LR-LRU: A PACS-Oriented Intelligent Cache Replacement Policy

Wang, Yinyin; Yang, Yuwang; Ye, Lei; Ke, Yaqi; Wang, Qingguang

doi:10.1109/access.2019.2913961

Cited by 18 publications

(13 citation statements)

References 22 publications

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“…Each cluster is composed of several trajectory points and cluster centers. For the cluster centers obtained by the first step cluster, a clustering algorithm based on density-based clustering method (DBCM) [31][32][33][34][35] is used for the secondary clustering. Compared with the existing clustering algorithm (e.g., DBSCAN), DBCM does not require embedding the data in a vector space and maximizing explicitly the density field for each data point.…”

Section: Trajectory Point Clustering Methods Based On Two-step Clusteringmentioning

confidence: 99%

Self-adaptive trajectory prediction for improving traffic safety in cloud-edge based transportation systems

et al. 2021

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Intelligent transportation brings huge benefits to humans’ life and Industrial production in terms of vehicle control and traffic management. Now, the development of edge-cloud computing has once again promoted intelligent transportation into a new era. However, the development of intelligent transportation inevitably produces a large amount of data, which brings new challenges to data privacy protection and security. In this paper, we propose to develop an improved trajectory prediction framework based on the self-adaptive trajectory prediction model (SATP), which could significantly enhance traffic safety in transportation systems. The proposed framework is capable of guaranteeing the accurate trajectory prediction of moving target under different application scenarios. In particular, to reduce the size of original trajectory point data collected by sensors, the angle change and minimum description length (MDL) principle are first combined to remove the redundant points in raw trajectories. The obtained points can then be reduced for model using the two-step clustering method. To further enhance the prediction performance, we add the “self-transfer” to the original model to solve the problems that the state of original SATP model may be discontinuous. Furthermore, we propose to develop a trajectory complementation method based on Bezier curve to improve the prediction accuracy. Finally, by comparing the two-step clustering method with the commonly-used SinglePass and density-based clustering method (DBCM) algorithms, the proposed two-step clustering policy greatly reduce the time cost of clustering. At the same time, by comparing the improved SATP model with the original model, the results show that the improved SATP method can greatly improve the speed of prediction model.

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Section: Trajectory Point Clustering Methods Based On Two-step Clusteringmentioning

confidence: 99%

Self-adaptive trajectory prediction for improving traffic safety in cloud-edge based transportation systems

et al. 2021

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“…They predict the content popularity as prior knowledge and make caching decision according to it with huge memory consumption. Some traditional lightweight strategies make caching decisions without prior knowledge and cache content frequently with simple replacement strategies used [23]. Gradually, content that remains in the cache is supposed to be popular.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Caching Decision Strategy Based on Node Edge-Degree for Information Centric Networking

Yang¹,

Deng

Wang

2024

IEEE Access

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In-network caching as a core function in ICN can greatly improve the content distribution efficiency and reduce redundant network traffic. It is a challenge to design caching strategies in ICN to improve the performance of in-network caching. Traditional lightweight strategies are inefficient due to problems such as edge caching pollution and slow diffusion speed. Current caching strategy with popularity prediction does not fully consider the content capacity of ICN network. They usually have huge computation overhead and cannot meet line-speed requirement. This paper focuses on the research of lightweight on-path caching. Our caching strategy is proposed according to the edge-degree of routers along the forwarding path. This strategy can quickly form a hierarchical content placement with little overhead. A large number of experiments are conducted to examine the performance of our strategy in different topologies and workloads. The simulation result reveals that our caching strategy can reduce the average download latency by 10.2% and increase the cache hit ratio by 9.6% at most compared with other lightweight strategies.

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“…Consequently, LRU does not capture the long-term popularity of objects. An uneven request arrival pattern might result in less popular objects being identified as popular ones [20], [23]. For example, when LRU is used, the cache might replace all the popular videos with contents that have recent and short-lived popularity.…”

Section: Related Workmentioning

confidence: 99%

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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LR-LRU: A PACS-Oriented Intelligent Cache Replacement Policy

Cited by 18 publications

References 22 publications

Self-adaptive trajectory prediction for improving traffic safety in cloud-edge based transportation systems

Self-adaptive trajectory prediction for improving traffic safety in cloud-edge based transportation systems

A Lightweight Caching Decision Strategy Based on Node Edge-Degree for Information Centric Networking

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

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