改进的最小效用流媒体代理缓存替换算法

在SCU-K算法的基础上,提出了基于流行度和将来访问次数的最小效用替换算法（SCU-PFUT）。此外算法还考虑了流媒体文件的字节有效性和文件块大小的因素,使得替换出内存的数据块更加合理。不但避免LRU和LFU算法中出现的媒体文件被连续替换的问题,相对于LRU、LFU和SCU-2,其在缓存命中率、字节命中率和空间利用率都得到了提升。     

一种高效的流媒体代理缓存替换算法

提出基于流行度和将来访问次数的最小效用替换算法（SCU-PFUT）,考虑流媒体文件的字节有效性和文件块大小等因素,使替换出内存的数据块更合理。避免LRU和LFU算法中出现的媒体文件被连续替换的问题,与LRU, LFU和SCU-2算法相比,该算法的缓存命中率、字节命中率和空间利用率较高。     

超节点P2P网络中一种有效的缓存策略

针对超节点P2P系统的特点,提出了一种有效且灵活的缓存策略.该策略使用文件价值来决定缓存替换的对象,并且在替换之前使用"阈值"选择要缓存的文件,使其系统只缓存价值较大的热点文件.最后通过Trace-Driven的方法模拟实验,结果表明,与现有的缓存策略LRU和LFU相比,这种缓存策略具有较好的缓存命中率和字节命中率.     

代理缓存替换一致性算法的研究

代理缓存技术能很好的解决Internet发展中出现的访问延迟过长、服务器过载等一系列的问题.针对代理缓存的一致性策略和替换策略还没有很好地结合起来的技术现状,设计并实现了一种新的优化代理缓存的替换一致性算法-RCA算法.这种算法包括一致性策略和替换策略两部分,一致性策略采用自适应TTL机制,替换策略是结合了LFU和LRU,并引入老化机制的LFRU算法.通过Trace-Driven模拟实验,结果表明RCA算法在文档命中率和文档字节命中率比上均优于传统的几个替换算法.     

基于流媒体文件字节有用性的代理服务器缓存替代策略

将流媒体文件缓存到离用户最近的代理服务器上，能够减少广域网络带宽的消耗，减轻服务器的负载压力以及降低用户的始播延迟，文章关注代理服务器的缓存替代问题．通过对问题建立模型并分析后得到替代算法模型，提出了流媒体文件字节有用性的概念并反映到替代算法的设计之中，并提出了BB，BBLRU-K和BBLCB-K缓存替代算法，在与LRU-2，LFU，LCB-2和LRU等算法的性能模拟实验比较中，BBLCB-2算法性能最优，但BB算法简单有效。     

基于高斯混合模型的Web代理服务器缓存替换策略

Web代理服务器缓存能够在一定程度上解决用户访问延迟和网络拥塞问题,Web代理缓存的缓存替换策略直接影响缓存的命中率,从而影响网络请求响应的效果;为此,使用一种通过固定大小的循环滑动窗口提取Web日志数据的多项特征,并使用高斯混合模型对Web日志数据进行聚类分析,预测在窗口时间内可能再次访问到Web对象,结合最近最少使用(LRU)算法,提出一种新的基于高斯混合模型的Web代理服务器缓存替换策略;实验结果表明,与传统的缓存替换策略LRU、LFU、FIFO、GDSF相比,该策略有效提高了Web代理缓存的请求命中率和字节命中率。     

基于谱聚类的Web多级缓存替换策略

服务器缓存性能的核心是缓存替换策略, 缓存替换策略直接影响缓存的命中率, Web缓存可以解决网络拥塞和用户访问延迟问题, 提高服务器的性能. 传统缓存替换算法的命中率往往不高, 为此文中提出了一种基于谱聚类的多级缓存替换策略. 该策略利用循环滑动窗口机制提取日志文件的多项时序特征和访问属性, 通过谱聚类对过滤后的数据集进行聚类分析从而得到访问预测结果. 多级缓存替换策略综合考虑了缓存对象的局部频率、全局频率以及资源大小能更好地对低价值资源进行剔除, 同时对高价值资源进行保留. 通过与传统替换算法LRU、LFU、RC、FIFO进行实验对比, 实验结果表明本文将谱聚类和多级缓存替换策略进行结合有效地提高了缓存请求命中率和字节命中率.     

基于密文策略属性基加密系统访问机制的缓存替换策略

为提高基于密文策略属性基加密（CP-ABE）系统的数据缓存性能,针对CP-ABE加密的数据,提出一种有效的缓存替换算法--最小属性价值（MAV）算法。该算法结合CP-ABE加密文件的访问策略并统计高频属性值的个数,利用余弦相似度方法和高频属性值统计表来计算属性相似度;同时结合属性相似度和文件大小计算缓存文件的属性值价值,并替换属性值价值最小的文件。在与最近最少使用（LRU）、最不经常使用（LFU）、Size缓存替换算法的对比实验中,针对CP-ABE加密后的数据,MAV算法在提高加密文件请求命中率和字节命中率方面具有更好的性能。     

基于对象角色的高精度缓存替换算法*

现有的Web缓存器的实现主要是基于传统的内存缓存算法,由于Web业务请求的异质性,传统的替换算法不能在Web环境中有效工作。研究了Web缓存替换操作的依据,分析了以往替换算法的不足,考虑到Web文档的大小、访问代价、访问频率、访问兴趣度以及最近一次被访问的时间对缓存替换的影响,提出了Web缓存对象角色的概念,建立了一种新的基于对象角色的高精度Web缓存替换算法(ORB算法);并以NASA和DEC的代理服务器数据为例,将该算法与LRU、LFU、SIZE、Hybrid算法进行了仿真实验对比,结果证明,ORB算     

最小驻留价值缓存替换算法

为提高搜索应用的缓存性能,提出一种新的缓存替换算法--最小驻留价值(LCV)算法。该算法通过计算对象访问频率,结合对象大小,优先选取对字节命中率贡献最小的对象集进行缓存替换。同时,将最优替换对象集的选取转化为经典0-1背包问题进行了求解,并给出一种快速近似解法及其算法数据结构。在与最近最少使用(LRU)、先进先出(FIFO)和考虑多重因子(GD-Size)算法的对比实验中,LCV算法在提高字节命中率(BHR)和降低平均延时时间(ALT)方面具有更好的性能。     

Window‐LRFU: a cache replacement policy subsumes the LRU and window‐LFU policies

Replacement algorithms have been widely used as key technologies for cache management in areas such as file systems or database management. A replacement algorithm determines which page to be evicted when the cache is full and a new page is referenced. Because replacement policies considering only recency or frequency such as LRU (Least Recently Used) and LFU (Least Frequently Used) do not perform well, replacement polices that take both recency and frequency into account have been intensively studied. As a classical replacement policy, LRFU (Least Recently/Frequently Used) policy subsumes the LRU and LFU policy. However, because LFU is not able to adapt to the change of page accessing pattern and it is hard to select a suitable λ for each certain trace, LRFU cannot always guarantee a good performance. In this paper, we propose a Window‐LRFU policy, to subsume the LRU and Window‐LFU policies. Experimental results show that the Window‐LRFU policy outperforms LRFU and has at least competitive performance than other classical algorithms. Copyright © 2015 John Wiley & Sons, Ltd.     

Heterogeneous-aware cache partitioning: Improving the fairness of shared storage cache

In this paper, we investigate the problem of fair storage cache allocation among multiple competing applications with diversified access rates. Commonly used cache replacement policies like LRU and most LRU variants are inherently unfair in cache allocation for heterogeneous applications. They implicitly give more cache to the applications that has high access rate and less cache to the applications of slow access rate. However, applications of fast access rate do not always gain higher performance from the additional cache blocks. In contrast, the slow application suffer poor performance with a reduced cache size. It is beneficial in terms of both performance and fairness to allocate cache blocks by their utility.In this paper, we propose a partition-based cache management algorithm for a shared cache. The goal of our algorithm is to find an allocation such that all heterogeneous applications can achieve a specified fairness degree as least performance degradation as possible. To achieve this goal, we present an adaptive partition framework, which partitions the shared cache among competing applications and dynamically adjusts the partition size based on predicted utility on both fairness and performance. We implement our algorithm in a storage simulator and evaluate the fairness and performance with various workloads. Experimental results show that, compared with LRU, our algorithm achieves large improvement in fairness and slightly in performance.     

Neuro-fuzzy system in partitioned client-side Web cache

This paper proposes a novel contribution in Web caching area, especially in Web cache replacement, so-called intelligent client-side Web caching scheme (ICWCS). This approach is developed by splitting the client-side cache into two caches: short-term cache that receives the Web objects from the Internet directly, and long-term cache that receives the Web objects from the short-term cache. The objects in short-term cache are removed by least recently used (LRU) algorithm as short-term cache is full. More significantly, when the long-term cache saturates, the neuro-fuzzy system is employed efficiently in managing contents of the long-term cache. The proposed solution is validated by implementing trace-driven simulation and the results are compared with least recently used (LRU) and least frequently used (LFU) algorithms; the most common policies of evaluating Web caching performance. The simulation results have revealed that the proposed approach improves the performance of Web caching in terms of hit ratio (HR), up to 14.8% and 17.9% over LRU and LFU. In terms of byte hit ratio (BHR), the Web caching performance is improved up to 2.57% and 26.25%, and for latency saving ratio (LSR), the performance is better with 8.3% and 18.9% over LRU and LFU, respectively.     

大规模视频点播磁盘cache替换算法

在规划视频播（ＫＳＶＯＤ）中ｃａｃｈｅ机制是提高系统效率的有效手段,是支持ＶＯＤ实用化的关键技术之一,由于连续媒体的数据量大,使用周期长等特点,传统的ｃａｃｈｅ替换算法不能直接应用于ＳＶＯＤ。文中根据ＶＯＤ的特点开发了两种基于访问频率的替换算法,ＬＦＲＵ（ｌｅａｓｔ ｆｒｅｑｕｅｎｃｙ ａｎｄ ｒｅｃｅｎｔｌｙ ｕｓｅｄ）和ＰＬＦＵ（ｐｅｒｉｏｄ ｌｅａｓｔ ｆｒｅｑｕｅｎｃｙ ｕｓｅｄ）算法,它     

Massively parallel algorithms for trace-driven cache simulations

Considers the use of massively parallel architectures to execute a trace-driven simulation of a single cache set. A method is presented for the least-recently-used (LRU) policy, which, regardless of the set size C, runs in time O(log N) using N processors on the EREW (exclusive read, exclusive write) parallel model. A simpler LRU simulation algorithm is given that runs in O(C log N) time using N/log N processors. We present timings of this algorithm's implementation on the MasPar MP-1, a machine with 16384 processors. A broad class of reference-based line replacement policies are considered, which includes LRU as well as the least-frequently-used (LFU) and random replacement policies. A simulation method is presented for any such policy that, on any trace of length N directed to a C line set, runs in O(C log N) time with high probability using N processors on the EREW model. The algorithms are simple, have very little space overhead, and are well suited for SIMD implementation     

基于协作小小区与流行度预测的在线热点视频缓存更新策略

针对无线移动设备数量的指数增长使得异构协作小小区（SBS）将承载大规模的流量负载问题，提出了一种基于协作SBS与流行度预测的在线热点视频缓存更新方案（OVCRP）。首先，分析在线热点视频的流行度在短期内变化情况；然后，构建k近邻模型进行在线热点视频流行度的预测；最后，确定在线热点视频的缓存更新位置。为了选择合适的位置存放在线热点视频，以最小化总体传输时延为目标，建立数学模型，设计整数规划优化算法。仿真实验结果显示，与随机缓存（RANDOM）、最近最少使用（LRU）、最不经常使用（LFU）方案相比，OVCRP在平均缓存命中率和平均访问时延方面具有明显的优势，因此减轻了协作SBS的网络负担。