基于图协同过滤模型的D2D协作缓存策略

针对设备到设备（D2D）缓存中基站信号覆盖范围有限导致的难以获得足够数据来预测用户偏好的问题,提出了一种基于图协同过滤模型的D2D协作缓存策略。首先,构建图协同过滤模型,通过多层图卷积神经网络捕捉用户-内容交互图中的高阶连通信息,并利用多层感知机学习用户和内容之间的非线性关系来预测用户偏好。其次,为了最小化平均访问时延,综合考虑用户偏好和缓存时延收益,将缓存内容放置问题建模为马尔可夫决策过程模型,设计基于深度强化学习的协作缓存算法进行求解。仿真实验表明,与现有的缓存策略相比,所提缓存策略在不同的内容种类、用户密度和D2D通信距离参数下均取得了最优的性能效果。     

带有特征感知的D2D内容缓存策略

设备到设备通信(D2D)可以有效地卸载基站流量,在D2D网络中不仅需要共享大众化内容还需要个性化内容缓存。该文对缓存内容选择问题进行了深入研究,提出一种结合特征感知的内容社交价值预测(CSVP)方法。价值预测不仅可以降低时延也可以减少缓存替换次数降低缓存成本。首先结合用户特征和内容特征计算内容当前价值,然后通过用户社交关系计算未来价值。微基站根据内容的价值为用户提供个性化内容缓存服务,宏基站则在每个微基站的缓存内容中选择价值较大部分的内容。仿真结果表明,该文提出的缓存策略可以有效缓解基站流量,与其他方法相比降低时延约20%～40%。     

P2P缓存系统中总开销最小的协作缓存策略

为降低P2P缓存系统中的全局开销,提出一种基于总开销最小的协作缓存策略。该策略综合考虑P2P缓存系统中的传输开销和存储开销,使用跨ISP域间链路开销、流行度、文件大小、存储开销来衡量文件的缓存增益。需要替换时,首先替换掉缓存增益最小的内容。实验结果表明,所提策略能够有效降低系统的总开销。     

雾无线接入网中基于内容流行度和信息新鲜度的缓存更新策略

将边缘缓存技术引入雾无线接入网,可以有效减少内容传输的冗余。然而,现有缓存策略很少考虑已缓存内容的动态特性。该文提出一种基于内容流行度和信息新鲜度的缓存更新算法,该算法充分考虑用户的移动性以及内容流行度的时空动态性,并引入信息年龄(AoI)实现内容的动态更新。首先,所提出算法根据用户的历史位置信息,使用双向长短期记忆网络(Bi-LSTM)预测下一时间段用户位置;其次,根据预测得到的用户位置,结合用户的偏好模型得到各位置区的内容流行度,进而在雾接入点进行内容缓存。然后,针对已缓存内容的信息年龄要求,结合内容流行度分布,通过动态设置缓存更新窗口以实现高时效、低时延的内容缓存。仿真结果表明,所提算法可以有效地提高内容缓存命中率,在保障信息的时效性的同时最大限度地减小缓存内容的平均服务时延。     

NDN中基于蚁群替换算法的邻居协作缓存管理策略

为提高NDN(命名数据网络)中的缓存利用率,提出了一种基于蚁群替换算法的邻居协作缓存管理(ACNCM)策略。首先将单节点的缓存替换问题,建模为0/1背包问题,并根据缓存数据的大小、使用频率以及邻居副本深度等信息定义本地存储内容的缓存价值,提出基于蚁群算法的缓存替换算法。然后利用邻域协作的思想,通过路由节点之间定期交换自身节点的缓存信息,对单个节点替换出去的缓存内容,选择邻居节点完成协作式缓存管理。实验结果表明,ACNCM策略在缓存命中率、网络开销和平均响应时延方面均优于现有方法。     

一种基于贪婪算法的内容中心网络数据包存储策略

内容中心网络(CCN)是未来互联网中一种有前景的网络架构。它通过网内缓存机制加强内容的传输减少网络传输代价或提高网络吞吐量。考虑传统的数据包缓存策略会使网络中冗余数据包增加从而降低网络性能,提出了一种基于贪婪算法的数据包存储策略--GBC策略,该策略能够有效地降低内容的冗余度。仿真结果表明:本文提出的缓存策略能有效提高网络的缓存命中率,降低用户获取内容的时延。     

面向用户感知的移动互联网缓存加速系统研究

针对上海电信移动网提升用户网站访问速度体验的要求,从用户HTTP(超文本传输协议)访问目的地址分布、源站拨测和用户感知APP数据分析的三个维度,论证在移动核心网中部署Web Cache(互联网缓存加速)系统必要性。对比分析从策略路由引流和DNS(域名系统)+HTTP重定向两种缓存方式的缓存效果、扩展性,然后指出:在移动网单独建设DNS+HTTP重定向缓存加速系统方案更加合理。     

移动边缘计算中基于联邦学习的视频请求预测和协作缓存策略

随着互联网社交平台的崛起和移动智能终端设备的普及,自媒体短视频、直播等视频业务蓬勃发展,人们对高质量视频服务的需求也急剧上升。与此同时,连接到核心网络的大量智能设备增加了回程链路的负载,传统的云计算难以满足用户对视频服务的低延迟要求。移动边缘计算(MEC)通过在网络边缘部署具有计算和存储能力的边缘节点,通过在更靠近用户的边缘侧提高计算和存储服务,降低了数据传输时延进而缓解了网络阻塞。因此,基于MEC架构,该文充分利用网络边缘资源,提出了基于联邦学习的视频请求预测和视频协作缓存策略。通过利用多个边缘节点对提出的深度请求预测模型(DRPN)视频请求预测模型进行联邦训练,预测视频未来的请求情况,然后量化缓存内容所带来的时延收益并协作地以最大化该时延收益为目的进行缓存决策。该文分析了真实数据集MovieLens,模拟了视频请求缓存场景并进行实验。仿真结果表明,相比于其他策略,所提策略不仅能有效降低用户等待时延,在有限的缓存空间中提高内容多样性,从而提高缓存命中率,降低缓存成本,还能降低整个系统的通信成本。     

内容中心网络中面向隐私保护的协作缓存策略

针对内容中心网络节点普遍缓存带来的隐私泄露问题,在兼顾内容分发性能的基础上,该文提出一种面向隐私保护的协作缓存策略。该策略从信息熵的角度提出隐私度量指标,以增大攻击者的不确定度为目标,首先对于缓存策略的合理性给予证明;其次,通过构建空间匿名区域,扩大用户匿名集合,增大缓存内容的归属不确定性。缓存决策时,针对垂直请求路径和水平匿名区域,分别提出沿途热点缓存和局域hash协同的存储策略,减小缓存冗余和隐私信息泄露。仿真结果表明,该策略可减小内容请求时延,提高缓存命中率,在提升内容分发效率的同时增强了用户隐私保护水平。     

一种基于传输成本的流媒体缓存替换算法及其性能评价

提出一种流媒体缓存替换算法—基于平均传输成本的最小缓存效用算法，并提出一组评价缓存替换算法有效性的性能指标：平均传输成本、中心服务器消耗的带宽和算法的计算开销。模拟结果表明，ATCB-SCU的传输成本和中心服务器消耗的平均带宽都比较低，其计算开销也较低。     

雾网络中基于统计分布的内容缓存与交付方案

作为5G中的一种重要模型，雾无线接入网络(Fog Radio Access Network, F-RAN)通过设备到设备通信和无线中继等技术获得了显著的性能增益，而边缘设备中合适的缓存则可以让内容缓存用户(Caching Users, CUs)向内容请求用户(Requesting Users, RUs)直接发送缓存内容，有效减小前传链路的负担和下载延迟。考虑一个F-RAN模型下用户发出请求并获得交付的场景，将每个CU的内容请求队列建模为独立的M/D/1模型，分析导出CUs缓存命中率和平均下载延迟关于内容缓存与交付方案的表达式，证明CUs缓存命中率与内容统计分布之间的联系有助于实现前者的近似最优解。针对在一段时间内的期望视角下建立的优化问题，提出了基于统计分布的算法并注意了执行时的交付控制。仿真结果表明，相较于现有缓存策略，优化内容整体统计分布的方案能够最大化CUs缓存命中率，同时减小平均下载延迟。     

Cooperative caching algorithm of UAV and user in UAV-assisted cellular network

Aiming at the problem of mass data content transmission and limited wireless backhaul resource of UAV in UAV-assisted cellular network,a cooperative caching algorithm for cache-enabled UAV and user was proposed.By deploying caches on UAV and user device,the popular content requested by user was cached and delivered,which alleviated the backhaul resource and energy consumption of UAV,reduced the traffic load and user delay.A joint optimization problem of UAV and user caching was established with the goal of minimizing user content acquisition delay,and decomposed into UAV caching sub-problem and user caching sub-problem,which were solved based on alternating direction method of multiplier and global greedy algorithm respectively.The iterative way was used to obtain convergent optimization result,and the cooperative caching of UAV and user was realized.Simulation results show that the proposed algorithm can effectively reduce user content acquisition delay and improve system performance.     

Cooperative caching game based on social trust for D2D communication networks

Content sharing via device‐to‐device (D2D) communications has become a promising method to increase system throughput and reduce traffic load. Due to the characteristic of spectrum sharing in D2D network, confidentiality is becoming a key issue in content transmission. Secure communication in D2D networks is generally guaranteed by a physical‐layer security mechanism. However, this method sacrifices the system transmission rate while ensuring security. Since mobile devices are carried by humans, we can leverage their trust relations to enhance the security of communications. As much, considering the psychology structure and social attributes of mobile users, we build a multidimensional trust evaluation mechanism to evaluate the trust relationship between users, and we pick out the trusted users based on the decision‐theoretic rough sets. By sharing content only between trust users, we can enhance the security of content transmissions without relying on physical‐layer security measures. Meanwhile, content caching is now widely used to improve accessing efficiency and reduce traffic load on cellular networks. However, caching content for other users incurs additional cost, which results in selfish and noncooperative behavior in users. Considering such selfishness, we introduce a cooperative caching game based on multidimensional trust relations to motivate users to cache contents for other devices. In this game, the trust relations and physical distance between two users are considered to formulate the cost function. Furthermore, we introduce an incentive caching algorithm based on social trust to minimize the total cost in the D2D network.     

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.     

QoE-aware video cooperative caching and transmission mechanism based on TWDM-PON and C-RAN

The quality of user experience suffers from performance deterioration dramatically due to the explosively growing data traffic.To improve the poor performance of cell-edge users and heavy-load cell users,which caused by dense network and load imbalance respectively,an QoE-aware video cooperative caching and transmission mechanism in cloud radio access network was proposed.Cooperative gain-aware virtual passive optical network was established to provide cooperative caching and transmission for video streaming by adopting collaborative approach in optical domain and wireless domain.Furthermore,user experience for video streaming,bandwidth provisioning and caching strategy were jointly optimized to improve QoE,which utilized the methods of dynamic caching in optical domain and buffer level-aware bandwidth configuration in wireless domain.The results show that the proposed mechanism enhances the quality of user experience and effectively improves the cache hit rate.     

Adaptive Delay Optimization of Multicast-Enabled Coded Caching in Device-to-Device Networks

This letter investigates a delay optimization problem in device-to-device(D2D)networks where users have pre-downloaded subfiles following a coded caching rule.Due to acquiring rest subfiles,users may suffer both receiving and transmitting delays.To achieve the delay minimization,we first propose a delay-aware mode-selection strategy to adaptively choose multicast or D2D communications mode to reduce the receiving-caused delay.By matching these transmit modes with distinct subfile sizes,we further formulate a min-max optimization problem to minimize the delivery delay.Finally,numerical results prove that the proposed scheme outperforms existing ones in terms of both receiving and transmitting delays.     

移动边缘计算中基于内容流行度的深度强化学习缓存机制

随着5G商用的推进,涌现出大量依赖高速率、低时延的新应用,混合现实(Mixed Reality,MR)就是其中之一.考虑到从中心云传输服务内容到MR设备会带来很大时延和能耗问题,引入移动边缘计算(Mobile Edge Compu?ting,MEC)技术,通过在MEC服务器上缓存用户的预渲染环境帧,以减少延迟和能耗.针...     

Dynamic theoretic proactive caching placement game for energy saving in H‐CRAN

Edge caching is an effective feature of the next 5G network to guarantee the availability of the service content and a reduced time response for the user. However, the placement of the cache content remains an issue to fully take advantage of edge caching. In this paper, we address the proactive caching problem in Heterogeneous Cloud Radio Access Network (H‐CRAN) from a game theoretic point of view. The problem is formulated as a bargaining game where the remote radio heads (RRHs) dynamically negotiate and decide which content to cache in which RRH under energy saving and cache capacity constraints. The Pareto optimal equilibrium is proved for the cooperative game by the iterative Nash bargaining algorithm. We compare between cooperative and noncooperative proactive caching games and demonstrate how the selfishness of different players can affect the overall system performance. We also showed that our cooperative proactive caching game improves the energy consumption of 40% as compared with noncooperative game and of 68% to no‐game strategy. Moreover, the number of satisfied requests at the RRHs with the proposed cooperative proactive caching scheme is significantly increased.     

Edge-first-based cooperative caching strategy in information centric networking

In order to optimize the replica placement in information centric networking,an edge-first-based cooperative caching strategy (ECCS) was proposed.According to the strategy,cache decision was made during the interest forwarding stage.The decision result and statistic information would been forwarded to upstream routers step by step.Utilizing the information,upstream nodes could update their cache information table immediately to achieve cooperative caching.The experimental results indicate ECCS can achieve salient performance gain in terms of server load reduction ratio,average hop reduction ratio,average cache hit ratio,compared with current strategies.