Performance analysis of probabilistic caching scheme using Markov chains

Tarnoi, Saran; Suppakitpaisarn, Vorapong; Kumwilaisak, Wuttipong; Ji, Yusheng

doi:10.1109/lcn.2015.7366282

Cited by 4 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fundamental assumption in the first part of this paper is that the requested contents at all instants, as integer random variables, are independent and identically distributed. This follows from the widely used independent reference model (IRM), a simplification of the actual request process that can be accurate with a large number of requesting users [21] or within a short time frame [32]. As the requested content follows a distribution that is time-invariant, the probability of content l ∈ C being requested can be denoted by υ l .…”

Section: A Content Request and Replacementmentioning

confidence: 99%

“…More recent works adopted Markov chains to model and analyze cache replacement schemes [17]- [20]. This class of studies generally focused on deriving the steady states of the aforementioned schemes and the mixing time of their underlying Markov chains [21] [22]. In our previous work [23], we considered the problem in reverse and designed the Markov chain underlying the replacement scheme so that a target set of content caching probabilities can be achieved.…”

Section: Introductionmentioning

confidence: 99%

“…First, we propose a general content replacement model based on probabilistic state transition as a unified model for cache replacement schemes. Unlike existing general model based on Markov chains, e.g., [21], we focus not just on the steady states but more on the dynamic change of the cache state distribution and describe this dynamic change in the vector space of state caching probabilities. Moreover, we introduce new ideas and results, such as the decomposition of state transition probability matrices based on contents and the mapping between state and content caching probabilities, to form a complete toolset for establishing our model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

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.

show abstract

Section: A Content Request and Replacementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

“…However, their work was done only through computer simulation and limited network settings were considered. Tarnoi et al developed, in [19], an analytical model based on Markov chains to compute the cache hit rate under the probabilistic scheme and with different cache replacement policies (including LRU). They evaluated their proposal through simulation, but the tests were done only in the case of a single cache and a two-nodes' network with a small-sized catalog of contents.…”

Section: Related Workmentioning

confidence: 99%

On the performance analysis of distributed caching systems using a customizable Markov chain model

Ben-Ammar

Hadjadj‐Aoul

Rubino

et al. 2019

Journal of Network and Computer Applications

View full text Add to dashboard Cite

In the last few years, Networks Operators (NO) have experienced an increased number of requests for video contents and rich media services, which are becoming increasingly popular. In view of the network scaling limitations, operators are developing their own caching systems to speed up the network performance. Indeed, disseminating caches in the infrastructure not only helps in absorbing the network's congestion, but in addition, brings content closer to users, which allows a reduced latency. Several studies have focused on improving the performance of such caching systems, especially in the context of Content-Centric Networking (CCN). In this paper, we propose a fairly generic model of caching systems that can be adapted very easily to represent different caching strategies, even the most advanced ones. Indeed, the proposed model of a single cache, named MACS, which stands for Markov chain-based Approximation of CCN Caching Systems, can be extended to represent an interconnection of caches under different schemes. In order to demonstrate the accuracy of our model, we proposed to derive the two most effective techniques in the literature, namely LCD and LRU-K, which may adapt to changing patterns of access. Simulation results using a discrete event simulator clearly show the accuracy of the proposed model under different network configurations.

show abstract

State Transition Field: A New Framework for Mobile Dynamic Caching

Gao

Ling³

et al. 2022

Wireless Networks

View full text Add to dashboard Cite

Performance analysis of probabilistic caching scheme using Markov chains

Cited by 4 publications

References 11 publications

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

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

On the performance analysis of distributed caching systems using a customizable Markov chain model

State Transition Field: A New Framework for Mobile Dynamic Caching

Contact Info

Product

Resources

About