Enhanced web cache replacement policy based on data mining and RFSD scoring

This article describes how the named data networking (NDN) has recently received a lot of attention as a potential information-centric networking (ICN) architecture for the future Internet. The NDN paradigm has a great potential to efficiently address and solve the current seminal IP-based IoT architecture issues and requirements. NDN can be used with different sets of caching algorithms and caching replacement policies. The authors investigate the most suitable combination of these two features to be implemented in an IoT environment. For this purpose, the authors first reviewed the current research and development progress in ICN, then they conduct a qualitative comparative study of the relevant ICN proposals and discuss the suitability of the NDN as a promising architecture for IoT. Finally, they evaluate the performance of NDN in an IoT environment with different caching algorithms and replacement policies. The obtained results show that the consumer-cache caching algorithm used with the Random Replacement (RR) policy significantly improve NDN content validity in an IoT environment.

In previous research on cache replacement policies, have seen that a block usually becomes dead after some uses. By classifying the dead block and discarding them early, there are various policies have been used but still there are number of problems such as cache pollution, high cache miss rate and cache overhead existed which degrades the system performance. In this paper, a new approach for cache replacement policy has introduced which reduces the cache miss rate and cache overhead by using shared L2 level cache. In terms of less miss rate and good hit rate there is need to deploy effective replacement policies. Cache replacement techniques is one of the crucial design parameter that affects the overall processor performance and also become more important with current growing technological moves in the direction of high associative cache . Keywords: Cache pollution; Cache overhead; Cache miss rate; Cache replacement policy (LRU and LFU); Memory overhead;

The performance of computing systems has been improved significantly for several decades. However, increasing the throughput of recent CPUs (Central Processing Units) is restricted by power consumption and thermal issues. GPUs (Graphics Processing Units) are recognized as efficient computing platform with powerful hardware resources to support CPUs in computing systems. Unlike CPUs, there is a large number of CUDA (Compute Unified Device Architecture) cores in GPUs, hence, some cache blocks are referenced many times repeatedly. If those cache blocks reside in the cache for long time, hit rates can be improved. On the other hand, many cache blocks are referenced only once and never referenced again in the cache. These blocks waste cache memory space, resulting in reduced GPU performance. Conventional LRU replacement policy cannot consider the problems from non-reused cache blocks and frequently-reused cache blocks. In this paper, a new cache replacement policy based on the reuse pattern of cache blocks is proposed. The proposed cache replacement policy manages cache blocks by separating reused cache blocks and thrashing cache blocks. According to simulation results, the proposed cache reuse replacement policy can increase IPC by up to 4.4% compared to the conventional GPU architecture.

We consider the problem of on-chip L2 cache management and replacement policies. We propose a new adaptive cache replacement policy, called Dueling CLOCK (DC), that has several advantages over the Least Recently Used (LRU) cache replacement policy.

Caching has been introduced and applied in prototype and commercial Web-based information systems in order to reduce the overall bandwidth and increase system's fault tolerance. This paper presents a track of Web cache replacement algorithms based on the Least Recently Used (LRU) idea. We propose an extension to the conventional LRU algorithm by considering the number of references to Web objects as a critical parameter for the cache content replacement. The proposed algorithms are validated and experimented under Web cache traces provided by a major Squid proxy cache server installation environment. Cache and bytes hit rates are reported showing that the proposed cache replacement algorithms improve cache content.

Reliability improvement in private non-uniform cache architecture using two enhanced structures for coherence protocols and replacement policies

Due to the increasing gap between CPU and memory speed, cache performance plays an increasingly critical role in determining the overall performance of microprocessor systems. One of the important factors that a affect cache performance is the cache replacement policy. Despite the importance, current analytical cache performance models ignore the impact of cache replacement policies on cache performance. To the best of our knowledge, this paper is the first to propose an analytical model which predicts the performance of cache replacement policies. The input to our model is a simple circular sequence profiling of each application, which requires very little storage overhead. The output of the model is the predicted miss rates of an application under different replacement policies. The model is based on probability theory and utilizes Markov processes to compute each cache access' miss probability. The model realistic assumptions and relies solely on the statistical properties of the application, without relying on heuristics or rules of thumbs. The model's run time is less than 0.1 seconds, much lower than that of trace simulations. We validate the model by comparing the predicted miss rates of seventeen Spec2000 and NAS benchmark applications against miss rates obtained by detailed execution-driven simulations, across a range of different cache sizes, associativities, and four replacement policies, and show that the model is very accurate. The model's average prediction error is 1.41%,and there are only 14 out of 952 validation points in which the prediction errors are larger than 10%.

Rapid growth in network services and vast usage of Internet worldwide have led to an increase in network traffic and created bottleneck over the internet. Such traffic results in an increase of access time of web documents, server response latency, reduced network bandwidth and slow response time for popular websites. Web cache is an essential optimization technique used to reduce response time and improve performance. However due to its limited size and cost of cache comparable to other storage devices, cache replacement algorithm is used to determine and evict page when the cache is full to create space for new pages. Several algorithms had been introduced and their performances are important in producing high speed web caching. However, their performances are not well optimized. This work proposes a hybrid method that optimize cache replacement algorithm using Naive Bayes (NB) based approach. Naive Bayes is an intelligent method that depends on Bayes’ probability theory integrated with Adaptive Weight Ranking Policy (AWRP) via dynamic aging factor to improve the response time and network performance. From the pseudocode of the proposed algorithm, it is observed that the complexity of the algorithm is O(n)which is linear, hence the response time is considergood. Performance evaluations based on hit rate and byte hit rate for new the method over conventional methods with real data will be conducted for validation and verification.

Proxy web caching is usually employed to maximize the efficiency and utilization of the network and the origin servers while reducing the request latency. However, and due to the limited cache size, some replacement policy has to be enforced in order to decide on the object(s) to be evicted from the cache once it is full. This paper introduces the use of the K-mean clustering to categorize the objects in the cache into groups of different priorities. This categorization is then used for replacement purposes such that the object(s) of lowest priority are chosen for eviction. The proposed improved the hit rate and the byte hit rate of the cache when compared to conventional and intelligent web proxy caching algorithms.

Caching web pages at proxies and in web servers' memories can greatly enhance performance. Proxy caching is known to reduce network and both proxy and server caching can significantly decrease latency. Web caching problems have different properties than traditional operating systems caching, and replacement can benefit by recognizing and exploiting these differences. We address two aspects of the predictability of traffic patterns: the overall experienced by large proxy and web servers, and the distinct access patterns of individual pages. We formalize the notion of cache load under various replacement policies, includingLRU andLFU, and demonstrate that the trace of a large proxy server exhibits regular load. Predictable allows for improved design, analysis, and experimental evaluation of replacement policies. We provide a simple and (near) optimal replacement policy when each page request has an associated distribution function on the next request time of the page. Without the predictable assumption, no such online policy is possible and it is known that even obtaining an offline optimum is hard. For experiments, predictable enables comparing and evaluating replacement policies using partial traces, containing requests made to only a subset of the pages. Our results are based on considering a simpler caching model which we call the interval caching model .W e relate traditional and interval caching policies under predictable load, and derive (near)-optimal replacement policies from their optimal interval caching counterparts.

Self Organizing Maps (SOM) are widely used in data mining and high-dimensional data visualization due to its unsupervised nature and robustness. Growing Self Organizing Maps (GSOM) is a variant of SOM algorithm which allows nodes to be grown so that it can represent the input space better. Without using a fixed 2D grid like SOM, GSOM starts with four nodes and keeps track of the quantization error in each node. New nodes are grown from an existing node if its error value exceeds a pre-defined threshold. Ability of the GSOM algorithm to represent input space accurately is vital to extend its applicability to a wider spectrum of problems. This ability can be improved by identifying nodes that represent low probability regions in the input space and removing them periodically from the map. This will improve the homogeneity and completeness of the final clustering result. A new extension to GSOM algorithm based on node deletion is proposed in this paper as a solution to this problem. Furthermore, two new algorithms inspired by cache replacement policies are presented. First algorithm is based on Adaptive Replacement Cache (ARC) and maintains two separate Least Recently Used (LRU) lists of the nodes. Second algorithm is built on Frequency Based Replacement policy (FBR) and maintains a single LRU list. These algorithms consider both recent and frequent trends in the GSOM grid before deciding on the nodes to be deleted. The experiments conducted suggest that the FBR based method for node deletion outperforms the standard algorithm and other existing node deletion methods.

Three directory schemes are investigated: the local cache (LC) scheme in which there is a cache at the network nodes to store the results of a previous query for a remote resource so that a subsequent query for that resource can be resolved locally, and two enhancements of this scheme, namely the regional cache server (RCS) scheme and the cooperating cache servers (CCS) scheme, in which the queries not resolved locally are funneled through designated nodes or servers to reduce multiple network searchers for a given resource. The performance of these schemes in terms of the network search rate is dependent on the cache sizes at the nodes and servers, the probability distribution for queuing resources, and the cache replacement policy used to displace resources from the cache to accommodate newly discovered ones. Two policies are investigated: A/sub 0/, which is the optimal policy for the LC scheme, and the least recently used policy, which is of practical importance. >

ProWGen: a synthetic workload generation tool for simulation evaluation of web proxy caches

Data caching in mobile clients is an important technique to enhance data availability and to improve data access time. Due to cache size limitations, cache replacement policies are used to find a suitable subset of items for eviction from the cache. In this paper, we study the issues of cache replacement for location-dependent data under a geometric location model and we have proposed a new cache replacement policy RAAR (Re-entry probability, Area of valid scope, Age, Rate of Access) by taking into account the spatial and temporal parameters. The experimental evaluations using synthetic datasets show that our replacement policy is effective in improving the system performance in terms of the cache hit ratio of mobile clients.

As the heterogeneity ofWeb clients increasing, the differentiated service becomes an important issue especially for e-commerce Web site. Web caching as a key accelerator on the Internet plays an important role in alleviating the client-perceived delay. To meet the Service Level Agreement (SLA) for clients without excessively over-provisioning resources, this paper proposes and evaluates a novel framework for enforcing Proportional Hit Rate. The framework combines the implement of Isolated Cache Model and the usage of control-theoretical approach for storage control. With system identification, the linear model is identified as well as the controller. At every sampling time, by dynamically reallocating storage spaces for different Web classes, the controller operates to guarantee the relationship of QoS metric among classes constant. The experimental results demonstrate the proposed approach achieves differentiated caching service with the enforcement of Greedy Dual Size Frequency (GDSF), Latest Recently Used (LRU) and Latest Frequently Used (LFU) cache replacement policies.