A case for stateful forwarding plane

Cheng, Yu-Ling; Afanasyev, Alexander; Moiseenko, Ilya; Wang, Lan; Zhang, Beichuan; Zhang, Lixia

doi:10.1016/j.comcom.2013.01.005

Cited by 408 publications

(281 citation statements)

References 18 publications

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“…AIM is similar to previous hop-by-hop interest management schemes [9][8], but we employ random early NACK as shown Figure 1. Every NDN node controls the size of PIT (the number of interest packets in flight) per outgoing interface.…”

Section: Proposed Schemementioning

confidence: 99%

Active Interest Management for Improving Flow Completion Time in Named-Data Networking

Lee

Kim

et al. 2014

Proceedings of the 2014 CoNEXT on Student Workshop

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Interest aggregation is the primary feature of named-data networking (NDN). The interest aggregation reduces duplicated transmissions for multiple requests on the same data, but can delay loss recovery when data packets are lost. Since retransmitted interest packets for loss recovery cannot be distinguished from regular interest packets, the lost data packets can be recovered after the timeout of the interest aggregation. The delayed loss recovery can largely increase flow completion time, particularly on small content downloads. In this paper, we propose an active interest management (AIM) scheme based on random early NACK to avoid the delayed loss recovery as well as keeping the benefit of interest aggregation. Using NS-3, we show that our scheme significantly reduced flow completion time of both small and large content.

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Section: Proposed Schemementioning

confidence: 99%

Active Interest Management for Improving Flow Completion Time in Named-Data Networking

Lee

Kim

et al. 2014

Proceedings of the 2014 CoNEXT on Student Workshop

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“…The second objective is to present a scalable and fast convergence properties with respect to varying bandwidths, traffic patterns, and number of users at different times utilizing the network. The evaluation through simulations shows that ACCPndn can quickly and effectively respond against congestion problems in a timely manner and performs successfully even in the large networks as compared to two recent congestion control mechanisms namely NACK [20] and HoBHIS [9] in terms of the applied performance metrics. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

“…This difficulty has recently motivated researchers to explore ways of congestion control in NDN. Some of the relevant contributions are [9,10,16,17,18,19,20]. The main weak points of the proposed methods are: a too high sensitivity to their control parameters as well as the inability to predict congestion traffic well enough in advance.…”

Section: Introductionmentioning

confidence: 99%

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

Karami

2015

Journal of Network and Computer Applications

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Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based Internet infrastructure. However, NDN is subject to congestion when the number of data packets that reach one or various routers in a certain period of time is so high than its queue gets overflowed. To address this problem many congestion control protocols have been proposed in literature which, however, they are highly sensitive to their control parameters as well as unable to predict congestion traffic well enough in advance. This paper develops an Adaptive Congestion Control Protocol in NDN (ACCPndn) by learning capacities in two phases to control congestion traffics before they start impacting the network performance. In the first phase -adaptive trainingwe propose a Time-Lagged Feedforward Network (TLFN) optimized by hybridization of particle swarm optimization and genetic algorithm to predict the source of congestion together with the amount of congestion. In the second phase -fuzzy avoidance-we employ a non-linear fuzzy logic-based control system to make a proactive decision based on the outcomes of first phase in each router per interface to control and/or prevent packet drop well enough in advance. Extensive simulations and results show that ACCPndn sufficiently satisfies the applied performance metrics and outperforms two previous proposals such as NACK and HoBHIS in terms of the minimal packet drop and highutilization (retrying alternative paths) in bottleneck links to mitigate congestion traffics.

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“…The decision depends on the implemented forwarding strategy, which may take as inputs the face cost, determined by the routing protocol, and the face rank, maintained by the forwarding strategy [30]. Indeed, unlike traditional IP-based networks where forwarding is a "dumb" operation driven by routing, in NDN, each node maintains a soft-state for every pending Interest in the PIT, and when the Data packets are received from the face(s), the node can know how the downstream faces actually work.…”

Section: Multi-face Communicationsmentioning

confidence: 99%

Priority-Based Content Delivery in the Internet of Vehicles through Named Data Networking

Amadeo

Campolo

Molinaro

2016

JSAN

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Named Data Networking (NDN) has been recently proposed as a prominent solution for content delivery in the Internet of Vehicles (IoV), where cars equipped with a variety of wireless communication technologies exchange information aimed to support safety, traffic efficiency, monitoring and infotainment applications. The main NDN tenets, i.e., name-based communication and in-network caching, perfectly fit the demands of time-and spatially-relevant content requested by vehicles regardless of their provenance. However, existing vehicular NDN solutions have not been targeted to wisely ensure prioritized traffic treatment based on the specific needs of heterogeneous IoV content types. In this work, we propose a holistic NDN solution that, according to the demands of data traffic codified in NDN content names, dynamically shapes the NDN forwarding decisions to ensure the appropriate prioritization. Specifically, our proposal first selects the outgoing interface(s) (i.e., 802.11, LTE) for NDN packets and then properly tunes the timing of the actual transmissions. Simulation results show that the proposed enhancements succeed in achieving differentiated traffic treatment, while keeping traffic load under control.

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A case for stateful forwarding plane

Cited by 408 publications

References 18 publications

Active Interest Management for Improving Flow Completion Time in Named-Data Networking

Active Interest Management for Improving Flow Completion Time in Named-Data Networking

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

Priority-Based Content Delivery in the Internet of Vehicles through Named Data Networking

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