了基于移动边缘计算技术的云VR系统方案,主要包括基于视点的VR处理和混合数模传输优化。首先采用基于金字塔投影的用户视点感知动态推流方法实现基于视点的VR处理;然后引入混合数模技术对传输进行优化改进,并给出了启发式的资源分配优化算法;最后在长期演进(LTE)系统的基础上改造基站协议栈,融入移动边缘计算技术,实现了完整的移动边缘云VR系统。实验结果表明,与已有方案相比,所提方案能够实现更加稳健高效的传输,达到更好的VR用户体验。     

A genetic‐based decision algorithm for multisite computation offloading in mobile cloud computing

Mobile cloud computing is a promising approach to improve the mobile device's efficiency in terms of energy consumption and execution time. In this context, mobile devices can offload the computation‐intensive parts of their applications to powerful cloud servers. However, they should decide what computation‐intensive parts are appropriate for offloading to be beneficial instead of local execution on the mobile device. Moreover, in the real world, different types of clouds/servers with heterogeneous processing speeds are available that should be considered for offloading. Because making offloading decision in multisite context is an NP‐complete, obtaining an optimal solution is time consuming. Hence, we use a near optimal decision algorithm to find the best‐possible partitioning for offloading to multisite clouds/servers. We use a genetic algorithm and adjust it for multisite offloading problem. Also, genetic operators are modified to reduce the ineffective solutions and hence obtain the best‐possible solutions in a reasonable time. We evaluated the efficiency of the proposed method using graphs of real mobile applications in simulation experiments. The evaluation results demonstrate that our proposal outperforms other counterparts in terms of energy consumption, execution time, and weighted cost model.     

Research on intelligent computing offloading model based on reputation value in mobile edge computing

Aiming at the problem of high-latency,high-energy-consumption,and low-reliability mobile caused by computing-intensive and delay-sensitive emerging mobile applications in the explosive growth of IoT smart mobile terminals in the mobile edge computing environment,an offload decision-making model where delay and energy consumption were comprehensively included,and a computing resource game allocation model based on reputation that took into account was proposed,then improved particle swarm algorithm and the method of Lagrange multipliers were used respectively to solve models.Simulation results show that the proposed method can meet the service requirements of emerging intelligent applications for low latency,low energy consumption and high reliability,and effectively implement the overall optimized allocation of computing offload resources.