User Preference Aware Hierarchical Edge-User Cooperative Caching Strategy

The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been considered as a feasible scheme.However,how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past decade.In this paper,considering the time-varying user requests and the heterogeneous content sizes,a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is proposed.We divide the caching strategy into three phases,that is,the content placement,the content delivery and the content update.In the content placement phase,a cooperative content placement algorithm for local content popularity is designed to cache contents proactively.In the content delivery phase,a cooperative delivery algorithm is proposed to deliver the cached contents.In the content update phase,a content update algorithm is proposed according to the popularity of the contents.Finally,the proposed caching strategy is validated using the MovieLens dataset,and the results reveal that the proposed strategy improves the delay performance by at least 35.3%compared with the other three benchmark strategies.

Cooperative Content Caching and Delivery in Vehicular Networks: A Deep Neural Network Approach

The growing demand for low delay vehicular content has put tremendous strain on the backbone network.As a promising alternative,cooperative content caching among different cache nodes can reduce content access delay.However,heterogeneous cache nodes have different communication modes and limited caching capacities.In addition,the high mobility of vehicles renders the more complicated caching environment.Therefore,performing efficient cooperative caching becomes a key issue.In this paper,we propose a cross-tier cooperative caching architecture for all contents,which allows the distributed cache nodes to cooperate.Then,we devise the communication link and content caching model to facilitate timely content delivery.Aiming at minimizing transmission delay and cache cost,an optimization problem is formulated.Furthermore,we use a multi-agent deep reinforcement learning(MADRL)approach to model the decision-making process for caching among heterogeneous cache nodes,where each agent interacts with the environment collectively,receives observations yet a common reward,and learns its own optimal policy.Extensive simulations validate that the MADRL approach can enhance hit ratio while reducing transmission delay and cache cost.

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

Cooperative Caching for Scalable Video Coding Using Value-Decomposed Dimensional Networks

Scalable video coding(SVC)has been widely used in video-on-demand(VOD)service,to efficiently satisfy users’different video quality requirements and dynamically adjust video stream to timevariant wireless channels.Under the 5G network structure,we consider a cooperative caching scheme inside each cluster with SVC to economically utilize the limited caching storage.A novel multi-agent deep reinforcement learning(MADRL)framework is proposed to jointly optimize the video access delay and users’satisfaction,where an aggregation node is introduced helping individual agents to achieve global observations and overall system rewards.Moreover,to cope with the large action space caused by the large number of videos and users,a dimension decomposition method is embedded into the neural network in each agent,which greatly reduce the computational complexity and memory cost of the reinforcement learning.Experimental results show that:1)the proposed value-decomposed dimensional network(VDDN)algorithm achieves an obvious performance gain versus the traditional MADRL;2)the proposed VDDN algorithm can handle an extremely large action space and quickly converge with a low computational complexity.

RecCac:Recommendation-Empowered Cooperative Edge Caching for Internet of Things

Edge caching is an emerging technology for supporting massive content access in mobile edge networks to address rapidly growing Internet of Things(IoT)services and content applications.However,the edge server is limited with the computation/storage capacity,which causes a low cache hit.Cooperative edge caching jointing neighbor edge servers is regarded as a promising technique to improve cache hit and reduce congestion of the networks.Further,recommender systems can provide personalized content services to meet user’s requirements in the entertainment-oriented mobile networks.Therefore,we investigate the issue of joint cooperative edge caching and recommender systems to achieve additional cache gains by the soft caching framework.To measure the cache profits,the optimization problem is formulated as a 0-1 Integer Linear Programming(ILP),which is NP-hard.Specifically,the method of processing content requests is defined as server actions,we determine the server actions to maximize the quality of experience(QoE).We propose a cachefriendly heuristic algorithm to solve it.Simulation results demonstrate that the proposed framework has superior performance in improving the QoE.

Performance of Distributed Energy Aware Routing (DEAR) Protocol with Cooperative Caching for Wireless Sensor Networks

Considering Wireless Sensor Networks (WSNs) in today’s scenario, sending and receiving uninterrupted sensory data remains a challenge to achieve with minimal latency and energy consumption as low as possible. Energy consumption is exponentially growing in computing devices such as computers, embedded systems, portable devices, and wireless sensor networks. Extensive research has been in practice recently to minimize energy consumption without compromising the Quality of Service (QoS) that is to provide data to the requester node with minimum Delay and high Reliability. In this paper, a cooperative caching algorithm is used with the proposed Distributed Energy Aware Routing (DEAR) protocol that attempts to minimize energy consumption by reducing the packet overhead in the network and also providing the data to the requester with minimum delay by retrieving requested datum from the nearby caching node available in the vicinity of the requester or sink node. The simulation results clearly show that the energy consumption is less when the grid-based analytical model is used against the star/cluster based model while keeping the same necessary attributes.

并行文件系统PARFSNOW++中的Cooperative Cache技术的研究

本文介绍了并行文件系统PARFSNOW ++中采用的有权限控制的复合协作式缓冲管理机制 ,并针对当前协作式缓冲机制中的问题 ,提出了新的全局内存数据同步和数据替换策略 .它们能有效地提高全局内存数据同步和缓冲数据替换的效率 .通过实验 ,本文证明了引入这种改进的协作式缓冲机制后 ,并行文件系统效率得到了提高 .

Learning-Based Joint Service Caching and Load Balancing for MEC Blockchain Networks

Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure reward mechanism that can facilitate load balancing among MECS.In addition,intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of workloads.In this paper,we investigate a learningbased joint service caching and load balancing policy for optimizing the communication and computation resources allocation,so as to improve the resource utilization of MEC blockchain networks.We formulate the problem as a challenging long-term network revenue maximization Markov decision process(MDP)problem.To address the highly dynamic and high dimension of system states,we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network(DQN)approach.The simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.

Research and implementation of cooperative cache for PVFS

At present, there are many effective ways to achieve high performance in cluster system storage management, including server-end disk, server-end caching, local caching and cooperative caching. The cooperative caching mechanism shares caches among different clients so as to avoid expensive disk access costs and to improve overall throughput of cluster system. In this paper, a Single Copy Cooperative Cache model is proposed together with block lookup algorithm, block replacement algorithm and the consistency algorithm based on the model. Meanwhile, the prototype system of the model is implemented in PVFS file system. Finally, the performance of this system is tested in InfiniBand Framework, the result of which shows that in contrast to the original PVFS system, read performance of PVFS file system is improved by about two times, while write performance is reduced by nearly ten percent.

GCaching——一种网格协同缓存系统

协同缓存通过多个代理缓存服务器的协同工作 ,可以充分利用各服务器的缓存空间 ,提高缓存命中率 网格技术可以方便地共享和整合异构的服务器资源 结合网格和协同技术 ,提出了一种网格协同缓存系统GCaching ,它将地域上分布的多个代理缓存服务器组成缓存池 ,充分利用缓存资源 ,协同工作 ,为用户提供更好的服务能力 GCaching系统设计并实现了一种缓存放置和替换算法GCPR ,它使用周期缓存更新策略 ,根据用户访问模式自适应地调整缓存数据的分布 仿真实验表明 。

A P2P streaming service architecture with distributed caching

Multimedia streaming served through peer-to-peer (P2P) networks is booming nowadays. However, the end-to-end streaming quality is generally unstable due to the variability of the state of serve-peers. On the other hand, proxy caching is a bandwidth-efficient scheme for streaming over the Internet, whereas it is a substantially expensive method needing dedicated powerful proxy servers. In this paper, we present a P2P cooperative streaming architecture combined with the advantages of both P2P networks and multimedia proxy caching techniques to improve the streaming quality of participating clients. In this frame- work, a client will simultaneously retrieve contents from the server and other peers that have viewed and cached the same title before. In the meantime, the client will also selectively cache the aggregated video content so as to serve still future clients. The associate protocol to facilitate the multi-path streaming and a distributed utility-based partial caching scheme are detailedly dis- cussed. We demonstrate the effectiveness of this proposed architecture through extensive simulation experiments on large, Inter- net-like topologies.

Degree-Based Probabilistic Caching in Content-Centric Networking

Content-Centric Networking is a novel future network architecture that attracts increasing research interests in recent years. In-network caching has been regarded as a prominent feature of Content-Centric Networking since it is able to reduce the network traffic, alleviate the server bottleneck and decrease the user access latency. However, the CCN default caching scheme results in a high caching redundancy, causing an urgent need for an efficient caching scheme. To address this issue, we propose a novel implicit cooperative caching scheme to efficiently reduce the caching redundancy and improve the cache resources utilization. The simulation results show that our design achieves a higher hit ratio and a shorter cache hit distance in comparison with the other typical caching schemes.

Neighborhood-Based In-Network Caching for Information-Centric Networks

The current Internet is based on host-centric networking, and a user needs to know the host address before reaching a data target in the network. The new architecture of information-centric networking (ICN) facilitates users to locate data targets by giving their data names without any information about host addresses. In-network caching is one of the prominent features in ICN, which allows network routers to cache data contents. In this paper, we emphasize the management of in-network cache storage, and this includes the mechanisms of cache replacement and cache replication. A new cost function is then proposed to evaluate each cache content and the least valuable content is evicted when cache is full. To increase cache utilization, a cooperative caching policy among neighboring routers is proposed. The proper network locations to cache data contents are also discussed in the paper. Experimental results show the superiority of the proposed caching policy than some traditional caching polices.

基于联盟链的边缘缓存系统中最佳缓存策略设计

针对不同运营商各自部署边缘设备,投入巨大且缓存内容相互隔离,无法共享的问题,改进了一种基于联盟链的边缘缓存系统架构,使运营商部署的边缘设备间能够打破内容隔离,实现更大范围的共享。为了提高运营商缓存收益,同时保证用户传输质量,降低用户传输时延,首先针对缓存内容流行度、内容大小以及边缘设备的协作能力,分析不同内容交付方式对于用户传输时延和运营商缓存收益的影响;然后,以最大化缓存收益和最小化传输时延为目标建立优化问题;最后,为解决构建的高维度大规模的缓存决策问题,采用基于异步优势动作评价的内容缓存算法确定内容的最佳放置位置。仿真结果表明,所提缓存策略能够有效地提高边缘缓存收益,降低内容传输时延,提升用户体验。

基于协作缓存的VOD服务器端Cache设计

近年来,随着计算机网络技术和多媒体技术的发展,视频点播服务已逐渐成为现实。分布式VOD(VideoOnDemand)服务器系统的提出是为了支持更多的大量并发数据流,和单一服务器相比,这样的结构拥有更好的使用效率、可靠性和可扩展性。协作缓存CC(cooperativecache)技术将各服务器的内存协调工作,形成全局的cache。这样的结构不仅充分发挥了分布式VOD服务器结构的特点,同时也增大cache容量,提高系统全局命中率,从而提高了系统效率。该文在协作缓存技术基础上,针对流媒体和VOD系统的特点,提出了GBBcache替换算法。该算法以数据块的生命周期作为出发点,充分考虑了现有用户和请求接入用户的服务需求,提高了内存使用效率。笔者对该算法进行了理论分析,并证明了它在性能上与传统的cache替换算法相比的优越性。

SmartCache:基于兴趣的协作式Web缓存

发现并验证了Web访问的局部性原理,在其基础上提出一种全新的集中分类学习但分布协作运行的基于P2P的客户端缓存模型——SmartCache。SmartCache采取集中方法学习节点访问兴趣并按照访问兴趣对节点分类,节点之间按照兴趣分类组成非结构化的对等缓存网络,缓存网络的运行是完全分布的。仿真实验表明,SmartCache以低代价实现了高缓存命中率,能够有效提高缓存容量和访问速度。

CoLoRCache:智慧协同网络中的协作缓存机制

介绍一种有效支持缓存协作的未来网络体系架构:智慧协同网络,然后提出了一种高效的协作缓存机制,称为Co Lo RCache。Co Lo RCache的主要目标是减小缓存冗余和建立缓存共享机制。我们通过仿真结果来验证Co Lo RCache。仿真数据表明,相比较于其他缓存机制,Co Lo RCache能够产生更高的缓存命中率和有着最小的请求命中距离。

基于深度强化学习的边缘网络内容协作缓存与传输方案研究

为了应对第五代无线通信网络中数据吞吐量急剧增加的问题,移动边缘缓存成为了一种有效的解决方案。它通过在边缘设备上存储网络内容,减轻回程链路和核心网络的负担,缩短服务时延。到目前为止,大多数边缘缓存研究主要优化协作内容缓存,忽略了内容传输的效率。研究超密集网络的内容协作边缘缓存与无线带宽资源的分配问题,通过余弦相似度和高斯相似度求解基站之间总的相似度,将网络中的小基站根据总相似度进行分组,把缓存和无线带宽分配问题建模成一个长期混合整数的非线性规划问题(LT-MINLP),进而将协作边缘缓存与带宽分配问题转换为一个带约束的马尔可夫决策过程,并利用深度确定性策略梯度DDPG模型,提出了一种基于深度强化学习的内容协作边缘缓存与带宽分配算法CBDDPG。提出的基站分组方案增加了基站之间文件共享的机会,提出的CBDDPG算法的缓存方案利用DDPG双网络机制能更好地捕捉用户的请求规律,优化缓存部署。将CBDDPG算法与三种基线算法(RBDDPG、LCCS和CB-TS)进行了对比实验,实验结果表明所提方案能够有效地提高内容缓存命中率,降低内容传递的时延,提升用户体验。

RIS辅助的UAV与用户协同缓存策略

研究了智能反射面(RIS)和缓存辅助的无人机(UAV)中继通信系统方案,通过在UAV与用户之间搭建RIS反射信号,改善信道环境;在UAV与用户设备上部署缓存设备,预先存储热点内容,减轻无线回程链路的压力;以最大化用户服务成功概率为优化目标,建立缓存容量受限约束下的UAV与用户协同缓存放置策略优化模型,针对该非线性连续非凸约束优化问题,提出基于鲸鱼优化算法(WOA)的求解方法.仿真实验结果表明,使用RIS可以有效降低UAV通信中断概率,基于WOA的UAV与用户协同缓存最优放置策略优于现有其他两种缓存策略,能有效提高缓存命中概率,从而提高用户服务成功概率.

片上多处理器末级Cache优化技术研究

片上多核技术的出现给处理器的设计和实现带来很多挑战,片上存储系统的设计就是其中最重要的方面之一.为了缓解日益严峻的存储墙问题,研究者们通常在片上放置大容量末级Cache,片上末级Cache设计和优化技术已成为当前的研究热点.介绍了片上多处理器(CMP)末级Cache设计面临的挑战,然后分别介绍了以私有设计和共享设计为基础的多种CMP末级Cache优化技术,并对它们进行了比较分析.