Information-centric networking with built-in network coding to achieve multisource transmission at network-layer

Liu, Waixi; Shu, Yu; Tan, Guang; Cai, Jun

doi:10.1016/j.comnet.2015.05.009

Cited by 34 publications

(19 citation statements)

References 29 publications

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“…Meanwhile, MF are sensitive to delays, particularly for user‐facing applications (eg, web searches, social networks, or recommendation systems). Therefore, the objective for MF is to maximize the fraction of flows that achieves their deadlines: the deadline meet rate (DMR) …”

Section: Introductionmentioning

confidence: 98%

“…Therefore, the objective for MF is to maximize the fraction of flows that achieves their deadlines: the deadline meet rate (DMR). 7 Some early flow-scheduling methods for data center network (DCN), such as, Equal-Cost Multipath Routing (ECMP), create a risk of load imbalance: some paths could become congested owing to a concentration of EF1/EF2 whereas others remain underutilized. This arises, in part, because the size of mice/elephant flows varies significantly and the time that they occupy a path varies considerably.…”

Section: Introductionmentioning

confidence: 99%

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Mix‐flow scheduling using deep reinforcement learning for software‐defined data‐center networks

Liu¹,

Cai

Wang

et al. 2019

Internet Technology Letters

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For a mix‐flow scenario in software‐defined data‐center networks, how to simultaneously achieve the different performance requirements of the different types of flows is a considerable challenge. This paper proposes a mix‐flow scheduling scheme based on deep reinforcement learning (DRL). This paper establishes three private link sets for three types of flows. Then, DRL is employed to adaptively and intelligently allocate bandwidth for each type of flow according to the traffic variations across time and space. A novel metric is designed as a function of DRL's reward to guide the process of simultaneously maximizing the deadline meet rate for mice flows (MF) and minimizing the flow completion time for elephant flows. Within these three private link sets, three flow‐scheduling strategies (ie, priority‐based allocation for MF, stable matching‐based allocation for elephant flows with unknown sizes, and proportion‐based allocation for elephant flows with known sizes) are employed. A simulation demonstrates the effectiveness of the proposed scheme compared with previous methods (Fincher and pFabric). DRL‐Flow's overhead also is minimal to satisfy the scalability well and is deployable in a large‐scale network.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Mix‐flow scheduling using deep reinforcement learning for software‐defined data‐center networks

Liu¹,

Cai

Wang

et al. 2019

Internet Technology Letters

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“…The number of vectors received at each node is distributed randomly across the network. We solved the MIP model using CPlex 7 . The evaluation benchmarks are generated using HElib library for homomorphic encryption [12] which implements an optimized version of the BGV (Brakerski-Gentry-Vaikuntanathan) fully homomorphic encryption scheme.…”

Section: A Scenario and Parameters Settingmentioning

confidence: 99%

“…In [6], the authors investigate the required architectural changes that arise from the semantic difference between naming. In [7], the authors propose a novel signature scheme compatible with network coding, they propose also a forwarding plane to observe the network state, such as network failure, link transmission performance and distribution of coded packets. In [8], the authors design a special Interest coding and forwarding strategies for getting linearly independant coded blocks simultaneously from multiple nodes.…”

Section: Introductionmentioning

confidence: 99%

Towards authenticated network coding for named data networking

Boussaha

Challal

Bessedik

et al. 2017

2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

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Abstract-Named Data Networking represents a novel and an alternative approach to the current host based Internet architecture, in which the content becomes the core of the communication model. In this paper, we propose to improve named data networking robustness and throughput performances by introducing network coding functionalities. We also propose an optimized authentication model based on homomorphic encryption to deal with the "pollution attacks" problem in which malicious nodes can flood the network with bad packets and prevent the reconstruction of information at recipients. We first present our optimized homomorphic signature scheme with respect to the practical constraints such as the processing overhead generated by complex cryptographic operations and we define the network coding scheme tailored for named data networking. Our numerical results demonstrate that the use of this optimized model reduces by 10% the overall calculation cost induced by cryptographic operations compared to a hop-by-hop coding and verification model.

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“…[14][15][16][17][18][19][20][21][22][23][24] However, a coded block may be cached by multiple content routers on its way to consumers, and it may be sent to the same consumer in future requests. [14][15][16][17][18][19][20][21][22][23][24] However, a coded block may be cached by multiple content routers on its way to consumers, and it may be sent to the same consumer in future requests.…”

mentioning

confidence: 99%

Node importance to community based caching strategy for information centric networking

Shan

Cai

Liu

et al. 2018

Concurrency and Computation

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Information Centric Networking (ICN) is a novel future network architecture that is focusing on content distribution. Its ubiquitous caching schemes can improve network performance. In this paper, we propose a node importance to community based caching scheme with network coding in ICN, which is named as NICNC. For each community, content router makes cache decision depending on its node importance to community to make content cached more reasonable in temporal and spatial distribution. Moreover, by applying network coding into ICN, one coded blocks containing information of multiple chunks can satisfy multiple interests for different chunks sent by different consumers. This can significantly enhance cache diversity and cache hit rate without increasing cache capacity. Experimental results show that our scheme can improve the network performance at many aspects, such as average download time, cache hit rate, and instantaneous hop reduction rate. KEYWORDScaching, information centric networking, network coding, node importance to community INTRODUCTIONIn the recent years, the primary uses of the Internet have been changed to content distribution. Therefore, a new concept of Information Centric Networking (ICN) is proposed. In this concept, content routers forward packets based on content names instead of host addresses. The information-centric approach to future Internet is being widely studied by several research projects, such as 4WARD, 1 PSIRP 2 in Europe and NDN/CCN, 3 and DONA 4 in US. Content Centric Networking (CCN) 5 is the most popular architecture of ICN. In CCN, a consumer requests for content by sending an Interest packet that is only identified by content name. Any node receiving the Interest packet and having the data specified by Interest can respond the consumer with a Data packet that carries the actual content. When CCN nodes forward Interest packet, they set up state information, so that the returning Data packet can take the path created by the Interest back to consumer. Moreover, each CCN node can cache the Data passed by it in the ContentStore according to the cache strategy. Ubiquitous in-network caching is one of the salient ICN features. With caching, requests are satisfied by content routers that are closer to consumers, rather than original content server. The primary aims of in-networking caching are load balancing to reduce the load of original server and Concurrency Computat Pract Exper. 2019;31:e4797. wileyonlinelibrary.com/journal/cpe

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Information-centric networking with built-in network coding to achieve multisource transmission at network-layer

Cited by 34 publications

References 29 publications

Mix‐flow scheduling using deep reinforcement learning for software‐defined data‐center networks

Mix‐flow scheduling using deep reinforcement learning for software‐defined data‐center networks

Towards authenticated network coding for named data networking

Node importance to community based caching strategy for information centric networking

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