基于分布式缓存管理架构的P2P VoD系统

针对P2PVoD系统中怎样有效地管理和使用节点所缓存的媒体内容的问题,本文提出了一种基于分布式的缓存管理架构。在这一架构中每个节点存储系相应的媒体分块,所存储的媒体分块可以组合成一个完整的媒体文件的所有节点分布在一个环内。因此在一个环内可以快速定位到这个媒体文件的不同分块,提高了资源搜素的效率。仿真证明这种架构可以有效地管理和使用节点所缓存的媒体内容支持用户的VCR操作。     

基于内容预期价值的内容中心网络缓存替换策略

缓存替换技术是内容中心网络的研究内容之一。相对于海量的内容数据,缓存空间总是有限的,良好的缓存替换策略可以提高缓存收益。考虑到内容在将来被请求的概率越大,预期访问时间距离当前时刻越近,其留存价值就越大,提出了一种基于内容预期价值的替换策略。该方案通过考虑内容流行度和预期访问时间到当前时刻的时间距离,构建内容预期价值函数,并据此将价值最小的内容替换出去。仿真实验证明,该策略相对于传统的缓存替换策略,可以有效提高缓存命中率、降低请求跳数,提高网络性能。     

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.     

节点状态感知的机会网络分布式协作缓存机制

合理利用节点间的协作关系及相邻节点的缓存资源可有效提高机会网络中节点缓存的利用率与消息的投递率。该文提出一种节点状态感知的分布式协作缓存机制,通过感知消息扩散程度动态估计消息的重要程度,确定消息在缓存操作中的优先级。进而根据节点的活跃度及相对粘度获知给定消息与其目标节点的相遇概率,并针对消息源节点的差异设计分区协作缓存方法,同时在本地缓存处于存满状态时动态选取协作节点,实现本地消息到相邻节点的转移,以达到高效利用缓存资源的目的。结果表明,所提出的协作缓存机制能够有效地利用节点有限的缓存资源,并大幅度地改善消息投递率及节点缓存利用率。     

Caching strategy based on node’s importance to community in information-centric networks

In-network caching is one of the most important features of ICN (information-centric networks).A caching strategy based on node’s importance to community was proposed to make content objects cached more reasonable in temporal and spatial distribution.Within each community,the strategy not only caches content objects in those nodes where users are easy to access the content,but also makes different popularity of content objects more reasonable in temporal distribution.The CSNIC strategy was implemented under a variety of experimental conditions,and compare it with the previous strategies CEE-LRU,Betw-LFU and Opportunistic.The simulation results show that the strategy can yield a significant performance improvement,such as,cache hit ratio,hop reduction ratio,content diversity ratio and number of replacement.Furthermore,the additional overhead of CSNIC is small.     

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%。     

面向物联网数据特征的信息中心网络缓存方案

将信息中心网络(ICN)应用到物联网(IoT)架构(ICN-IoT),可以有效地解决数据分发问题,提高数据的传输效率。但在ICN-IoT中,现有的缓存研究主要是在内容流行度或新鲜度等单一维度上实现缓存配置,无法适应海量和多态的物联网数据特征,导致缓存效率低。针对上述问题,该文首先分析了物联网数据特征,将数据分为周期性数据和事件触发性数据。然后,综合考虑物联网的这两种数据特征,提出一种具有不同缓存决策的ICN-IoT缓存方案(CS-DCI),路由器根据到达数据的特征类型执行相应的缓存决策。最后详细介绍两种数据类型的缓存策略,对于周期性数据,考虑内容流行度和时间请求概率缓存用户请求最多的数据;对于事件触发性数据,考虑内容流行度和事件触发频率缓存有意义的数据。仿真表明,该方案能够提高内容差异率,增加缓存内容的多样性,从而满足ICN-IoT不同应用的请求,获得较优的缓存命中率以及减少内容获取跳数。     

Semantic social overlay networks

Peer selection for query routing is a core task in peer-to-peer networks. Unstructured peer-to-peer systems (like Gnutella) ignore this problem, leading to an abundance of network traffic. Structured peer-to-peer systems (like Chord) enforce a particular, global way of distributing data among the peers in order to solve this problem, but then encounter problems of network volatility and conflicts with the autonomy of the peer data management. In this paper, we propose a new mechanism, INGA, which is based on the observation that query routing in social networks is made possible by locally available knowledge about the expertise of neighbors and a semantics-based peer selection function. We validate INGA by simulation experiments with different data sets. We compare INGA with competing peer selection mechanisms on resulting parameters like recall, message gain or number of messages produced     

Social video caching

We investigate online browsing of interrelated content, represented as a catalog of items of interest featuring graph dependencies. The content is served to clients via a system of decentralized proxy caches connected to cloud servers. A client selects the next item to browse from the list of recommended items, displayed on the currently browsed item׳s catalog page. A cache has a limited size to have every item selected by its browsing clients available for local access. Thus, the system pays a penalty, whenever a client selects an item that cannot be served directly from the proxy. Conversely, the system gains a reward, if a client selects an immediately available item. We aim to select the items to cache that maximize the profit earned by the system, for the given cache capacity. We design two linear-time optimization techniques for finding the desired items to cache. We enhance the operation of the system via two additional strategies. The first one dynamically tracks the items׳ selection probabilities for a client, as a function of its prior catalog access pattern and those of its community peers. The second one constructs dynamic overlays, on behalf of the clients, that are used to share the selected items directly among them. This augments the system׳s serving capacity and enhances the clients׳ browsing experience. We study the performance of the optimization techniques via numerical experiments. They exhibit efficiency gains over reference methods, by exploiting the content dependencies and correlated community-driven access patterns of the clients. We also report proxy bandwidth savings achieved by our overlay strategy over state-of-the-art methods, on content access patterns of clients with Facebook or Twitter ties.     

Decentralised detection of periodic encounter communities in opportunistic networks

We tackle the problem of individuals being able to self-detect the encounter communities within which they periodically occur. This has widespread applicability, not least for future communication systems where content can be locally shared via wireless opportunistic networking when devices carried by participants come into close range. In this paper, we introduce a comprehensive model and decentralised algorithm to accomplish the detection of periodic communities in opportunistic networks. To the best of our knowledge, this is the first decentralised algorithm for the detection of periodic communities. We investigate the behaviour of our approach both analytically and with real-world data.     

基于节点动态内容流行度的缓存管理策略

针对命名数据网络中节点无法感知内容流行度变化的缺陷,提出了基于缓存内容流行度动态变化的内容管理策略.将缓存分为主缓存（Primary Cache,PC）和副缓存（Secondary Cache,SC）,分别用于识别和保护流行内容;采用标准布鲁姆过滤器（Standard Bloom Filter,SBF）过滤流行内容请求;引入滑动时间窗口算法和HASH表对副缓存内容进行细粒度的统计分析,进而管理缓存内容.仿真显示,与现有算法相比,该策略以增加少量复杂度为代价,延长高流行度内容的缓存驻留时间,提高了缓存命中率,减轻了服务器负载,并具有可扩展性,具备单线路40Gbit/s的报文处理能力.     

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.     

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。      

一种改进的基于HLR/VLR体系结构的终身号码方案

在PCS系统中，终身号码允许用户在改变运营商时保留其原有电话号码，本文提出的基于HLR/VLR体系结构Cache改进算法，能保证用户在多次改变运营商时，使用户实现真正的号码携带。在当前无线网络运营中，基于HLR/NLR体系结构位置管理的无线网络取得了极大成功，使得研究基于HLR/NLR体系结构的终身号码方案具有非常重要的现实意义。与原基于HLR／VLR位置管理Cache算法相比，本文算法的实现只需要软件升级和存储器扩容。随着软硬件技术的发展，使本文提出的方案具有较强的可行性。     

Analytical characterization of cache replacement policy impact on content delivery time in information‐centric networks

Information‐centric networking (ICN) has emerged as a promising candidate for designing content‐based future Internet paradigms. ICN increases the utilization of a network through location‐independent content naming and in‐network content caching. In routers, cache replacement policy determines which content to be replaced in the case of cache free space shortage. Thus, it has a direct influence on user experience, especially content delivery time. Meanwhile, content can be provided from different locations simultaneously because of the multi‐source property of the content in ICN. To the best of our knowledge, no work has yet studied the impact of cache replacement policy on the content delivery time considering multi‐source content delivery in ICN, an issue addressed in this paper. As our contribution, we analytically quantify the average content delivery time when different cache replacement policies, namely, least recently used (LRU) and random replacement (RR) policy, are employed. As an impressive result, we report the superiority of these policies in term of the popularity distribution of contents. The expected content delivery time in a supposed network topology was studied by both theoretical and experimental method. On the basis of the obtained results, some interesting findings of the performance of used cache replacement policies are provided.     

命名数据网络中基于局部请求相似性的协作缓存路由机制

该文针对命名数据网络(Named Data Networking, NDN)应答内容的高效缓存和利用问题,依据内容请求分布的局域相似特征,提出一种协作缓存路由机制。缓存决策时,将垂直请求路径上的冗余消除和水平局域范围内的内容放置进行有效结合。垂直方向上,提出基于最大内容活跃因子的路径缓存策略,确定沿途转发对应的最大热点请求区域;水平方向上,采用一致性Hash协同缓存思想,实现应答内容的局域定向存储。路由查找时,将局域节点缓存引入到路由转发决策中,依据内容活跃等级动态执行局域缓存查找,增大内容请求就近响应概率。该机制减小了内容请求时延和缓存冗余,提高了缓存命中率,以少量额外的代价换取了内容请求开销的大幅下降,仿真结果验证了其有效性。     

Defending against cache consistency attacks in wireless ad hoc networks

Caching techniques can be used to reduce bandwidth consumption and data access delay in wireless ad hoc networks. When cache is used, the issue of cache consistency must be addressed, and maintaining strong cache consistency is desired in some strategic scenarios (e.g., battlefields). In these situations, the invalidation-based approach is preferred due to its low overhead. However, this approach may suffer from some security attacks. For example, malicious nodes (also called intruders) may drop, insert or modify invalidation messages to mislead receivers to use stale data or unnecessarily invalidate data that are still valid. In this paper, we first propose to employ the Invalidation Report (IR) based cache invalidation strategy to prevent intruders from dropping or modifying invalidation messages. Although digital signatures can be used to protect IRs, this has significantly high overhead in terms of computational and bandwidth overhead. To address this problem, we further propose a family of randomized grouping-based schemes for intrusion detection, damage recovery and intruder identification. Extensive analysis and simulations are performed to evaluate the proposed schemes. The results show that our solution can achieve a satisfactory level of security with low overhead.     

GroCoca: group-based peer-to-peer cooperative caching in mobile environment

In a mobile cooperative caching environment, we observe the need for cooperating peers to cache useful data items together, so as to improve cache hit from peers. This could be achieved by capturing the data requirement of individual peers in conjunction with their mobility pattern, for which we realized via a GROup-based COoperative CAching scheme (GroCoca). In GroCoca, we define a tightly-coupled group (TCG) as a collection of peers that possess similar mobility pattern and display similar data affinity. A family of algorithms is proposed to discover and maintain all TCGs dynamically. Furthermore, two cooperative cache management protocols, namely, cooperative cache admission control and replacement, are designed to control data replicas and improve data accessibility in TCGs. A cache signature scheme is also adopted in GroCoca in order to provide information for the mobile clients to determine whether their TCG members are likely caching their desired data items and to perform cooperative cache replacement Experimental results show that GroCoca outperforms the conventional caching scheme and standard COoperative CAching scheme (COCA) in terms of access latency and global cache hit ratio. However, GroCoca generally incurs higher power consumption     

Community‐based traffic preservation in telecommunication networks

Large‐scale failures in telecommunication networks have been traditionally mitigated by attempting to preserve global connectivity in the network. However, although communication inside communities may be of high relevance, their preservation is a challenging task. To achieve this, we propose a new concept of community that combines not only the topological information of the network but also the traffic‐level interaction. This new concept is then used to present six novel community‐based strategies that, when working within a limited budget, are able to determine which are the best candidate nodes to protect. The strategies proposed have been tested over four different types of networks and have been compared with other well‐known immunization or protection methods. Results show that community‐based strategies better preserve the inner community traffic with respect to traditional approaches. Furthermore, in some cases, both global and intra‐community traffic preservation is able to be improved. Copyright © 2013 John Wiley & Sons, Ltd.     

Applying Hebbian Theory to Enhance Search Performance in Unstructured Social‐Like Peer‐to‐Peer Networks

Unstructured peer‐to‐peer (p2p) networks usually employ flooding search algorithms to locate resources. However, these algorithms often require a large storage overhead or generate massive network traffic. To address this issue, previous researchers explored the possibility of building efficient p2p networks by clustering peers into communities based on their social relationships, creating social‐like p2p networks. This study proposes a social relationship p2p network that uses a measure based on Hebbian theory to create a social relation weight. The contribution of the study is twofold. First, using the social relation weight, the query peer stores and searches for the appropriate response peers in social‐like p2p networks. Second, this study designs a novel knowledge index mechanism that dynamically adapts social relationship p2p networks. The results show that the proposed social relationship p2p network improves search performance significantly, compared with existing approaches.