Impact of traffic mix on caching performance in a content-centric network

Fricker, Christine; Pierpoint, Robert; Roberts, James W.; Sbihi, Nada

doi:10.1109/infcomw.2012.6193511

Cited by 228 publications

(205 citation statements)

References 15 publications

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“…The independent reference model (IRM) has been frequently employed in literature (see [11], [12], [15] and references therein), to model how a particular object is referenced in an arbitrary traffic stream. Under the IRM model, requests for the objects/content occur in an infinite stream.…”

Section: A the Independent Reference Model (Irm)mentioning

confidence: 99%

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Information centric modeling for two-tier cache enabled cellular networks

Zaidi

Ghogho

McLernon

2015

2015 IEEE International Conference on Communication Workshop (ICCW)

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Abstract-In this article, we introduce a new metric called 'information centric coverage probability' to characterize the performance of a two-tier cache enabled cellular network. The proposed metric unifies the dynamics of in-network caching and heterogeneous networking to provide a unified performance measure. Specifically, it quantifies the probability that a mobile user (MU) is covered at a desired rate when a certain content is requested from a global content library. In other words, it quantifies the percentage of time when an MU can be served locally without paying the traffic penalties at backhaul, fronthaul and core networks. Caching dynamics are modeled by considering that the content which is least recently used (LRU) is evicted while the requested content is stored in the cache. The considered two-tier cellular model leverages coordination between the macro base-station (MBS) and the small cell base-stations (SBSs) to maximize the resource efficiency. More specifically, coordination between macro and small cells enables an arbitrary SBS to exploit the caches at other SBSs in the neighborhood. Thus reducing the requirement for huge and expensive memory modules at individual SBSs. The spatial dynamics of cellular network are modeled by borrowing well established tools from stochastic geometry. Propagation uncertainties are explicitly factored in characterization by considering the small scale Rayleigh fading and the large scale power-law path-loss model. It is shown that the information centric coverage probability is a function of (i) the size of caches at the SBSs and the MBS; (ii) the content eviction strategy; (iii) the underlying popularity law for referenced objects; (iv) the size of the global content library; (v) desired downlink transmission rate; (vi) the amount of spectrum allocated to each tier; (vii) pathloss exponent; and (viii) the deployment density of the SBSs and the MBSs. Our analysis reveals that significant performance gains can be harnessed with appropriate dimensioning of both cache sizes and deployment density. Finally, identification of memory limited vs. QoS limited operational regime for two-tier cellular networks is considered.

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Section: A the Independent Reference Model (Irm)mentioning

confidence: 99%

“…Empirical studies [15]- [17] have shown that the Zipf distribution renders itself as a most promising fit for describing the popularity of the content across various references. Under the Zipf popularity distribution p(i) < p(j) for i > j.…”

Section: B Content Popularity Law-a Case For Zipf Distributionmentioning

confidence: 99%

Information centric modeling for two-tier cache enabled cellular networks

Zaidi

Ghogho

McLernon

2015

2015 IEEE International Conference on Communication Workshop (ICCW)

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“…applying then the bounded convergence theorem (Section 13.6, [22]) to the latter identity and dividing by the expected number of requests E[λ] leads to formulas (8) and (9), as claimed.…”

Section: Irm-m Modelmentioning

confidence: 87%

An Inverse Problem Approach for Content Popularity Estimation

Olmos¹,

Kauffmann²

2016

Proceedings of the 9th EAI International Conference on Performance Evaluation Methodologies and Tools

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The Internet increasingly focuses on content, as exemplified by the now popular Information Centric Networking paradigm. This means, in particular, that estimating content popularities becomes essential to manage and distribute content pieces efficiently. In this paper, we show how to properly estimate content popularities from a traffic trace.Specifically, we consider the problem of the popularity inference in order to tune content-level performance models, e.g. caching models. In this context, special care must be taken due to the fact that an observer measures only the flow of requests, which differs from the model parameters, though both quantities are related by the model assumptions. Current studies, however, ignore this difference and use the observed data as model parameters. In this paper, we highlight the inverse problem that consists in determining parameters so that the flow of requests is properly predicted by the model. We then show how such an inverse problem can be solved using Maximum Likelihood Estimation. Based on two large traces from the Orange network and two synthetic datasets, we eventually quantify the importance of this inversion step for the performance evaluation accuracy.

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“…The effect of content caching on content-centric networks where multiple applications or services are running has been investigated in [6][7][8][9]. Carofiglio et al [6,7] have clarified the role of the CS allocation method (an exclusive method) on the cache hit rate of multiple applications running on content-centric networks by means of experiments and simulations.…”

Section: Related Workmentioning

confidence: 99%

“…In the hybrid method, several VCCN router instances within a CCN router are assigned their own parts of its CS and other instances jointly use the remaining CS. Previous studies of the effects of content caching on content-centric networks have focused only on the exclusive and shared methods [6][7][8][9]. However, when content request patterns are heterogeneous, these two methods can barely maintain a balance between network fairness for VCCN slices and overall network performance.…”

Section: Introductionmentioning

confidence: 99%

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2013

IJNGN

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Impact of traffic mix on caching performance in a content-centric network

Cited by 228 publications

References 15 publications

Information centric modeling for two-tier cache enabled cellular networks

Information centric modeling for two-tier cache enabled cellular networks

An Inverse Problem Approach for Content Popularity Estimation

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