Cache Replacement Based on Distance to Origin Servers

Kamiyama, Noriaki; Nakano, Yukiko I.; Shiomoto, Kohei

doi:10.1109/tnsm.2016.2600240

Cited by 11 publications

(2 citation statements)

References 26 publications

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“…Two replacement schemes were proposed for the video-on-demand service in femtocells [27], the first of which exploits content access history for improving cache hit ratio while the second exploits information on user access delay to promote service fairness. Kamiyama et al proposed a replacement scheme for content delivery networks based on the hop count from end users to the content server with an objective to reduce network traffic load [28]. Chattopadhyay et al investigated content replacement based on the knowledge of cached contents at neighbor base stations for a cellular network with densely deployed base stations [29].…”

Section: Introductionmentioning

confidence: 99%

The Study of Dynamic Caching via State Transition Field—the Case of Time-Invariant Popularity

Gao¹,

Zhao²,

Xuemin

et al. 2019

IEEE Trans. Wireless Commun.

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This two-part paper investigates cache replacement schemes with the objective of developing a general model to unify the analysis of various replacement schemes and illustrate their features. To achieve this goal, we study the dynamic process of caching in the vector space and introduce the concept of state transition field (STF) to model and characterize replacement schemes. In the first part of this work, we consider the case of time-invariant content popularity based on the independent reference model (IRM). In such case, we demonstrate that the resulting STFs are static, and each replacement scheme leads to a unique STF. The STF determines the expected trace of the dynamic change in the cache state distribution, as a result of content requests and replacements, from any initial point. Moreover, given the replacement scheme, the STF is only determined by the content popularity. Using four example schemes including random replacement (RR) and least recently used (LRU), we show that the STF can be used to analyze replacement schemes such as finding their steady states, highlighting their differences, and revealing insights regarding the impact of knowledge of content popularity. Based on the above results, STF is shown to be useful for characterizing and illustrating replacement schemes.Extensive numeric results are presented to demonstrate analytical STFs and STFs from simulations for the considered example replacement schemes. Index Termscache replacement policy, probabilistic caching, cache state transition, IRM, online caching, mobile edge caching.

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Section: Introductionmentioning

confidence: 99%

The Study of Dynamic Caching via State Transition Field—the Case of Time-Invariant Popularity

Gao¹,

Zhao²,

Xuemin

et al. 2019

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…Depending on different CDN deployments, the distributed cache nodes may have the capability to search other nodes' caches [10] for a requested content and copy it from a closer and cost-efficient neighboring node. Current academic research viewpoint [5,7] and state-of-the-art technology point of view [3,8,9,11] provide an understanding that only relies on objective network metrics and cloud resource constraints whereas this paper introduces a brand new foundational understanding of the impact of QoE on load balancing and resource optimization. The edge CDN cache pulls the content, and end-users get the service via their video players [12].…”

Section: Introductionmentioning

confidence: 99%