Universal Caching Model and Markov-based cache analysis for Information Centric Networks

Among various factors contributing to performance of named data networking (NDN), the organization of caching is a key factor and has benefited from intense studies by the networking research community. The performed studies aimed at (1) finding the best strategy to adopt for content caching; (2) specifying the best location, and number of content stores (CS) in the network; and (3) defining the best cache replacement policy. Accessing and comparing the performance of the proposed solutions is as essential as the development of the proposals themselves. The present work aims at evaluating and comparing the behavior of four caching policies (i.e., random, least recently used (LRU), least frequently used (LFU), and first in first out (FIFO)) applied to NDN. Several network scenarios are used for simulation (2 topologies, varying the percentage of nodes of the content stores (5–100), 1 and 10 producers, 32 and 41 consumers). Five metrics are considered for the performance evaluation: cache hit ratio (CHR), network traffic, retrieval delay, interest re-transmissions, and the number of upstream hops. The content request follows the Zipf–Mandelbrot distribution (with skewness factor α=1.1 and α=0.75). LFU presents better performance in all considered metrics, except on the NDN testbed, with 41 consumers, 1 producer and a content request rate of 100 packets/s. For the level of content store from 50% to 100%, LRU presents a notably higher performance. Although the network behavior is similar for both skewness factors, when α=0.75, the CHR is significantly reduced, as expected.

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“…In [50,51], a caching replacement policy dubbed universal caching (UC) was proposed. The proposal was evaluated and compared to FIFO and LRU.…”

Section: Related Workmentioning

confidence: 99%

NDN Content Store and Caching Policies: Performance Evaluation

2022

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“…Coded caching has been a research focus during recent years [5][6][7][8][9]. Coded caching is finding its application in modern technologies and services, such as content delivery [10][11][12], mobile computing [13][14][15], and information-enteric networks [16]. Different aspects of coded caching have recently been studied among which we can refer to as [17], centralized [18,19] and decentralized [18,19] coded caching, placement [20] and delivery [21,22] schemes, as well as added pre-fetching phase [23], multi-casting [22,[24][25][26], scheduling [27], error correction [28], clustering [29], heterogeneity [12,25,30], the impact of file size [31,32], dealing with non-uniform user demands [33] and peak-time traffic reduction [1].…”

Section: Related Workmentioning

confidence: 99%

Cryptography in Hierarchical Coded Caching: System Model and Cost Analysis

Zolfaghari

Singh

Kumar

et al. 2021

Entropy

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The idea behind network caching is to reduce network traffic during peak hours via transmitting frequently-requested content items to end users during off-peak hours. However, due to limited cache sizes and unpredictable access patterns, this might not totally eliminate the need for data transmission during peak hours. Coded caching was introduced to further reduce the peak hour traffic. The idea of coded caching is based on sending coded content which can be decoded in different ways by different users. This allows the server to service multiple requests by transmitting a single content item. Research works regarding coded caching traditionally adopt a simple network topology consisting of a single server, a single hub, a shared link connecting the server to the hub, and private links which connect the users to the hub. Building on the results of Sengupta et al. (IEEE Trans. Inf. Forensics Secur., 2015), we propose and evaluate a yet more complex system model that takes into consideration both throughput and security via combining the mentioned ideas. It is demonstrated that the achievable rates in the proposed model are within a constant multiplicative and additive gap with the minimum secure rates.

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“…Some other strategies are recently proposed in the context of ICN. In [17], authors proposed a Universal Caching (UC) strategy designed for ICN. UC makes the replacement decision depending on a parameter assigned to any incoming content.…”

Section: B Cache Replacement Policiesmentioning

confidence: 99%

How to Cache in ICN-Based IoT Environments?

Meddeb

Dhraief

Belghith

et al. 2017

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)

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Abstract-Information-Centric Networking (ICN) is an emerging network paradigm based on name-identified data objects and in-network caching. Therefore, ICN contents are distributed in a scalable and cost-efficient manner. With the rapid growth of IoT traffic, ICN is intended to be a suitable architecture to support IoT networks. In fact, ICN provides unique persistent naming, in-network caching and multicast communications which reduce the data producer load and the response latency. Using ICN in an IoT environment requires a study of caching policies in terms of cache placement strategies and cache replacement policies. To this end, we address, in this paper, caching challenges with the aim to identify which caching policies are suitable for IoT networks. Simulation findings show that the combination of the consumer-cache caching strategy and the RR cache replacement policy is the most convenient in IoT environments in terms of hop reduction ratio, server hit reduction and response latency.

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Universal Caching Model and Markov-based cache analysis for Information Centric Networks

Cited by 17 publications

References 15 publications

NDN Content Store and Caching Policies: Performance Evaluation

NDN Content Store and Caching Policies: Performance Evaluation

Cryptography in Hierarchical Coded Caching: System Model and Cost Analysis

How to Cache in ICN-Based IoT Environments?

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