mReno: a practical multipath congestion control for communication networks

Vo, Phuong Luu; Lê, Tuấn Anh; Lee, Sungwon; Hong, Choong Seon; Kim, Byeongsik; Song, Hoyoung

doi:10.1007/s00607-013-0341-1

Cited by 11 publications

(2 citation statements)

References 11 publications

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“…It has been agreed that using multipath could bring enhancements to the communication reliability, and more importantly, generally increase the available bandwidth for bandwidth-hungry multimedia applications. Recent attention has been turned to the cases in multipath networks, e.g., fair resource allocation for users [21][22][23][24], effective resource allocation for elastic services [25,26], multipath transmission algorithm design [27][28][29], congestion control for heterogeneous flows [30], and some interesting research results on optimal resource allocation in multipath scenarios are obtained. For example, in [30] a unified approach is proposed to optimize performance of heterogeneous flows when inelastic and elastic services both exist in multipath networks.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation for multi-class services in multipath networks

Sun

Tian

2015

Performance Evaluation

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Section: Introductionmentioning

confidence: 99%

Resource allocation for multi-class services in multipath networks

Sun

Tian

2015

Performance Evaluation

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“…Multipath transmission control protocol (MPTCP) [17][18][19] also utilizes multiple paths to transmit data from a source to a destination. It is shown that MPTCP achieves high throughput, provides a smooth hand-off, and improves the high availability of TCP connection.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning - based adaptation and scheduling methods for multi-source DASH

Nguyen

Luu

et al. 2023

COMSIS J

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Dynamic adaptive streaming over HTTP (DASH) has been widely used in video streaming recently. In DASH, the client downloads video chunks in order from a server. The rate adaptation function at the video client enhances the user?s quality-of-experience (QoE) by choosing a suitable quality level for each video chunk to download based on the network condition. Today networks such as content delivery networks, edge caching networks, content centric networks, etc. usually replicate video contents on multiple cache nodes. We study video streaming from multiple sources in this work. In multi-source streaming, video chunks may arrive out of order due to different conditions of the network paths. Hence, to guarantee a high QoE, the video client needs not only rate adaptation, but also chunk scheduling. Reinforcement learning (RL) has emerged as the state-of-the-art control method in various fields in recent years. This paper proposes two algorithms for streaming from multiple sources: RL-based adaptation with greedy scheduling (RLAGS) and RL-based adaptation and scheduling (RLAS). We also build a simulation environment for training and evaluation. The efficiency of the proposed algorithms is proved via extensive simulations with real-trace data.

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Physical-traffic attentive vigorous routing (P-TAVR) and hot-spot detection to reduce the bottleneck in WSN

Praghash¹,

Ravi²

2018

Cluster Comput

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mReno: a practical multipath congestion control for communication networks

Cited by 11 publications

References 11 publications

Resource allocation for multi-class services in multipath networks

Resource allocation for multi-class services in multipath networks

Reinforcement learning - based adaptation and scheduling methods for multi-source DASH

Physical-traffic attentive vigorous routing (P-TAVR) and hot-spot detection to reduce the bottleneck in WSN

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