FPGA implementation of simplified Fuzzy LRU replacement algorithm

When a page fault occurs, the operating system has to choose a page to remove from memory to make room for the page that has to be brought in. The page replacement is done by swapping the required pages from backup storage to main memory and vice-versa. When a new page needs memory for allocation, page replacement algorithms decide which pages to swap out of the memory. In this paper, a comparison and analysis of five replacement algorithms: First in First out (FIFO), Least Recently Used (LRU), Clock, Most Recently Used algorithm (MRU), and Optimal Page Replacement (OPT) are made.

Virtual memory techniques are available in modern operating system will permits the execution of a program even if 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. The primary storage size has increased by multiple order of magnitude. With several gigabytes of primary memory, algorithms that require a periodic check of each and every memory frame are becoming less and least practical. Some basic assumptions used by the traditional page replacement algorithms were invalidated, resulting in a revival of research area. Currently the usage of the object oriented programming is much higher than the structured programming. Hence the locality of reference of user software has weakened. Due to the above problem for the page replacement methods also we have to think about new methods. In this paper mainly focused on the improvement of the processor performance. In order to achieve this, the number of page faults are tried to reduce, by using the page replacement method in the correct way. In modern operating systems mainly make use of the Least Recently Used (LRU) page replacement method, in which only the arrival time of the pages to the page frame are make used. Here we made an attempt to consider both the arrival time and the number of reference done on the page is considered. By consider these two parameter we developed a new algorithm Least Recently Used and Least Frequently Used (LRU-LFU) which will give a better performance than LRU page replacement method.

It is estimated that the annual network traffic of the internet will go beyond the threshold. Named Data Network will enter a new era in the world of networks because NDN nodes can store the data that has been requested by consumers in the content store so that one day when the data is requested by another consumer, it will be fast in the distribution of data. There are several optimization techniques based on replacement algorithms, one example is Least Recently Used (LRU), which is more focused on content that is rarely accessed and stores popular content in the content store. But the LRU has a weakness, which is only using the latest reference time and cannot distinguish between frequently or rarely the object being accessed. LRU modification is made to combine frequency and recently files in the decision stage to replace files so that the Modified-LRU can improve performance more optimally. In this paper, it is proposed the Modified-LRU algorithm. Modified-LRU takes an idea of SF-LRU but it is more simple in the process of deleting files. So, it can reduce the processing load. In this paper, the performance of LRU and Modified-LRU are compared. The simulation results show that the Modified-LRU is feasible to improve the LRU performance. The hit ratio is 8.7% greater compared to LRU, reducing delay by 60% and packet drop by 95%.

Page replacement algorithms play a crucial role in enhancing the performance issue brought on by variations in processor speeds and memory by effectively removing pages from computer memory to improve overall efficiency. The majority of these algorithms can address the page replacement problems, but their implementation is challenging. This paper introduces a new efficient page replacement algorithm, i.e., enhanced least-replacement (E-LRU) based on two introduced features used to select the victim page. By incorporating elements of traditional algorithms such as first in first out (FIFO) and least recently used (LRU), E-LRU presents itself as a new approach with potential benefits for memory management. This study evaluates the effectiveness of E-LRU in reducing power consumption by reducing cache faults and compares its performance to existing algorithms in various settings. The results provide insight into the advantages and disadvantages of E-LRU and essential perspectives on its potential benefits for contemporary memory management algorithms. Furthermore, the study puts E-LRU into the perspective of evolving algorithms and provides directions for future investigation and improvement in the ever-changing field of memory management. The study proved that E-LRU works better than FIFO and LRU algorithms.

As disk performance continues to lag behind that of memory systems and processors, virtual memory management becomes increasingly important for overall system performance. In this paper we study the page reference behavior of a collection of memory-intensive applications, and propose a new virtual memory page replacement algorithm, SEQ. SEQ detects long sequences of page faults and applies most-recently-used replacement to those sequences. Simulations show that for a large class of applications, SEQ performs close to the optimal replacement algorithm, and significantly better than Least-Recently-Used (LRU). In addition, SEQ performs similarly to LRU for applications that do not exhibit sequential faulting.

Memory management systems are generally custom-made where the replacement algorithm deals only with the number of memory hits. So, to reduce the page fault rate, we are proposing a page replacement algorithm called Farthest Page Replacement (FPR) algorithm. More the page faults lesser the performance. However, there are many other parameters which are to be considered like access time costs in the aspects of time and energy. Access time is the time required by a processor to access data or to write data from and to memory. Higher performance can be achieved with a reduction of access time costs. So, to decrease the access time and energy consumption, we are proposing an approach for selecting the page to be replaced.

The increasing popularity of photo sharing in social networking service (SNS) complicates the challenge of storing and transmitting large photo data for SNS providers. Distributed web caches are generally used by SNS providers to improve data transmission performance effectively. Two critical factors affect the efficiency of web caches: storage media and replacement algorithms. Solid-state drive (SSD) is often used in web caches due to its good performance. Random write performance and SSD endurance are directly affected by the characteristic of write amplification, this characteristic is also related to replacement algorithms. The least recently used (LRU) algorithm effectively computes cache hit ratio but induces severe write amplification. First in first out (FIFO) method minimizes write amplification, whereas a weak hit ratio increases bandwidth consumption. In this paper, we present a hybrid replacement strategy, namely, F-SkLRU. This strategy facilitates a good trade-off between performance and the cost of web cache. F-SkLRU takes advantage of sequential writing to reduce SSD wear. This strategy also consolidates multiple factors to improve hit ratio. Simulation results show that F-SkLRU can reduce bandwidth consumption by 14.11% to 23.4% in comparison with that of FIFO and LRU. The proposed strategy can also reduce the cost of SSD by up to 300% more than LRU can in extreme situations.

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.

Recent studies have shown that, in highly associative caches, the performance gap between the least recently used (LRU) and the theoretical optimal replacement algorithms is large, motivating the design of alternative replacement algorithms to improve cache performance. In LRU replacement, a line, after its last use, remains in the cache for a long time until it becomes the LRU line. Such deadlines unnecessarily reduce the cache capacity available for other lines. In addition, in multilevel caches, temporal reuse patterns are often inverted, showing in the L1 cache but, due to the filtering effect of the L1 cache, not showing in the L2 cache. At the L2, these lines appear to be brought in the cache but are never reaccessed until they are replaced. These lines unnecessarily pollute the L2 cache. This paper proposes a new counter-based approach to deal with the above problems. For the former problem, we predict lines that have become dead and replace them early from the L2 cache. For the latter problem, we identify never-reaccessed lines, bypass the L2 cache, and place them directly in the L1 cache. Both techniques are achieved through a single counter-based mechanism. In our approach, each line in the L2 cache is augmented with an event counter that is incremented when an event of interest such as certain cache accesses occurs. When the counter reaches a threshold, the line ";expires"; and becomes replaceable. Each line's threshold is unique and is dynamically learned. We propose and evaluate two new replacement algorithms: Access interval predictor (AIP) and live-time predictor (LvP). AIP and LvP speed up 10 capacity-constrained SPEC2000 benchmarks by up to 48 percent and 15 percent on average (7 percent on average for the whole 21 Spec2000 benchmarks). Cache bypassing further reduces L2 cache pollution and improves the average speedups to 17 percent (8 percent for the whole 21 Spec2000 benchmarks).

Access to the internet and WWW is growing extensively, which results in heavy network traffic. To reduce the network traffic proxy server is used. Proxy server reduces the load of server. If the cache replacement algorithm of proxy server's cache is efficient then proxy server will be helpful to reduce the network traffic in more efficient manner. In this paper we are considering proxy server cache to be Level 1 (L1) cache and storage cache of proxy server to be Level 2 (L2) cache. For collecting the real trace CC proxy server is used in an organization. Log of proxy server gives the information of various URLs accessed by various clients with time. For performing experiments various URLs were given a numeric identity. This paper proposes an efficient replacement algorithm on L1 for proxy server. The replacement algorithms taken into consideration are Least Recently Used (LRU), Least Frequently Used (LFU), First In First Out (FIFO). Access Pattern of L1 and L2 are different as if the desired page is not on L1 then it accesses to L2. Thus L1 is having better temporal locality than L2. Thus the replacement algorithm which is giving efficient results for L1 may not be suitable for L2. This paper also proposes a preeminent pair of replacement algorithms for L1 and L2 cache for proxy server.

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 paper takes multiple new factors into consideration in designing the optimal replacement algorithm for in-network content caching, i.e. requester's tolerance to common semantics information of requested content, latency requirement on content arrival. The paper proposes a new metric, named importance value, to indicate a content is important enough to be cached in the network. By formulating a 0-1 knapsack optimization problem, an algorithm based on dynamic programming is proposed (named MTT). The performance valuations show the proposed MTT algorithm improves over the existing Least Recently Used (LRU) and popularity based replacement algorithms in achieving shorter response time in various content popularity settings.

Locality of reference in program behavior has been studied and modelled extensively because of its application to CPU, cache and virtual memory design, code optimization, multiprogramming etc. In this paper we propose a scheme based on Markov chains for modelling the time interval between successive references to the same address in a program execution. Using this technique and trace driven simulations, it is shown that memory references are predictable and repetitive. This is used to improve miss ratios of memory replacement algorithms. Using trace driven simulations over a wide range of traces we get improvements up to 35% over the least recently used (LRU) replacement algorithm. >

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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). >