面向移动边缘计算网络的高能效计算卸载算法

移动边缘计算（mobile edge computing,MEC）是一种高效的技术,通过将计算密集型任务从移动设备卸载到边缘服务器,使终端用户实现高带宽、低时延的目标.移动边缘计算环境下的计算卸载在减轻用户负载和增强终端计算能力等方面发挥着重要作用.考虑了服务缓存,提出一种云-边-端协同的计算卸载框架,在该框架中引入D2D （device-to-device,D2D）通信和机会网络.基于建立的模型,将计算卸载决策问题转化为一个混合整数非线性规划问题,并对无线特性和移动用户之间的非合作博弈交互制定了一个迭代机制来共同确定计算卸载方案.对提出的计算卸载算法从理论上证明了多用户计算卸载博弈模型为严格势力场博弈（exact potential game,EPG）,卸载决策可获得全网范围内的最优效益.考虑到服务器的计算资源、卸载任务数据量和任务延迟需求,提出对用户和MEC服务器之间最佳用户关联匹配算法.最后,模拟结果表明,卸载决策算法具有较快的收敛速度,并在能效方面优于其他基准算法.     

移动查询缓存处理的研究

客户缓存为提高客户／服务器数据库系统整体性能以及客户方数据可用性提供了有效途径。移动环境下网络资源的贫乏使客户缓存的作用更为重要,语义缓存是基于客户查询语义相关建立的一类缓存,提出一个基于语义缓存的客户缓存机制,给出缓存的内容组织,提出缓存项合并策略;然后讨论了基于语义缓存的查询处理策略;最后,模拟结果表明该客户缓存机制能够提高分布式、特别是移动环境下客户服务器数据库系统的性能。     

基于D2D通信的流量卸载技术综述

随着各类移动终端与高带宽需求业务的普及,蜂窝网流量已呈现出爆炸性增长的趋势;同时,由于网络用户分布不均匀,蜂窝网流量分布也存在不均衡的现象。基于D2D(Device-to-Device)通信的流量卸载(Traffic offloading)是解决数据流量急剧增加和不均匀分布的一种经济、有效的技术。首先,根据D2D通信在流量卸载中所承担的任务,将基于D2D通信的流量卸载技术分为以D2D通信为目标和以D2D通信为中继两类;然后,详细介绍每一类技术的研究现状并分析讨论各自的优缺点;最后,提出下一步的潜在研究方向。     

D2D网络中的缓存副本数布设算法

在Device-to-Device (D2D)缓存网络中,缓存文件的副本数量是制约系统缓存效率的重要因素,过多的副本会导致缓存资源不能得到充分利用,副本数过低又将使流行文件难以被有效获取。针对D2D缓存网络副本布设问题,以系统缓存命中率最大化为目标,利用凸规划理论,提出了一种缓存文件副本数布设算法(CRP)。仿真结果显示,与现有副本数量布设算法相比,该算法可以有效提升D2D缓存网络总体缓存命中率。     

内容中心移动边缘网络用户移动感知缓存机制

为了提高内容中心移动边缘网络的缓存性能,提出了一种基于用户移动性感知和节点中心性度量的内容中心移动边缘网络缓存机制（user mobility-aware and node centrality based caching,简称UMANCC）.UMANCC机制利用边缘节点计算节点中心性、缓存空闲率以及小区内用户逗留时间.移动边缘网络控制器综合各边缘节点的信息,计算各边缘节点的重要性并进行排序,最后根据排序结果选择内容缓存节点.仿真实验结果表明：与传统缓存机制LCE及Prob相比,UMANCC有效减少用户获取内容的平均跳数高达15.9%,提高边缘节点缓存命中率至少13.7%,减少进入核心网流量高达32.1%,有效地提高了内容中心移动边缘网络的内容分发性能.     

基于移动边缘计算的任务卸载优化

随着智慧物联体系的发展,物联网中应用程序的种类与数量不断增加.在移动边缘计算(mobile edge computing, MEC)中,通过允许移动用户将任务卸载至附近MEC服务器以加快移动应用程序的速度.本文通过考虑不同任务属性、用户的移动性和时间延迟约束模拟移动边缘场景.根据用户移动轨迹,将目标建模为寻找满足时延约束条件且在卸载过程中产生最小能耗MEC服务器优化模型,并提出一种最小能耗卸载算法求解该问题的最优解.仿真结果表明,在约束条件下,提出的算法可以找到在用户移动轨迹中产生最小能耗的MEC服务器,并显著降低任务卸载过程的能耗与时延,提高应用程序服务质量.     

移动机会网络中接触时间感知的协作缓存策略

数据访问效率一直都是移动机会网络研究中的一个备受关注的问题.协作缓存技术经常被用来提高网络数据访问效率,但是该类网络中节点的强自主移动性和有限的接触时间使得传统的缓存技术无法得到有效应用.首先提出一个新的度量节点在协作缓存中重要程度的指标,即节点重要度.基于此指标,利用贪心算法选择初始缓存节点;利用缓存节点相遇的机会,进行缓存数据的主动再分配.为了适应节点间有限的接触时间,提出了数据分片策略,并使用随机线性网络编码方法对分片进行编码,以解决数据恢复时的赠券收集问题.此外为每个节点确定了缓存边界来限制缓存数据量,从而合理利用缓存空间.最后,通过实验验证了提出的缓存协议能够有效地提高数据访问效率.     

Design a progressive video caching policy for video proxy servers

Proxy servers have been used to cache web objects to alleviate the load of the web servers and to reduce network congestion on the Internet. In this paper, a central video server is connected to a proxy server via wide area networks (WANs) and the proxy server can reach many clients via local area networks (LANs). We assume a video can be either entirely or partially cached in the proxy to reduce WAN bandwidth consumption. Since the storage space and the sustained disk I/O bandwidth are limited resources in the proxy, how to efficiently utilize these resources to maximize the WAN bandwidth reduction is an important issue. We design a progressive video caching policy in which each video can be cached at several levels corresponding to cached data sizes and required WAN bandwidths. For a video, the proxy server determines to cache a smaller amount of data at a lower level or to gradually accumulate more data to reach a higher level. The proposed progressive caching policy allows the proxy to adjust caching amount for each video based on its resource condition and the user access pattern. We investigate the scenarios in which the access pattern is priorly known or unknown and the effectiveness of the caching policy is evaluated.     

MobiCache: Cellular traffic offloading leveraging cooperative caching in mobile social networks

Offloading cellular traffic through mobile social networks has arisen as a promising way for relieving cellular networks. Prior studies mainly focused on caching data in a number of pre-selected helpers. However, such a strategy would fail when mobile users enter and leave the target area over time. In this paper, we examine the research decisions and design tradeoffs that arise when offloading cellular traffic in such a dynamic area of interest, referred to as a MobiArea, and we design an offloading framework, MobiCache, for maximizing cellular operators’ revenues and minimizing the overhead imposed on mobile devices. On the user side, we propose a content floating-based cooperative caching strategy that caches data in geographical floating circles, instead of selected helpers in previous studies, to cope with the dynamics. A geographical routing scheme is designed for delivering data and queries towards floating circles. We also develop a cache replacement scheme to improve caching cost-effectiveness inside floating circles. On the operator side, query history and feedback are maintained for cellular operators to optimize framework parameters that maximize their revenues. Extensive trace-driven simulations show that, compared with a state-of-the-art scheme, MobiCache offloads up to 52% more traffic with 15% shorter delay and 6% less forwarding cost.     

基于深度强化学习的移动边缘计算任务卸载研究

在移动边缘计算中,本地设备可以将任务卸载到靠近网络边缘的服务器上进行数据存储和计算处理,以此降低业务服务的延迟和功耗,因此任务卸载决策具有很大的研究价值.首先构建了大规模异构移动边缘计算中具有多服务节点和移动任务内部具有多依赖关系的卸载模型;随后结合移动边缘计算的实际应用场景,提出利用改进的深度强化学习算法优化任务卸载策略;最后通过综合比较任务卸载策略的能耗、成本、负载均衡、延迟、网络使用量和平均执行时间等指标,分析了各卸载策略的优缺点.仿真实验结果表明,基于长短期记忆(long short-term memory, LSTM)网络和事后经验回放(hindsight experience replay, HER)改进的HERDRQN算法在能耗、费用、负载均衡和延迟上都有很好的效果.另外利用各算法策略对一定数量的应用进行卸载,通过比较异构设备在不同CPU利用率下的数量分布来验证卸载策略与各评价指标之间的关系,以此证明HERDRQN算法生成的策略在解决任务卸载问题中的科学性和有效性.     

COPACC: An Architecture of Cooperative Proxy-Client Caching System for On-Demand Media Streaming

Proxy caching is a key technique to reduce transmission cost for on-demand multimedia streaming. The effectiveness of current caching schemes, however, is limited by the insufficient storage space and weak cooperation among proxies and their clients, particularly considering the high bandwidth demands from media objects. In this paper, we propose COPACC, a cooperative proxy-and-client caching system that addresses the above deficiencies. This innovative approach combines the advantages of both proxy caching and peer-to-peer client communications. It leverages the client-side caching to amplify the aggregated cache space and rely on dedicated proxies to effectively coordinate the communications. We propose a comprehensive suite of distributed protocols to facilitate the interactions among different network entities in COPACC. It also realizes a smart and cost-effective cache indexing, searching, and verifying scheme. Furthermore, we develop an efficient cache allocation algorithm for distributing video segments among the proxies and clients. The algorithm not only minimizes the aggregated transmission cost of the whole system, but also accommodates heterogeneous computation and storage constraints of proxies and clients. We have extensively evaluated the performance of COPACC under various network and end-system configurations. The results demonstrate that it achieves remarkably lower transmission cost as compared to pure proxy-based caching with limited storage space. On the other hand, it is much more robust than a pure peer-to-peer communication system in the presence of node failures. Meanwhile, its computation and control overheads are both kept in low levels     

多缓存容量场景下的D2D内容缓存布设优化方案

在终端直传（D2D）缓存网络中,用户有限且各异的缓存能力是制约缓存效率的一个关键参数,然而现有文献大多考虑不同用户具有相同的缓存能力,针对这一不足有必要进行用户具有不同缓存容量下的D2D内容缓存布设方案优化。首先,鉴于用户终端的移动性和随机分布特性,利用随机几何理论将网络中不同缓存容量的用户节点建模为相互独立的齐次泊松点过程;其次,考虑本地卸载和邻近D2D链路卸载两种内容卸载方式,推导得到网络缓存命中率;最后,将最大化缓存命中率作为优化目标函数,提出了基于坐标梯度的联合缓存布设（JCP）算法,从而得到多用户多缓存容量场景下的内容缓存布设方案。仿真结果表明,与现有的缓存布设方案相比,由JCP得到的缓存布设方案可以有效提高缓存命中率。     

面向5G/Beyond 5G的移动边缘缓存优化技术综述

随着移动设备和新兴移动应用的广泛使用,移动网络中流量的指数级增长所引发的网络拥塞、时延较大、用户体验质量差等问题无法满足移动用户的需求。边缘缓存技术通过对网络热点内容的复用,能极大缓解无线网络的传输压力;同时,该技术减少用户请求的网络时延,进而改善用户的网络体验,已经成为面向5G/Beyond 5G的移动边缘计算（MEC）中的关键性技术之一。围绕移动边缘缓存技术,首先介绍了移动边缘缓存的应用场景、主要特性、执行过程和评价指标;其次,对以低时延高能效、低时延高命中率及最大化收益为优化目标的边缘缓存策略进行了分析和对比,并总结出各自的关键研究点;然后,阐述了支持5G的MEC服务器的部署,并在此基础上分析了5G网络中的绿色移动感知缓存策略和5G异构蜂窝网络中的缓存策略;最后,从安全、移动感知缓存、基于强化学习的边缘缓存、基于联邦学习的边缘缓存以及Beyond 5G/6G网络的边缘缓存等几个方面讨论了边缘缓存策略的研究挑战和未来发展方向。     

移动边缘计算中基于改进拍卖模型的计算卸载策略

随着移动互联网业务的快速发展,增强现实、虚拟现实、超清视频等手机应用逐渐普及、IoT应用不断涌现,计算能力和续航能力的不足成为限制智能终端设备成功支撑这些应用的主要瓶颈。针对这一现状,采用计算卸载的方式解决该问题,在多用户多移动边缘服务器的场景下,综合考虑智能设备性能和服务器资源提出了一种基于改进拍卖算法的计算卸载策略。该策略主要包括两个阶段,在卸载决策阶段,通过综合考虑计算任务自身大小、计算需求和服务器计算能力、网络带宽等因素提出了卸载决策的依据;在任务调度阶段,通过综合考虑计算任务的时间需求和MEC服务器计算性能提出了基于改进拍卖算法的任务调度模型。实验证明,提出的计算卸载策略能够有效地降低服务时延,减少智能设备能耗,改善用户体验。     

Collaborative caching for efficient dissemination of personalized video streams in resource constrained environments

The ever increasing deployment of broadband networks and simultaneous proliferation of low-cost video capturing and multimedia-enabled mobile devices such as smart cellular phones, netbook computers, and tablet computers have triggered a wave of novel mobile multimedia applications making video streaming on mobile devices increasingly popular and commonplace. Networked environments consisting of mobile devices tend to be highly heterogeneous in terms of client-side and system-wide resource constraints, clients’ queries for information, geospatial distribution, and dynamic trajectories of the mobile clients, and client-side and server-side privacy and security requirements. Invariably, the video streams need to be personalized to provide a resource-constrained mobile device with video content that is most relevant to the client’s request while simultaneously satisfying the client-side and system-wide resource constraints, privacy and security requirements and the constraints imposed by the geospatial distribution and dynamic trajectories of the mobile clients relative to the server(s). In this paper, we present the design and implementation of a distributed system, consisting of several geographically distributed video personalization servers and proxy caches, for efficient dissemination of personalized video in a resource-constrained mobile environment. With the objective of optimizing cache performance, a novel cache replacement policy and multi-stage client request aggregation strategy, both of which are specifically tailored for personalized video content, are proposed. A novel Latency-Biased Collaborative Caching (LBCC) protocol based on counting Bloom filters is designed for further enhancing the scalability and efficiency of disseminating personalized video content. The benefits and costs associated with collaborative caching for disseminating personalized video content to resource-constrained and geographically distributed clients are analyzed and experimentally verified. The impact of different levels of collaboration among the caches and the advantages of using multiple video personalization servers with varying degrees of mirrored content on the efficiency of personalized video delivery are also studied. Experimental results demonstrate that the proposed collaborative caching scheme, coupled with the proposed personalization-aware cache replacement and client request aggregation strategies, provides a means for efficient dissemination of personalized video streams in resource-constrained environments.     

考虑内容异构5G无线网络云对边混合缓存策略

现有大多数内容缓存算法需要对内容流行度的准确估计，这在动态移动网络环境中是较难实现的。提出考虑内容异构5G无线网络云对边混合缓存策略，设计优化了内容缓存位置，其可以是原始内容服务器、云单元（CUs）和基站（BSs）。采用Lyapunov优化方法解决了NP-hard缓存控制问题与CU缓存和BS缓存控制决策之间的紧密耦合问题，有助于改善和识别网络体系结构的层次性和Cus缓存与BSs缓存之间的隶属关系，同时新的分层网络架构能够通过机会性地开发以云为中心和以边缘为中心的缓存来提高内容缓存性能，支持高平均请求的内容数据速率。采用李雅普诺夫优化技术，可实现恒定分数的容量区域的所有到达率的有限服务延迟，进而实现缓存数据的快速读取。仿真结果显示，所提缓存策略在平均端到端服务延迟和负载降低率方面具有较为显著的优势。     

移动边缘计算中的无人机三维部署和内容缓存优化方法

随着无线网络中的移动数据流量爆炸式增长,支持高速缓存的无人机被应用于移动计算领域充当边缘服务器,为网络中的用户提供按需服务。为了在满足其他资源约束的条件下,给用户带来更好的体验,通过联合优化无人机部署、缓存放置和用户关联以实现最小化所有用户的内容访问时延,并为用户提供质量不同的内容缓存服务。针对多无人机和地面基站协同提供缓存服务的场景,提出了一种基于迭代优化的联合优化算法。该算法通过迭代求解由目标问题分解得到的三个子问题的方式来获得具有收敛性保证的次优解决方案。首先,采用基于连续凸近似的算法求解无人机部署子问题;其次,采用基于贪心的算法求解内容缓存子问题;然后,利用基于罚函数的连续凸近似算法求解用户关联子问题;最后,对上述过程重复迭代,得到目标问题的一个次优解。多次仿真实验验证了所提算法的有效性和可行性。仿真结果表明,与基准算法相比,所提联合优化算法在平均内容访问时延、缓存命中率两方面均具有更好的性能。     

A weighted segment-based caching algorithm for video streaming objects over heterogeneous networking environments

Traditional caching technology is not applicable to cache video streaming objects over heterogeneous networking environments. The popularity of mobile devices in the heterogeneous networking environments make the access of Internet content become a common phenomenon. To support different mobile devices in the heterogeneity networking environments, a transcoding proxy is used to transcode different versions of the streaming videos according to clients’ requests. In this paper, we propose a weighted caching replace strategy for video streaming objects over heterogeneous networking environments. A new caching algorithm with static weight transcoding graph and dynamic caching relation tree is introduced. The proposed algorithm is compared with LRU, LFU, CP and PF cache algorithms in three parts: hit ratio, byte hit ratio, and average transmission delay. Experimental results show that the proposed algorithm outperforms than traditional LRU, LFU, CP and PF cache algorithms.     

基于多协议融合的实时移动视频监控系统

提出一种支持多协议融合的实时视频监控系统的设计与实现方案.系统可通过 Hi3520嵌入式处理器平台和移动端摄像头进行视频采集,采用H.264/AVC压缩编码和参数优化,兼容RTP/RTSP和RTMP多种通信协议进行媒体流传输;设计的Android的移动客户端可以实现视频数据的实时解析、网络串流等.实验测试表明： PC和采集端显示的画面仅有1~1.5秒延迟,系统可以灵活实现不同应用场景下的高清、实时监控.     

面向5G车联网场景的移动任务动态卸载策略研究

由于车辆自身的高速移动性和资源有限性等特征,使得采用传统通信和计算手段的车联网场景无法满足用户日益增长的数据计算需求和体验质量需求。采用5G和边缘计算技术构建的新型车联网架构可以满足以上需求,但由于网络结构的变化,需设计适合新场景下的车辆任务通信和计算策略。针对5G车联网场景下的移动车辆任务动态卸载问题进行研究,提出了对应的动态任务分配策略和卸载调度低时延算法。车辆会根据提出的策略和算法将未完成的计算任务卸载到相应的 MEC 服务器或车辆上,并且计算结果将通过边缘服务器之间的联合通信或直接从被选择接受卸载任务的附近空闲车辆上直接返回给车主。仿真结果表明,所提出的策略和算法在优化卸载延迟方面具有良好的性能,并提高了用户体验质量。