Least Recently Used Algorithm Research Articles

Impact of Segmentation and Popularity-based Cache Replacement Policies on Named Data Networking

The data distribution mechanism of internet protocol (IP) technology is inefficient because it necessitates the user to await a response from the server. Named data networking (NDN) is a cutting-edge technology being assessed for enhancing IP networks, primarily because it incorporates a data packet caching technique on every router. However, the effectiveness of this approach is highly dependent on the router's content capacity, thus requiring the use data replacement mechanism when the router capacity is full. The least recently used (LRU) method is employed for cache replacement policy; yet, it is considered ineffective as it neglects the content's popularity. The LRU algorithm replaces the infrequently requested data, leading to inefficient caching of popular data when multiple users constantly request it. To address this problem, we propose a segmented LRU (SLRU) replacement strategy that considers content popularity. The SLRU will evaluate both popular content and content that has previously been popular in two segment categories, namely the probationary and protected segments. Icarus simulator was used to evaluate multiple comprehensive scenarios. Our experimental results show that the SLRU obtains a better cache hit ratio (CHR) and able to minimize latency and link load compared to existing cache replacement policies such as First In, First Out (FIFO), LRU, and Climb.

Real-Time Memory Data Optimization Mechanism of Edge IoT Agent

With the full development of disk-resident databases (DRDB) in recent years, it is widely used in business and transactional applications. In long-term use, some problems of disk databases are gradually exposed. For applications with high real-time requirements, the performance of using disk database is not satisfactory. In the context of the booming development of the Internet of things, domestic real-time databases have also gradually developed. Still, most of them only support the storage, processing, and analysis of data values with fewer data types, which can not fully meet the current industrial process control system data types, complex sources, fast update speed, and other needs. Facing the business needs of efficient data collection and storage of the Internet of things, this paper optimizes the transaction processing efficiency and data storage performance of the memory database, constructs a lightweight real-time memory database transaction processing and data storage model, realizes a lightweight real-time memory database transaction processing and data storage model, and improves the reliability and efficiency of the database. Through simulation, we proved that the cache hit rate of the cache replacement algorithm proposed in this paper is higher than the traditional LRU (Least Recently Used) algorithm. Using the cache replacement algorithm proposed in this paper can improve the performance of the system cache.

A Memory-Aware Spark Cache Replacement Strategy

<p>Spark is currently the most widely used distributed computing framework, and its key data abstraction concept, Resilient Distributed Dataset (RDD), brings significant performance improvements in big data computing. In application scenarios, Spark jobs often need to replace RDDs due to insufficient memory. Spark uses the Least Recently Used (LRU) algorithm by default as the cache replacement strategy. This algorithm only considers the most recent use time of RDDs as the replacement basis. This characteristic may cause the RDDs that need to be reused to be evicted when performing cache replacement, resulting in a decrease in Spark performance. In response to the above problems, this paper proposes a memory-aware Spark cache replacement strategy, which comprehensively considers the cluster memory usage, RDD size, RDD dependencies, usage times and other information when performing cache replacement and selects the RDDs to be evicted. Furthermore, this paper designs extensive corresponding experiments to test and analyze the performance of the memory-aware Spark cache replacement strategy. The experimental data show that the proposed strategy can improve the performance by up to 13% compared with the LRU algorithm in different scenarios.</p> <p> </p>

Reduction of Power Consumption in Norflash Memory Based Wearable Electronics with Improved LRU Algorithm

Norflash storage is a very important memory module for the practical application in the smart wearable electronics. For the versatility and complexity of its functional applications, it is highly demanded to build Norflash flash devices in the system. The Norflash memory devices have advantages in terms of the fast transmission speed, small volume, and good stability, when the algorithm calls the storage module to record the data and read user inquired storage data. Each startup of access in the storage module will cause a lot of power consumption. The improved Least Recently Used (LRU) algorithm is implemented to the Norflash memory device of the intelligent wearable electronics in order to reduce the power consumption in this work. The type M29F010B of ST’s chips is used for this investigation. The interface design method of the chip is analyzed in details. This approach reduces the number of accesses in the wearable electronics devices while reading from the recorded Norflash pages in the memory, comparing with the ordinary storage methods under the original operations. It shows that the improved LRU algorithm could effectively reduce the system power consumption and therefore to enhance the battery efficiency up to 8.8%.

PR-LRU: partial random LRU technique for performance improvement of last level cache

As chip multiprocessors (CMP) have become eminent in all areas of computing, it is inevitable for the operating system to schedule processes efficiently on different cores. These multi-cores pose different challenges of which shared resource contention is the dominant one, as cores share resources like last level cache (LLC) and main memory. This can lead to poor and unpredictable performance of the threads running on the system. The cache replacement policy of LLC becomes critical in managing the cache data in an efficient way. Though prominent, least recently used (LRU) algorithm has some issues with applications which do not follow the temporal locality pattern. This study proposes a modification to the LRU algorithm where a random selection of the victim from 'N' LRU blocks yields better results than the conventional method. The evaluation of the algorithm is carried out using Multi2sim simulator using Parsec and Splash2 benchmarks. The results show an overall performance improvement in hit ratio up to 6% and 2% over LRU for PARSEC and SPLASH2 benchmarks respectively.

PR-LRU: partial random LRU technique for performance improvement of last level cache

As chip multiprocessors (CMP) have become eminent in all areas of computing, it is inevitable for the operating system to schedule processes efficiently on different cores. These multi-cores pose different challenges of which shared resource contention is the dominant one, as cores share resources like last level cache (LLC) and main memory. This can lead to poor and unpredictable performance of the threads running on the system. The cache replacement policy of LLC becomes critical in managing the cache data in an efficient way. Though prominent, least recently used (LRU) algorithm has some issues with applications which do not follow the temporal locality pattern. This study proposes a modification to the LRU algorithm where a random selection of the victim from 'N' LRU blocks yields better results than the conventional method. The evaluation of the algorithm is carried out using Multi2sim simulator using Parsec and Splash2 benchmarks. The results show an overall performance improvement in hit ratio up to 6% and 2% over LRU for PARSEC and SPLASH2 benchmarks respectively.

Distributed Cache with Utilizing Squid Proxy Server and LRU Algorithm

<p>In relation to the dissemination of information, the Internet is one of the fastest media to do so. The internet’s presence is growing very swiftly and rapidly, so it has become recognized by people from all walks of life. For that, the people need the appropriate way to maintain effectiveness in the use of the Internet. The following paper describes a study of the distribution of the cache, which is performed by the squid proxy server by creating a storage network design on Linux. Cache documents that are stored in the proxy server will be distributed to another over a network storage server. The process of caching on the proxy server is using the Least Recently Used (LRU) Algorithm. This research was carried out by developing the existing method of caching server process, then it is to be added a unit as a backup storage device for the data that must be erased because of the replacement policy applied to the squid proxy server. This study is looking at how the hit ratio and byte hit ratio after adding the storage server compared to not having a storage server. At the end of this research, it is concluded that the distributed cache processes a hit ratio and byte hit ratio higher than the cache on the current proxy server.</p>

Fault-tolerant Mechanism for Cloud Storage System with Integrity Verification Mechanism

A self-adaption fault-tolerant mechanism based on access frequency (SFMAF) is proposed for the cloud storage platform, wherein this mechanism aims at adopting the approximate LRU (Least Recently Used) algorithm to maintain a file access frequency table in order to self-adaptively adjust the fault-tolerant mode. Specifically, the replica redundancy mechanism is adopted in SFMAF for the frequently accessed files and RS (Reed-Solomon) erasure code tolerant mechanism is adopted in SFMAF for the rarely accessed files. The experiment result shows: compared with the replica redundancy mechanism, SFMAF can reduce the transmission flow of the internal system data at the cost of increasing CPU and memory usage rates in an acceptable range, namely: the system storage space is reduced.

Selection and replacement algorithms for memory performance improvement in Spark

SummaryAs a parallel computation framework, Spark can cache repeatedly resilient distribution datasets (RDDs) partitions in different nodes to speed up the process of computation. However, Spark does not have a good mechanism to select reasonable RDDs to cache their partitions in limited memory. In this paper, we propose a novel selection algorithm, by which Spark can automatically select the RDDs to cache their partitions in memory according to the number of use for RDDs. Our selection algorithm speeds up iterative computations. Nevertheless, when many new RDDs are chosen to cache their partitions in memory while limited memory has been full of them, the system will adopt the least recently used (LRU) replacement algorithm. However, the LRU algorithm only considers whether the RDDs partitions are recently used while ignoring other factors such as the computation cost and so on. We also put forward a novel replacement algorithm called weight replacement (WR) algorithm, which takes comprehensive consideration of the partitions computation cost, the number of use for partitions, and the sizes of the partitions. Experiment results show that with our selection algorithm, Spark calculates faster than without the algorithm, and we find that Spark with WR algorithm shows better performance. Copyright © 2015 John Wiley & Sons, Ltd.

Least Recently Used Page Replacement using Last Use Distance (LRUL)

Virtual memory technique is used in modern OS which permits the execution of a program while it is partially available in memory thus providing an illusion of very large memory to the user and freeing the user from the concern of large program size. This uses a page replacement technique such as first in first out (FIFO), least recently used (LRU), optimal etc. to replace a page in memory when a frame is needed and no free frame is available in memory. Here page replacement policy severely affects the performance of virtual memory. It has been observed that LRU approximates optimal so LRU and its variants are quite common in use in operating system as a reasonable choice of page replacement algorithm. But LRU performs very poor by generating continuous page faults while accessing looping pattern if all the pages does not fit in the memory simultaneously. This paper presents a simple modified LRU algorithm called LRUL overcoming this problem using the concept of last use distance (LUD). It is observed that the new algorithm gives better results than LRU. General Terms Operating system, page replacement algorithm.

Rapid Manipulation Method for Massive Remote Sensing Images Based on LRU Algorithm

With the rapid development of remote sensing (RS) technology, RS image data is growing larger and larger, which makes general image manipulation method limited in feasibility, speed and efficiency. In this paper, a rapid manipulation method of massive RS data based on Least Recently Used (LRU) algorithm in operating system page scheduling is proposed. Its core content includes data read-write based on memory mapping, data organization based on image pyramid with layers and blocks and caching based on LRU algorithm. The experiment shows that this method can greatly increase the speed and efficiency of massive RS images processing.

An admission-control technique for delay reduction in proxy caching

We evaluate an admission-control (screening) policy for proxy server caching that augments the LRU (Least Recently Used) algorithm. Our results are useful for operating a proxy server deployed by an Internet Service Provider or for an enterprise (forward) proxy server through which employees browse the Internet. The admission-control policy classifies documents as cacheable and non-cacheable based on loading times and then uses LRU to operate the cache. The mathematical analysis of the admission control approach is particularly challenging because it considers the dynamics of the caching policy (LRU) operating at the proxy server. Our results show substantial reduction (around 50% in our numerical simulations) in user delay. The improvement can be even larger at high levels of proxy server capacity or when the user demand patterns are more random. An approximation technique provides near optimal results for large problem sizes demonstrating that our approach can be used in real-world situations. We also show that the traffic downloaded by the proxy server does not change much (as compared to LRU) as a result of screening. A detailed simulation study on LRU and other caching algorithms validate the theoretical results and provide additional insights. Furthermore, we have provided ways to estimate policy parameter values using real-world trace data.

Competitive Analysis of the LRFU Paging Algorithm

Abstract. We present a competitive analysis of the LRFU paging algorithm, a hybrid of the LRU (Least Recently Used) and LFU (Least Frequently Used) paging algorithms. We show that the competitive ratio of LRFU is k + $\lceil$ log(1-λ ) / logλ $\rceil$ - 1, where 1/2 < ≤ λ ≤ 1 is the decay parameter used by the LRFU algorithm, and k is the size of the cache. This supplies, in particular, the first natural paging algorithms that are competitive but are not optimally competitive, answering a question of Borodin and El-Yaniv. Although LRFU, as it turns out, is not optimally competitive, it is expected to behave well in practice, as it combines the benefits of both LRU and LFU.

A neural network-based replacement strategy for high performance computer architectures

We propose a new scheme for the replacement of cache lines in high performance computer systems. Preliminary research, to date, indicates that neural networks (NNs) have great potential in the area of statistical predictions [1]. This attribute of neural networks is used in our work to develop a neural network-based replacement policy which can effectively eliminate dead lines from the cache memory by predicting the sequence of memory addresses referenced by the central processing unit (CPU) of a computer system. The proposed strategy may, therefore, provide better cache performance as compared to the conventional schemes, such as: LRU (Least Recently Used), FIFO (First In First Out), and MRU (Most Recently Used) algorithms. In fact, we observed from the simulation experiments that a carefully designed neural network-based replacement scheme does provide excellent performance as compared to the LRU scheme. The new approach can be applied to the page replacement and prefetching algorithms in virtual memory systems.

The unconventional replacement algorithms

This work proposes several new schemes for the replacement of cache lines/blocks in high performance computer systems. Our algorithms rely on judiciously chosen neural networks for accurate real-time statistical predictions. These algorithms, therefore, provide better cache performance as compared to the conventional LRU (Least Recently Used) algorithm. Simulation results indicate that the proposed set of replacement strategies can provide a performance improvement of as much as 16.4711% over the LRU algorithm for our selected benchmark trace files. The results are based on an experimentation involving 6 neural network paradigms and 21 different cache configurations. Excellent performance of the neural network-based replacement strategies means that this new approach can be studied as an alternative to the existing page replacement and prefetching algorithms in virtual memory systems.

FPGA implementation of the LRU algorithm for video compression

Cache based vector quantization algorithms (CVQ) have been shown to be efficient in constant quality and fixed bit rate video transmission applications. However, real time implementation of CVQ requires an efficient implementation of a stack replacement policy such as the least recently used (LRU) algorithm. The authors present an FPGA implementation of the LRU algorithm in CVQ for video compression. >