Dueling CLOCK: Adaptive cache replacement policy based on the CLOCK algorithm

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.

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.

SummaryHybrid storage systems that consist of flash‐based solid state drives (SSDs) and traditional disks are now widely used. In hybrid storage systems, there exists a two‐level cache hierarchy that regard dynamic random access memory (DRAM) as the first level cache and SSD as the second level cache for disk storage. However, this two‐level cache hierarchy typically uses independent cache replacement policies for each level, which makes cache resource management inefficient and reduces system performance. In this paper, we propose a novel adaptive multi‐level cache (AMC) replacement algorithm in hybrid storage systems. The AMC algorithm adaptively adjusts cache blocks between DRAM and SSD cache levels using an integrated solution. AMC uses combined selective promote and demote operations to dynamically determine the level in which the blocks are to be cached. In this manner, the AMC algorithm achieves multi‐level cache exclusiveness and makes cache resource management more efficient. By using real‐life storage traces, our evaluation shows the proposed algorithm improves hybrid multi‐level cache performance and also increases the SSD lifetime compared with traditional multi‐level cache replacement algorithms. Copyright © 2015 John Wiley & Sons, Ltd.

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;

Multi-core processors improve performance, but they can create unpredictability owing to shared resources such as caches interfering. Cache partitioning is used to alleviate the Worst-Case Execution Time (WCET) estimation by isolating the shared cache across each thread to reduce interference. It does, however, prohibit data from being transferred between parallel threads running on different cores. In this paper we present (SRCP) a cache replacement mechanism for partitioned caches that is aware of data being shared across threads, prevents shared data from being replicated across partitions and frequently used data from being evicted from caches. Our technique outperforms TA-DRRIP and EHC, which are existing state-of-the-art cache replacement algorithms, by 13.34% in cache hit-rate and 10.4% in performance over LRU (least recently used) cache replacement policy.

Due to the ever increasing performance gap between the processor and the main memory, it becomes crucial to bridge that gap by designing an efficient memory hierarchy capable of reducing the average memory access time. The cache replacement algorithm plays a central role in designing an efficient memory hierarchy. Many of the recent studies in cache replacement algorithms have focused on improving L2 cache replacement algorithms by minimizing the miss count. However, depending on the dependency chain, cache miss bursts, and other factors, a processor's ability to partially hide the cost of an L2 cache miss varies; that is, cache miss costs are not uniform. Therefore, a better solution would account also for the aggregate miss cost in designing cache replacement algorithms. Our proposed solution combines the two principles of locality and cost-sensitivity into one which we call: LACS: Locality-Aware Cost-Sensitive cache replacement algorithm. LACS estimates a cache block's cost from the number of instructions the processor manages to issue during a cache miss on that block and then victimizes cache blocks with low cost and poor locality in order to maximize the overall cache performance. When LACS is evaluated using a uniprocessor architecture model, it speeds up 10 L2 cache performance-constrained SPEC CPU2000 benchmarks by up to 85% and 15% on average while not slowing down any of the 20 SPEC CPU2000 benchmarks evaluated. When evaluated using a dual-core CMP architecture model, LACS speeds up 6 SPEC CPU2000 benchmark pairs by up to 44% and 11% on average.

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.

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.

I/O subsystem manufacturers attempt to reduce latency by increasing disk rotation speeds, incorporating more intelligent disk scheduling algorithms, increasing I/O bus speed, using solid-state disks, and implementing caches at various places in the I/O stream. In this article, we examine the use of caching as a means to increase system response time and improve the data throughput of the disk subsystem. Caching can help to alleviate I/O subsystem bottlenecks caused by mechanical latencies. This article describes a caching strategy that offers the performance of caches twice its size. After explaining some basic caching issues, we examine some popular caching strategies and cache replacement algorithms, as well as the advantages and disadvantages of caching at different levels of the computer system hierarchy. Finally, we investigate the performance of three cache replacement algorithms: random replacement (RR), least recently used (LRU), and a frequency-based variation of LRU known as segmented LRU (SLRU). >

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.

The cache replacement policy is a crucial phase in caching-based systems that deal with the process of selecting applicable cache contents. In this paper, we propose two novel cache replacement algorithms based on the dataset obtained from a typical wireless femto network. In the first algorithm, called Weighted Least Frequently used with an Adaptive Time Window (WLF-ATW), we aim to make a balance between the network's traffic and the recognition of popular contents. The WLF-ATW algorithm takes the frequency and the recency information of files into account to ascertain the popularity of contents. We suggest another new cache replacement policy namely Fairness Scheduling-based with an Adaptive Time Window (FS-ATW) that is based on fairness scheduling in order to minimize the user's access delay. The novelty of our proposed FS-ATW lies in ranking clients according to their last situations that lead to a further user's experience. The effectiveness of these new algorithms is evaluated from the cache hit ratio, transferred byte volume, user's access delay, user's experience, and load balance. A comprehensive numerical evaluation shows that the performance of the proposed WLF-ATW and FS-ATW algorithms is significantly better than some existing cache replacement strategies.

An effectiveness-based adaptive cache replacement policy

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%.

By rapid growth of the Internet users and devices, thenumber of servers also increase simultaneously which causes the exponentialincrement of the internet traffic and static data. Handling these huge amountsof user requests and efficiently responding to them require high bandwidthlinks, powerful servers and robust equipment, which despite the availability ofthese requirements getting the full user satisfaction is extremely difficultand a tough challenge. In order to overcome the mentioned problem, the cacheservers are being used as a suitable solution. The performance of web cacheserver directly depends on its replacement policies. Several cache replacementpolicies have been proposed in literature each having varied hit rate (HR) andbyte hit rate (BHR) performances on different networks. The replacement policyproposed in this paper is a dynamic cache replacement policy which trainsitself utilizing previous network logs and by exploiting the data miningclustering algorithm. Once the training step is completed, the proposed policyutilizes the normalization formulas to score each metric of the enquiriesincluding recency, frequency, size and delay. Simulation results showed thatthe proposed policy has the optimum performance on different networks and itnot only improved the performance of web cache server in term of HR and BHR,but also decreases the data retrieval time (Delay Ratio (DR)) of the cacheservers.