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

Gao, Jie; Zhao, Lian; Shen, Xuemin

doi:10.1109/twc.2019.2940667

Cited by 17 publications

(8 citation statements)

References 25 publications

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“…According to (22), the average delay increases with the refreshing probability p. This is reasonable since the queueing delay increases with traffic load. As p decreases with W , the average delay decreases with the refreshing window.…”

Section: Service Delay Analysismentioning

confidence: 90%

“…Cache update deals with the popularity variation in spatial or temporal domains. Specifically, the items with faded popularity are discarded to make room for new popular ones, so as to maintain the content hit rate [21], [22]. With perfect knowledge of content popularity, existing works have proposed to update the cache during offpeak hours, by utilizing the idle transmission resources opportunistically [23], [24].…”

Section: Literature Reviewmentioning

confidence: 99%

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AoI-Delay Tradeoff in Mobile Edge Caching with Freshness-Aware Content Refreshing

Zhang

Wang

Luo

et al. 2020

Preprint

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Mobile edge caching can effectively reduce service delay but may introduce information staleness, calling for timely content refreshing. However, content refreshing consumes additional transmission resources and may degrade the delay performance of mobile systems. In this work, we propose a freshness-aware refreshing scheme to balance the service delay and content freshness measured by Age of Information (AoI). Specifically, the cached content items will be refreshed to the up-to-date version upon user requests if the AoI exceeds a certain threshold (named as refreshing window). The average AoI and service delay are derived in closed forms approximately, which reveals an AoI-delay tradeoff relationship with respect to the refreshing window. In addition, the refreshing window is optimized to minimize the average delay while meeting the AoI requirements, and the results indicate to set a smaller refreshing window for the popular content items. Extensive simulations are conducted on the OMNeT++ platform to validate the analytical results. The results indicate that the proposed scheme can restrain frequent refreshing as the request arrival rate increases, whereby the average delay can be reduced by around 80% while maintaining the AoI below one second in heavily-loaded scenarios.

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Section: Service Delay Analysismentioning

confidence: 90%

Section: Literature Reviewmentioning

confidence: 99%

AoI-Delay Tradeoff in Mobile Edge Caching with Freshness-Aware Content Refreshing

Zhang

Wang

Luo

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…where ρ LRU e(k,m)|k represents the conditional probability that content e(k, m) is the least recently used content given that the cache is in state k. The probability ρ LRU e(k,m)|k can be found, as a simplified special case under IRM, based on Lemma 1 in the second part of this two-part paper, which addresses the more general case of time-varying content popularity [31].…”

Section: Lrumentioning

confidence: 99%

“…The first part of this work focuses on the case when the content popularity is time-invariant while the second part investigates the scenario of time-varying content popularity [31]. Through the two parts of this paper, we demonstrate that a replacement scheme corresponds to a unique state transition matrix, which in turn generates a unique STF, and the resulting STF jointly determines the performance of the replacement scheme with the content request statistics.…”

Section: Introductionmentioning

confidence: 98%

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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“…The latter case requires to download new versions of the cached items from time to time to guarantee the content effectiveness. Extensive efforts have been devoted on cache update considering the content popularity variation, including popularity prediction and content replacement algorithm design [11], [12]. However, the dynamic content variations of cached items has been seldom considered.…”

Section: Introductionmentioning

confidence: 99%

Low-Latency and Fresh Content Provision in Information-Centric Vehicular Networks

Zhang¹,

Li²,

Luo³

et al. 2020

Preprint

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In this paper, the content service provision of information-centric vehicular networks (ICVNs) is investigated from the aspect of mobile edge caching, considering the dynamic driving-related context information. To provide up-to-date information with low latency, two schemes are designed for cache update and content delivery at the roadside units (RSUs). The roadside unit centric (RSUC) scheme decouples cache update and content delivery through bandwidth splitting, where the cached content items are updated regularly in a round-robin manner. The request adaptive (ReA) scheme updates the cached content items upon user requests with certain probabilities. The performance of both proposed schemes are analyzed, whereby the average age of information (AoI) and service latency are derived in closed forms. Surprisingly, the AoI-latency trade-off does not always exist, and frequent cache update can degrade both performances. Thus, the RSUC and ReA schemes are further optimized to balance the AoI and latency. Extensive simulations are conducted on SUMO and OMNeT++ simulators, and the results show that the proposed schemes can reduce service latency by up to 80% while guaranteeing content freshness in heavily loaded ICVNs.

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

Cited by 17 publications

References 25 publications

AoI-Delay Tradeoff in Mobile Edge Caching with Freshness-Aware Content Refreshing

AoI-Delay Tradeoff in Mobile Edge Caching with Freshness-Aware Content Refreshing

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

Low-Latency and Fresh Content Provision in Information-Centric Vehicular Networks

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