Least fresh first cache replacement policy for NDN-based IoT networks

Meddeb, Maroua; Dhraief, Amine; Belghith, Abdelfettah; Monteil, Thierry; Drira, Khalil; Mathkour, Hassan

doi:10.1016/j.pmcj.2018.12.002

Cited by 38 publications

(19 citation statements)

References 10 publications

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“…The replacement strategy used for all considered caching techniques is the Least Recently Used (LRU). We could have easily used other replacement techniques [37], but here we are solely concentrating on the design of an efficient caching technique. Table 1 summarizes the system parameters used in our simulations.…”

Section: A Simulation Scenariomentioning

confidence: 99%

Efficient Pooling and Collaborative Cache Management for NDN/IoT Networks

2021

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Named Data Networking (NDN) has been recognized as a lever to the Internet of Things (IoT). One of the most founding features of NDN is in-network caching to improve data availability and reduce retrieval delays and network load. Despite the existence of several caching decision algorithms, the fetching and distribution of contents with minimum resource utilization remains a great challenge. In this paper, we propose an efficient caching technique named PoolCache that augments the effective caching capacity of some defined conglomerates of nodes. This is accomplished by pooling the various caches and manage them in a way to insure zero content redundancy within any defined node conglomerate. The resulting high diversity of cached contents throughout the network tacitly amounts to much better overall performances. We conducted extensive simulations using the CCNsim simulator to evaluate the performance of PoolCache and compare it to that of some well known caching strategies. Simulations using a large Transit Stub topology show that PoolCache clearly outperforms the other caching strategies in terms of a much greater content diversity and consequently a limited number of content evictions, a much better cache hit ratio, and a much lower content retrieval delay. Simulations also showed that PoolCache benefits from any eventual content popularity.

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Section: A Simulation Scenariomentioning

confidence: 99%

Efficient Pooling and Collaborative Cache Management for NDN/IoT Networks

2021

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“…When the lifetime is not known in advance, forecasting methods can be developed to estimate, at the consumer/cacher side, when a new version of the content will be generated at the source [50]. For example, the Least Fresh First (LFF) policy in Reference [50] has been conceived for transient traffic originated by sensors and evicts the items that are supposed to be replaced soon based on forecasting of sensors-related future events. In order to predict the residual lifetime of the packet, LFF leverages an Autoregressive Moving Average (ARMA) model for time series forecasting.…”

Section: Replacement Policiesmentioning

confidence: 99%

“…Therefore, to avoid cache inconsistency and correctly compute the residual lifetime, a timestamp should be added in the Data packet header. If adding additional timing information is not possible, e.g., when data are provided by IoV sensors implementing low-rate and low-power protocols and there are limitations in the packet size, consumers can apply forecasting methods, like the one considered in Reference [50], to estimate the residual lifetime.…”

Section: Cache Consistencymentioning

confidence: 99%

A Literature Review on Caching Transient Contents in Vehicular Named Data Networking

Amadeo

2021

Telecom

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Vehicular Named Data Networking (VNDN) is a revolutionary information-centric architecture specifically conceived for vehicular networks and characterized by name-based forwarding and in-network caching. So far, a variety of caching schemes have been proposed for VNDN that work in presence of static Data packets, like traditional Internet contents. However, with the advent of Internet of Things (IoT) and Internet of Vehicles (IoV) applications, large sets of vehicular contents are expected to be transient, i.e., they are characterized by a limited lifetime and become invalid after the latter expires. This is the case of information related to road traffic or parking lot availability, which can change after a few minutes—or even after a few seconds—it has been generated at the source. The transiency of contents may highly influence the network performance, including the gain of in-network caching. Therefore, in this paper, we consider the dissemination of transient contents in vehicular networks and its effects on VNDN caching. By providing a detailed review of related work, we identify the main challenges and objectives when caching transient contents, e.g., to avoid cache inconsistency, to minimize the Age of Information (AoI) and the retrieval latency, and the main strategies to fulfill them. We scan the existing caching and replacement policies specifically designed for transient contents in VNDN and, finally, we outline interesting research perspectives.

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“…Among them, LRU is the most widely used in ICN in-network caching research [16,17]. In addition, LFF [18] (Least Fresh First) is used in IoT networks; it aims to use the ARMA model to predict the time of the next content update event in the sensor, so that the predicted remaining time will be set as the freshness of contents in caching nodes.…”

Section: Related Workmentioning

confidence: 99%

Popularity-Aware In-Network Caching for Edge Named Data Network

Yin

Jiang

Mino

et al. 2021

Wireless Communications and Mobile Computing

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The traditional centralized network architecture can lead to a bandwidth bottleneck in the core network. In contrast, in the information-centric network, decentralized in-network caching can alleviate the traffic flow pressure from the network center to the edge. In this paper, a popularity-aware in-network caching policy, namely, Pop, is proposed to achieve an optimal caching of network contents in the resource-constrained edge networks. Specifically, Pop senses content popularity and distributes content caching without adding additional hardware and traffic overhead. We conduct extensive performance evaluation experiments by using ndnSIM. The experiments showed that the Pop policy achieves 54.39% cloud service hit reduction ratio and 22.76% user request average hop reduction ratio and outperforms other policies including Leave Copy Everywhere, Leave Copy Down, Probabilistic Caching, and Random choice caching. In addition, we proposed an ideal caching policy (Ideal) as a baseline whose popularity is known in advance; the gap of Pop and Ideal in cloud service hit reduction ratio is 4.36%, and the gap in user request average hop reduction ratio is only 1.47%. More simulation results further show the accuracy of Pop in perceiving popularity of contents, and Pop has good robustness in different request scenarios.

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Least fresh first cache replacement policy for NDN-based IoT networks

Cited by 38 publications

References 10 publications

Efficient Pooling and Collaborative Cache Management for NDN/IoT Networks

Efficient Pooling and Collaborative Cache Management for NDN/IoT Networks

A Literature Review on Caching Transient Contents in Vehicular Named Data Networking

Popularity-Aware In-Network Caching for Edge Named Data Network

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