Caching on Named Data Network: a Survey and Future Research

Yovita, Leanna Vidya; Syambas, Nana Rachmana

doi:10.11591/ijece.v8i6.pp4456-4466

Cited by 20 publications

(8 citation statements)

References 43 publications

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“…More recently, the concept of Named Data Networks (NDN) has been proposed to solve a number of issues with the IP structure [46]. However, while some sources claim that NDN is a replacement to the IP layer [45], when looking at the original specification, it notes that ''the core IP routing protocols, BGP, IS-IS, and OSPF can be used asis.'' The other core tenant of NDN is that work is done by the router or a similar device to create the caching that is in place for the data.…”

Section: Related Workmentioning

confidence: 99%

DDD: Distributed Dataset DNS

White-Swift

Fumagalli

2022

Cluster Comput

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The advent of inexpensive data storage has resulted in larger and larger datasets as the cost of pruning data becomes more expensive then storing it for future insights. This decreasing cost of storage has also led to the practice of storing data in multiple locations for redundancy. However, without any uniform method of determining link costs to different storage sites, a dataset is not always retrieved from the most cost effective site. Distributed dataset DNS, or DDD, solves this problem in two key ways. The first allows “local” servers to provide meaningful information to a user in order to ensure that they target the location that offers the most advantageous network connection. The second allows other trusted servers to easily gain access to this information in a distributed way. These combined approaches aim to both lower aggregate network bandwidth usage and prevent single points of failure when retrieving dataset pointers.

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Section: Related Workmentioning

confidence: 99%

DDD: Distributed Dataset DNS

White-Swift

Fumagalli

2022

Cluster Comput

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“…The work carried out by Yovita and Syambas [16] have discussed about the importance of the caching mechanism which is highly essential for large distributed network. According to the study outcome, there are yet unsolved problems towards caching mechanism.…”

Section: Introductionmentioning

confidence: 99%

A novel cost-based replica server placement for optimal service quality in cloud-based content delivery network

Dharmapal,

Channakrishnaraju,

Krishnamur

2023

IJECE

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<span lang="EN-US">Replica server placement is one of the crucial concerns for a given geographic diversity associated with placement problems in content delivery network (CDN). After reviewing the existing literatures, it is noted that studies are more for solving placement problem in conventional CDN and not much over cloud-based CDN architectures, which some few studies are reported towards replica selection are much in its nascent stages of development. Moreover, such models are not benchmarked or practically assessed to prove its effectiveness. Hence, the proposed study introduces a novel design of computational framework associated with cloud-based CDN which can facilitate cost-effective replica server management for enhanced service delivery. Implemented using analytical research methodology, the simulated study outcome shows that proposed scheme offers reduced cost, reduced resource dependencies, reduced latency, and faster processing time in contrast to existing models of replica server placement.</span>

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“…Most state-of-the-art caching techniques are performance-driven and agnostic to variable QoS needs. Most previous studies focused on differentiating data based on factors such as popularity (LFU), recency (LRU), without considering QoS requirements per user or per application [5].…”

Section: Introductionmentioning

confidence: 99%

Investigating Multi-Tier and QoS-Aware Caching Based on ARC

Ait-Oucheggou,

Rubini,

Battou

et al. 2023

2023 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)

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Memory caching is a common practice to reduce application latencies by buffering relevant data in high speed memory. When the volume of data to cache is too large or a DRAM-based solution too expensive, several technologies such as NVM or high speed SSDs could complement DRAM to form a multi-tier cache. Additionally, most existing policies focus on categorizing the data based on factors like recency and frequency, setting aside the fact that applications/customers have varying Quality-of-Service requirements. This concept is well established in Cloud environment with Service Level Agreement (SLA). In this paper, by extending the Adaptive Replacement Cache (ARC), that uses recency and frequency lists, we propose a QoSaware Multi-tier Adaptive Replacement Cache (QM-ARC) policy with the ability to take into account data applications/customers priorities through the concept of penalty borrowed from the Cloud. QM-ARC is generic, as it can be applied whatever the number of tiers and can accommodate different penalty functions. Using synthetic and real traces, our solution improved QoS as compared to state-of-the-art work.

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Caching on Named Data Network: a Survey and Future Research

Cited by 20 publications

References 43 publications

DDD: Distributed Dataset DNS

DDD: Distributed Dataset DNS

A novel cost-based replica server placement for optimal service quality in cloud-based content delivery network

Investigating Multi-Tier and QoS-Aware Caching Based on ARC

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