Survey of named data networks: future of internet

Nilay, Kumar; Yadav, Kuldeep

doi:10.1007/s41870-017-0014-y

Cited by 11 publications

(14 citation statements)

References 26 publications

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“…The Named Data Networking (NDN) paradigm [1] A network of N content routers are assumed and equipped with needed storage space to cache D content chunks from C content sources. The data chunk size is considered as M bytes.…”

Section: System Model and Proposed Forwarding Mechanismmentioning

confidence: 99%

Reinforcement learning inspired forwarding strategy for information centric networks using Q-learning algorithm

Delvadia

Dutta

2023

Preprint

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Content interest forwarding is a prominent research area in Information Centric Network (ICN). An efficient forwarding strategy can significantly enhance the user level performance parameters such as data retrieval latency. It also helps to minimize the origin server load, network congestion and overhead. Reinforcement learning is widely accepted for taking efficient routing decisions in network. This paper introduces a Q-learning driven forwarding strategy in ICN for interest packets. We have investigated the feasibility of exploiting reinforcement learning mechanism named Q-Learning for Named Data Network (NDN) paradigm of ICN. By revising Q-Learning mechanism to address the inherent challenges related to overhead and latency for content retrieval, this paper introduces design and implementation of Q-Learning based forwarding mechanism. It aims to gain learning through historical events and selects best mechanism to forward interest. The performance investigation of proposed protocol is carried out using simulator named ndnSIM-2.0. Outcomes are compared by integrating proposed protocol with LCD (Leave Copy Down), LCE (Leave Copy Everywhere, CL4M (Cache Less for More) and ProbCache (Probability driven caching). Protocol behaviour is also compared against recent routing and forwarding mechanisms. The considered performance parameters are data retrieval delay, server hit rate, network overhead, network throughput and network load. The experimental outcomes conclude that integration of proposed forwarding strategy to state-of-the-art protocols lead to performance enhancement up to 10–35%. The integrated protocol variants are also sensitive to any changes in network compared to existing approaches.

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Section: System Model and Proposed Forwarding Mechanismmentioning

confidence: 99%

Reinforcement learning inspired forwarding strategy for information centric networks using Q-learning algorithm

Delvadia

Dutta

2023

Preprint

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“…In the latest research, Wibowo et al [62] show that LSR is on average 24% more efficient than GR in terms of data transmission in NDN, traditional routing methods based on link-state and distance vector routing strategies may be utilized without modification [132]. This is accomplished by a single basic change: the NDN router publishes name prefixes rather than IP prefixes.…”

Section: B Challenges and Open Issues In Link State Routing In Ndnmentioning

confidence: 99%

Named Data Networking: A Survey on Routing Strategies

et al. 2022

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Data has become an increasingly significant component of the Internet as it has increased in popularity. Where people care more about the data than the location of data. The Named Data Network (NDN) took this concept and paired it with the idea of making data a core component rather than host addresses. Because of its in-network caching capability, NDN will become a better contender than current Transmission Control Protocol and Internet Protocol (TCP/IP) based networks as data traffic grows exponentially. NDNrelated challenges are available for investigation as NDN becomes more important. Routing in NDN is another essential domain that needs to be addressed, and several approaches have been presented to address routing concerns in NDN. In this study, we discuss and highlight NDN and its routing strategies comprehensively. In addition, this research illustrates a comparison of important routing paradigms to emphasis the breadth of routing research in NDN. Also, we investigate the routing attributes of NDN and expose the latest literature on this critical topic. Finally, this study provides useful insights into the emerging areas of guidance in the NDN to assist future studies in addressing challenges and open research issues. INDEX TERMSNamed Data Networks, Open Research issues in NDN, Routing in NDN I.

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“…In recent times, due to tremendous rise in number of internet users, the internet data traffic has massively increased. The CISCO forecasts that this huge user base with an extensive number of connected devices and greater internet speed will exchange around 400 Exabytes data in a month 1 . Out of this estimated data traffic, 82% congestion is due to video content having massive size and higher bandwidth need with demand of faster data retrieval 1 .…”

Section: Introductionmentioning

confidence: 99%

“…The CISCO forecasts that this huge user base with an extensive number of connected devices and greater internet speed will exchange around 400 Exabytes data in a month 1 . Out of this estimated data traffic, 82% congestion is due to video content having massive size and higher bandwidth need with demand of faster data retrieval 1 . The current internet users are concerned about retrieving desired data with minimal latency irrespective of content location given that integrity and authenticity are assured.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning inspired forwarding strategy for information centric networks using Q‐learning algorithm

Delvadia,

Dutta

2024

Int J Communication

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SummaryContent interest forwarding is a prominent research area in Information Centric Network (ICN). An efficient forwarding strategy can significantly improve data retrieval latency, origin server load, network congestion, and overhead. The state‐of‐the‐art work is either driven by flooding approach trying to minimize the adverse effect of Interest flooding or path‐driven approach trying to minimize the additional cost of maintaining routing information. These approaches are less efficient due to storm issues and excessive overhead. Proposed protocol aims to forward interest to the nearest cache without worrying about FIB construction and with significant improvement in latency and overhead. This paper presents the feasibility of integrating reinforcement learning based Q‐learning strategy for forwarding in ICN. By revising Q‐learning to address the inherent challenges, we introduce Q‐learning based interest packets and data packets forwarding mechanisms, namely, IPQ‐learning and DPQ‐learning. It aims to gain self‐learning through historical events and selects best next node to forward interest. Each node in the network acts as an agent with aim of forwarding packet to best next hop according to the Q value such that content can be fetched within fastest possible route and every action returns to be a learning process, which improves the accuracy of the Q value. The performance investigation of protocol in ndnSIM‐2.0 shows the improvement in a range of 10%–35% for metrics such as data retrieval delay, server hit rate, network overhead, network throughput, and network load. Outcomes are compared by integrating proposed protocol with state‐of‐the‐art caching protocols and also against recent forwarding mechanisms.

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Survey of named data networks: future of internet

Cited by 11 publications

References 26 publications

Reinforcement learning inspired forwarding strategy for information centric networks using Q-learning algorithm

Reinforcement learning inspired forwarding strategy for information centric networks using Q-learning algorithm

Named Data Networking: A Survey on Routing Strategies

Reinforcement learning inspired forwarding strategy for information centric networks using Q‐learning algorithm

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