CWRP: An efficient and classical weight ranking policy for enhancing cache performance

Swain, Debabrata; Marar, Shreerag; Motwani, Nikhil; Hiwarkar, Vivek; Valakunde, Nandakishor D.

doi:10.1109/iciip.2017.8313747

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…As discussed in Section I, we focus on popularity estimation in VoD systems based on request statistics of videos. The underlying principles behind most cache replacement algorithms are popularity estimation methods using the request record [3], [17], [19], [20]. That is, in every cache replacement algorithm in general, the objects least likely to be requested are identified using past access patterns so that those objects can be removed from the cache when required.…”

Section: Related Workmentioning

confidence: 99%

“…While the LFU method captures long-term popularity of objects, it responds poorly to changes in user demand as it does not emphasize recent history over earlier reports. Since LFU cannot distinguish between requests that occurred recently with requests that occurred significantly earlier, it can incorrectly identify many ''has-beens'' as popular objects because of their high request count in the past [20], [22]. For example, when the LFU method is used, earlier episode of a drama series that is no longer popular might remain in the cache for a long time.…”

Section: Related Workmentioning

confidence: 99%

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Estimating Video Popularity From Past Request Arrival Times in a VoD System

et al. 2020

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Efficient provision of Video-on-Demand (VoD) services requires that popular videos are stored in a cache close to users. Video popularity (defined by requested count) prediction is, therefore, important for optimal choice of videos to be cached. The popularity of a video depends on many factors and, as a result, changes dynamically with time. Accurate video popularity estimation that can promptly respond to the variations in video popularity then becomes crucial. In this paper, we analyze a method, called Minimal Inverted Pyramid Distance (MIPD), to estimate a video popularity measure called the Inverted Pyramid Distance (IPD). MIPD requires choice of a parameter, k, representing the number of past requests from each video used to calculate its IPD. We derive, analytically, expressions to determine an optimal value for k, given the requirement on ranking a certain number of videos with specified confidence. In order to assess the prediction efficiency of MIPD, we have compared it by simulations against four other prediction methods: Least Recency Used (LRU), Least Frequency Used (LFU), Least Recently/Frequently Used (LRFU), and Exponential Weighted Moving Average (EWMA). Lacking real data, we have, based on an extensive literature review of real-life VoD system, designed a model of VoD system to provide a realistic simulation of videos with different patterns of popularity variation, using the Zipf (heavy-tailed) distribution of popularity and a non-homogeneous Poisson process for requests. From a large number of simulations, we conclude that the performance of MIPD is, in general, superior to all of the other four methods. INDEX TERMS Popularity prediction, video-on-demand, pre-placement, request statistic, Zipf distribution, non-homogeneous Poisson process. BILL MORAN (Life Member, IEEE) received the B.Sc. degree (Hons.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Estimating Video Popularity From Past Request Arrival Times in a VoD System

et al. 2020

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“…Debabala Swain [8] have used an alternative, an adaptive replacement algorithm that ranks objects in the cache based on weight as defined in (1):…”

Section: Related Workmentioning

confidence: 99%

“…Adaptive Weight Ranking Policy(AWRP) attempts to give weight to each page depending on three factors: recency, frequency and total number of access to be made. This overcomes the problem of traditional algorithms such as least recently used LRU, First Input First Output FIFO and Clock Adaptive Replacement CAR [8,9]. Intelligent replacement algorithms were proposed to overcome problems of conventional replacement algorithms and to enhance the performance of web cache.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Cooperative Adaptive Weight Ranking Policy via dynamic aging based on NB and J48 classifiers

Al-Qudah

Olanrewaju

Azman

2017

IJEEI

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The increased usage of World Wide Web leads to increase in network traffic and create a bottleneck over the internet performance. For most people, the accessing speed or the response time is the most critical factor when using the internet. Reducing response time was done by using web proxy cache technique that storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. But, the cache size is limited, so cache replacement algorithms are used to remove pages from the cache when it is full. On the other hand, the conventional algorithms for replacement such as Least Recently Use (LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomised Policy, etc. may discard essential pages just before use. Furthermore, using conventional algorithms cannot be well optimized since it requires some decision to evict intelligently before a page is replaced. Hence, this paper proposes an integration of Adaptive Weight Ranking Policy (AWRP) with intelligent classifiers (NB-AWRP-DA and J48-AWRP-DA) via dynamic aging factor. To enhance classifiers power of prediction before integrating them with AWRP, particle swarm optimization (PSO) automated wrapper feature selection methods are used to choose the best subset of features that are relevant and influence classifiers prediction accuracy. Experimental Result shows that NB-AWRP-DA enhances the performance of web proxy cache across multi proxy datasets by 4.

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“…On the basis of studying FIFO, LRU and other cache algorithms, Yu et al proposed minimum reference count (LRC) [7] and minimum effective reference technique (LERC) [8].Ho et al [9] proposed a cache update technique that enables users to replace a single RDD partition by partially updating RDD, thus avoiding the large overhead caused by loading the entire RDD. Swain et al [10] designed an AWRP algorithm, calculating the weight according to the access frequency of the object. Chen et al [11] proposed a register allocation (RA) replacement algorithm to replace the RDD partition with the latest end of use time.…”

Section: Introductionmentioning

confidence: 99%

Research Cache Replacement Strategy in Memory Optimization of Spark

Zhao¹,

Liu²,

Du³

et al. 2019

International Journal of New Technology and Research

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In Spark, using LRU to implement RDD cache replacement. Its metrics do not take the data characteristics of Spark into account, resulting in memory not being effectively utilized, affecting task execution efficiency. This paper optimizes the LWR (Least Weight Replacement) algorithm, and a new replacement algorithm is proposed. Considering the parallel computing, the dependency integrity impact factor is added to the weight calculation to make the RDD partition weight value more accurate, so as to improve the accuracy of the cache replacement object selection, and the relevant factor values are dynamically adjusted according to the task execution, so that the cache replacement can adapt to the changes in the task execution process. The source of the experimental data set for this article is the Stanford Network Analysis Project. According to comparison experiments, this methods can effectively improve task execution efficiency. Index Terms-Parallel computing, Resilient DistributedDataset, Spark, self-adaptive Recently Used) to replace the block. The metrics method in

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CWRP: An efficient and classical weight ranking policy for enhancing cache performance

Cited by 7 publications

References 14 publications

Estimating Video Popularity From Past Request Arrival Times in a VoD System

Estimating Video Popularity From Past Request Arrival Times in a VoD System

Intelligent Cooperative Adaptive Weight Ranking Policy via dynamic aging based on NB and J48 classifiers

Research Cache Replacement Strategy in Memory Optimization of Spark

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