Preeminent pair of replacement algorithms for L1 and L2 cache for proxy server

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.

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.

The cache hierarchy design in existing SMT and a superscalar processor is optimized for latency, but not for bandwidth. The size of the Level 1 (L1) data cache did not scale over the Past decade. Instead, larger unified Level 2 (L2) and Level 3 (L3) caches were introduced. The present paper is the part of the L3 cache. This paper describes last level cache memory decoding structure, which is designed mainly to improve performance. Functional verification as well as pre layout and post layout STA and ERC verifications are done on the 4MB cache. Results of each verification flow are presented. Based on the SDP methodology, good placement and routing, RC extraction, and noise analysis are achieved. SDP methodology is proven better than the RLS methodology for data path configurations. Cadence tools are used for performing the verification of SDP flows.

Web caching techniques have been widely used with the objective of caching as many web pages and web objects in the proxy server cache as possible to improve network performance. Web pre-fetching schemes have also been widely discussed where web pages and web objects are pre-fetched into the proxy server cache. This paper presents an approach that integrates web caching and web pre-fetching approach to improve the performance of proxy server’s cache.

Traditionally, file transfer protocol (FTP) servers are major archive providers and users apply the Archie server to locate FTP archives. With the extreme popularity of the WWW, Web servers are now important archive providers and users download archives via HTTP. To reduce HTTP traffic, proxy cache servers are deployed on the Internet. However, we find the hit rate of archives in cache servers is quite low. This study proposes a combination of caching and better searching mechanisms to alleviate the problem. We enable a proxy server to automatically collect WWW and FTP archives from its cache, organize them in the form of an FTP directory, and then offer the directory list to the Archie. Accordingly, users can find archives on WWW and FTP servers through the Archie, and they can directly download archives from the proxy server, thus improving the reuse of cached archives. A system was implemented and operated in a real environment to evaluate the approach and results are discussed.

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.

We propose an organization for the on-chip memory system of a chip multiprocessor in which 16 processors share a 16-Mbyte pool of 64 level-2 (L2) cache banks. The L2 cache is organized as a nonuniform cache architecture (NUCA) array with a switched network embedded in it for high performance. We show that this organization can support a spectrum of degrees of sharing: unshared, in which each processor owns a private portion of the cache, thus reducing hit latency, and completely shared, in which every processor shares the entire cache, thus minimizing misses, and every point in between. We measure the optimal degree of sharing for different cache bank mapping policies and also evaluate a per-application cache partitioning strategy. We conclude that a static NUCA organization with sharing degrees of 2 or 4 works best across a suite of commercial and scientific parallel workloads. We demonstrate that migratory dynamic NUCA approaches improve performance significantly for a subset of the workloads at the cost of increased complexity, especially as per-application cache partitioning strategies are applied. We also evaluate the energy efficiency of each design point in terms of network traffic, bank accesses, and external memory accesses.

360º video is an essential component of VR/AR/MR systems that provides immersive experience to the users. However, 360º video is associated with high bandwidth requirements. The required bandwidth can be reduced by exploiting the fact that users are interested in viewing only a part of the video scene and that users request viewports that overlap with each other. Motivated by the findings of our recent works where the benefits of caching video tiles at edge servers instead of caching entire 360º videos were shown, in this paper, we introduce the concept of virtual viewports that have the same number of tiles with the original viewports. The tiles forming these viewports are the most popular ones for each video and are determined by the users' requests. Then, we propose a proactive caching scheme that assumes unknown videos' and viewports' popularity. Our scheme determines which videos to cache as well as which is the optimal virtual viewport per video. Virtual viewports permit to lower the dimensionality of the cache optimization problem. To solve the problem, we first formulate the content placement of 360º videos in edge cache networks as a Markov Decision Process (MDP), and then we determine the optimal caching placement using the Deep Q-Network (DQN) algorithm. The proposed solution aims at maximizing the overall quality of the 360º videos delivered to the end-users by caching the most popular 360º videos at base quality along with a virtual viewport in high quality. We extensively evaluate the performance of the proposed system and compare it with that of known systems such as Least Frequently Used (LFU), Least Recently Used (LRU), First-In-First-Out (FIFO), over both synthetic and real 360º video traces. The results reveal the large benefits coming from proactive caching of virtual viewports instead of the original ones in terms of the overall quality of the rendered viewports, the cache hit ratio, and the servicing cost.

To satisfy rapidly increasing multimedia service requests from mobile users, content caching at the network edges (e.g., base stations) has been regarded as a promising technique in future mobile networks. In this paper, by virtue of Deep Reinforcement Learning (DRL) with respect to solving complicated control problems, we propose a framework on Double Deep Q-Network for cooperative edge caching in mobile networks. Particularly, we aim at minimizing the long-term average content fetching delay of mobile users without requiring any priori knowledge of content popularity distribution. Trace-driven simulation results show that our proposed framework outperforms some existing caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU) and First-In First-Out (FIFO) caching strategies by 7%, 11% and 9% improvements, respectively. Besides, our proposed work is further shown that only average 4% performance loss exists compared to an omniscient oracle algorithm.

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.

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%.

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).

The Least Recently Used (LRU) policy is often used as a page replacement algorithm for virtual memory, and it is considered to be good. The major problem is that it is time consuming algorithm. This paper presents an efficient hardware FPGA implementation for LRU algorithm based on fuzzy logic with reduced inference rules. In this proposal, the page's recency of reference is used as input crisp variable to calculate the fuzzy value. Then a knowledge base that composed of five inference rules are used to calculate the page replacement weights for the pages in the memory. These pages' weights are used to select the victim page for replacement when there is a page fault and no empty page frame available in the memory. All stages starting from the crisp input values until the decision of selection the victim page to be replaced is implemented using very careful designed FPGA circuits. The main features of the design are focusing on the time speed of decision and the FPGA area. That is done in two ways, reducing the fuzzy rules to five only and using the best of the FPGA capability. The results show that the merging between fuzzy logic and FPGA implementation is very promising to achieve the LRU page replacement algorithm in real time.

Increasing the speed of cache simulation to obtain hit/miss rates enables performance estimation, cache exploration for embedded systems and energy estimation. Previously, such simulations, particularly exact approaches, have been exclusively for caches which utilize the least recently used (LRU) replacement policy. In this paper, we propose a new, fast and exact cache simulation method for the First In First Out(FIFO) replacement policy. This method, called DEW, is able to simulate multiple level 1 cache configurations (different set sizes, associativities, and block sizes) with FIFO replacement policy. DEW utilizes a binomial tree based representation of cache configurations and a novel searching method to speed up simulation over single cache simulators like Dinero IV. Depending on different cache block sizes and benchmark applications, DEW operates around 8 to 40 times faster than Dinero IV. Dinero IV compares 2.17 to 19.42 times more cache ways than DEW to determine accurate miss rates.

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.