基于概率的启发式ICN缓存内容放置方法

提出一种基于概率存储的启发式住处中心网络内容缓存方法（PCP）。主要思想是请求消息和数据消息在传输过程中统计必要信息,当数据消息返回时,沿途各缓存节点按照一定概率决策是否在本地缓存该内容。设计缓存概率时综合考虑内容热度和缓存放置收益,即内容热度越高,放置收益越大的内容被缓存的概率越高。实验结果表明,PCP在缓存服务率、缓存命中率、平均访问延迟率等方面,与现有方法相比具有显著优势,同时PCP开销较小。     

Packet‐level‐based traffic aggregation to optimize NDN content delivery

In recent years, named data networking (NDN) has been accepted as the most popular future paradigm and attracted much attention, of which the routing model contains interest forwarding and content delivery. However, interest forwarding is far from the bottleneck of routing optimization; instead, the study on content delivery can greatly promote routing performance. Although many proposals on content delivery have been investigated, they have not considered packet‐level caching and deep traffic aggregation, which goes against the performance optimization of content delivery. In this paper, we propose a packet‐level‐based traffic aggregation (PLTA) scheme to optimize NDN content delivery. At first, the packet format is devised, and data plane development kit (DPDK) is used to ensure same size for each packet. Then, the whole delivery scheme with traffic aggregation consideration is presented. The simulation is driven by the real YouTube dataset over Deltacom, NSFNET, and CERNET topologies, and the experimental results demonstrate that the proposed PLTA has better delivery performance than three baselines in terms of cache hit ratio, delivery delay, network load, and energy efficiency.     

命名数据网络中基于内容请求相关性的协作缓存算法

针对命名数据网络(Named Data Networking, NDN)存储空间的有效利用和应答内容的高效缓存问题,该文采用差异化缓存的方式,提出一种依据内容请求序列相关性的协作缓存算法。在内容请求中,预先发送对于后续相关数据单元的并行预测请求,增大内容请求的就近响应概率;缓存决策时,提出联合空间存储位置与缓存驻留时间的2维差异化缓存策略。根据内容活跃度的变化趋势,空间维度上逐跳推进内容存储位置,时间维度上动态调整内容缓存时间,以渐进式的方式将真正流行的请求内容推送至网络边缘存储。该算法减小了内容请求时延和缓存冗余,提高了缓存命中率,仿真结果验证了其有效性。     

雾无线接入网中基于Q-learning的协作式内容缓存策略

为了提高雾无线接入网（Fog-Radio Access Networks,F-RAN）的边缘缓存效率,提出一种基于用户偏好预测和内容流行度预测的协作式内容缓存策略。首先,利用主题模型中隐含狄利克雷分布（Latent Dirichlet Allocation,LDA）模型动态的预测用户偏好;其次,利用网络中不同设备之间的拓扑关系和已预测的用户偏好以在线的方式预测内容流行度的变化,然后再结合基站之间的相关度,以减少缓存内容文件的重复率;最后,以最大化缓存命中率为目标,利用强化学习中的Q-learning算法获得了最优的内容缓存策略。仿真结果表明,与其他内容缓存策略相比,该内容缓存策略能有效的提高缓存命中率。      

Fast convergence caching replacement algorithm based on dynamic classification for content-centric networks

One of the key research fields of content-centric networking （CCN） is to develop more efficient cache replacement policies to improve the hit ratio of CCN in-network caching. However, most of existing cache strategies designed mainly based on the time or frequency of content access, can not properly deal with the problem of the dynamicity of content popularity in the network. In this paper, we propose a fast convergence caching replacement algorithm based on dynamic classification method for CCN, named as FCDC. It develops a dynamic classification method to reduce the time complexity of cache inquiry, which achieves a higher caching hit rate in comparison to random classification method under dynamic change of content popularity. Meanwhile, in order to relieve the influence brought about by dynamic content popularity, it designs a weighting function to speed up cache hit rate convergence in the CCN router. Experimental results show that the proposed scheme outperforms the replacement policies related to least recently used （LRU） and recent usage frequency （RUF） in cache hit rate and resiliency when content popularity in the network varies.     

An efficient in‐network caching decision algorithm for Internet of things

Recently, content‐centric networking (CCN) has become one of the important technologies for enabling the future networks. Along with its recognized potentialities as a content retrieval and dissemination solution, CCN has been also recently considered as a promising architecture for the Internet of things (IoT), because of 2 main features such as named‐based routing and in‐network caching. However, IoT is characterized by challenging features: small storage capacity of resource‐constrained devices due to cost and limitation of energy and especially transient data that impose stringent requirements on the information freshness. As a consequence, the intrinsic caching mechanisms existing in CCN approach do not well suit IoT domains; hence, providing a specific caching policy at intermediate nodes is a very challenging task. This paper proposes an effective multiattribute in‐network caching decision algorithm that performs a caching strategy in CCN‐IoT network by considering a set of crucial attributes including the content store size, hop count, particularly key temporal properties like data freshness, and the node energy level. Simulation results proved that our proposed approach outperforms 2 cache management schemes (probabilistic least recently used and AlwaysCache–first in first out in terms of improving total hit rate, reducing data retrieval delay, and enhancing content reusability in IoT environment).     

内容中心网络中能耗优化的隐式协作缓存机制

综合考虑内容中心网络(CCN)的能耗优化及性能提升,该文提出一种内容中心网络中能耗优化的隐式协作缓存机制。缓存决策时,利用缓存节能量作为判决条件优先在用户远端节点缓存,并利用数据包携带最近上游缓存跳数信息进行隐式协作,减轻用户近端节点缓存空间的竞争压力,提高邻近节点缓存的差异性。缓存替换时,选取缓存节能量最小的缓存内容加以替换,达到最优的能耗优化效果。仿真结果表明,该缓存机制在性能上获得较优的缓存命中率及平均路由跳数,同时有效降低了网络能耗。     

基于内容分块流行度分级的信息中心网络缓存策略

信息中心网络(ICN:Information-Centric Networking)域内缓存机制的研究大多假定单个内容的流行度相同,而忽视了同一内容的不同分块具有不同流行度的特性.本文提出了一种基于内容分块流行度以及缓存节点位置的分级缓存策略,通过兴趣包和数据包携带标签的方式实现隐式缓存协作.仿真实验证明相比于其他方案,该方案可以充分利用细粒度的内容分块流行度这一特性,提高缓存路由器缓存命中率,减小用户请求内容时延以及网络流量,进而提升用户对实时业务的服务体验.     

Coordinated caching scheme based on popular content awareness and tracking

To overcome the problems of the on-path caching schemes in the content centric networking,a coordinated caching scheme based on the node with the max betweenness value and edge node was designed.According to the topol-ogy characteristics,the popular content was identified at the node with the max betweenness value and tracked at the edge node.The on-path caching location was given by the popularity and the cache size.Simulation results show that,com-pared with the classical schemes,this scheme promotes the cache hit ratio and decreases the average hop ratio,thus en-hancing the efficiency of the cache system.     

Cooperative caching for content dissemination in vehicular networks

The explosive growth of mobile data traffic has made cellular operators to seek low‐cost alternatives for cellular traffic off‐loading. In this paper, we consider a content delivery network where a vehicular communication network composed of roadside units (RSUs) is integrated into a cellular network to serve as an off‐loading platform. Each RSU subjecting to its storage capacity caches a subset of the contents of the central content server. Allocating the suitable subset of contents in each RSU cache such that maximizes the hit ratio of vehicles requests is a problem of paramount value that is targeted in this study. First, we propose a centralized solution in which, we model the cache content placement problem as a submodular maximization problem and show that it is NP‐hard. Second, we propose a distributed cooperative caching scheme, in which RSUs in an area periodically share information about their contents locally and thus update their cache. To this end, we model the distributed caching problem as a strategic resource allocation game that achieves at least 50% of the optimal solution. Finally, we evaluate our scheme using simulation for urban mobility simulator under realistic conditions. On average, the results show an improvement of 8% in the hit ratio of the proposed method compared with other well‐known cache content placement approaches.     

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。     

基于网络代价的流媒体缓存策略研究

该文构造了一种新的流媒体缓存效用函数,该函数综合考虑流媒体节目的流行度特性及传输网络的代价参数;设计了一种针对多视频服务器、基于网络代价的流媒体缓存分配与替换算法(Network Cost Based cache allocation and replacement algorithm, NCB)。仿真实验结果显示,NCB算法有效提高了缓存命中率,降低了传送流媒体所消耗的总体网络代价;该算法在网络结构复杂、节目数量庞大的Internet流媒体应用环境中表现出较优越的性能。     

COACS: A Cooperative and Adaptive Caching System for MANETs

This paper introduces a cooperation-based database caching system for Mobile Ad Hoc Networks (MANETs). The heart of the system is the nodes that cache submitted queries. The queries are used as indices to data cached in nodes that previously requested them. We discuss how the system is formed and how requested data is found if cached, or retrieved from the external database and then cached. Analysis is performed and expressions are derived for the different parameters, including upper and lower bounds for the number of query caching nodes as well as the average load they experience, generated network traffic, node bandwidth consumption, and other performance-related measures. Simulations with the ns-2 software were used to study the performance of the system in terms of average delay and hit ratio, and to compare it with the performance of two other caching schemes for MANETs, namely CachePath and CacheData. The results demonstrate the effectiveness of the proposed system in terms of achieved hit ratio and low delay.     

A unifying co‐operative web caching architecture

Network caching of objects has become a standard way of reducing network traffic and latency in the web. However, web caches exhibit poor performance with a hit rate of about 30%. A solution to improve this hit rate is to have a group of proxies form co‐operation where objects can be cached for later retrieval. A co‐operative cache system includes protocols for hierarchical and transversal caching. The drawback of such a system lies in the resulting network load due to the number of messages that need to be exchanged to locate an object. This paper proposes a new co‐operative web caching architecture, which unifies previous methods of web caching. Performance results shows that the architecture achieve up to 70% co‐operative hit rate and accesses the cached object in at most two hops. Moreover, the architecture is scalable with low traffic and database overhead. Copyright © 2002 John Wiley & Sons, Ltd.     

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.     

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。     

基于内容流行度差异性的CDN-P2P融合分发网络缓存替换机制研究

现有的CDN-P2P缓存替换机制没有关注内容文件中各片段的个体流行度差异性,而无法提高预缓存内容片段的访问命中率。鉴于此,提出了基于流行度差异性的缓存替换机制Diff-Attribute。同时考虑了内容文件的整体流行度和文件中各个片段的个体流行度。此外,基于分布熵,定义了一种内容流行度均衡性度量方法：若流行度均衡,就提前缓存各文件的前缀片段;否则提前缓存热门文件或其中最热门的内容片段。仿真结果表明：在缓存命中率和字节命中率方面,Diff-Attribute机制分别高出LFU、LRU等传统机制约6%和8%;在访问延迟启动率和传输成本消耗率方面,Diff-Attribute机制则降低了约13%和7%。     

Modeling and implementation of 5G edge caching over satellite

The fifth‐generation (5G) wireless networks have to deal with the high data rate and stringent latency requirements due to the massive invasion of connected devices and data‐hungry applications. Edge caching is a promising technique to overcome these challenges by prefetching the content closer to the end users at the edge node's local storage. In this paper, we analyze the performance of edge caching 5G networks with the aid of satellite communication systems. First, we investigate the satellite‐aided edge caching systems in two promising use cases: (a) in dense urban areas and (b) in sparsely populated regions, eg, rural areas. Second, we study the effectiveness of satellite systems via the proposed satellite‐aided caching algorithm, which can be used in three configurations: (a) mono‐beam satellite, (b) multi‐beam satellite, and (c) hybrid mode. Third, the proposed caching algorithm is evaluated by using both empirical Zipf‐distribution data and the more realistic Movielens dataset. Last but not least, the proposed caching scheme is implemented and tested by our developed demonstrators which allow real‐time analysis of the cache hit ratio and cost analysis.     

面向软件定义无线接入网的高效协作内容缓存算法

为了缓解海量的移动业务数据与容量受限的无线接入网回传链路之间的矛盾,本文提出一种面向软件定义无线接入网（SD-RAN）的协作内容缓存网络架构.在宏蜂窝基站（MBS）的控制管理下,小蜂窝基站（SBS）可以在存储单元有序存储一些高流行度的内容.针对SBS存储单元空间受限问题,进一步提出SD-RAN网络架构下的协作内容缓存算法.该算法中,每个SBS缓存空间被分割成两部分：（1）用于存储全网流行度最高的公共内容以保证各小蜂窝小区本地命中率.（2）用于存储流行度较高的差异化的内容以促进MBS内SBS之间的协作.在此基础上,解析推导具有最优平均内容获取开销的分割参数闭合表达式.仿真结果表明该算法在不同系统参数条件下能显著降低SD-RAN的平均内容获取开销.     

一种基于SDN的CCN集中控制缓存决策方法

在软件定义网络(SDN)和内容中心网络(CCN)融合架构下,为了充分利用控制层对网络拓扑和缓存资源的全局感知,在全网中实现缓存资源的优化使用,提出了一种集中控制的缓存决策优化方案.在该方案中,应用粒子群优化算法(PSO)并且根据节点边缘度、节点重要度以及内容流行度对缓存资源和内容进行集中缓存决策,使得内容在不同的节点进行合理的缓存.仿真结果表明,通过评估缓存大小对缓存性能的影响,PSO缓存决策方法取得了比LCE、PROB缓存决策策略更优的缓存命中率和路径延展率,明显降低了缓存节点的缓存替换数,使得缓存达到了整体缓存优化.