Performance evaluation of CQIC and TCP BBR in mobile network

Zhang, Zhong; Hamchaoui, Isabelle; Khatoun, Rida; Serhrouchni, Ahmed

doi:10.1109/icin.2018.8401585

Cited by 12 publications

(9 citation statements)

References 11 publications

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“…The most important issue of BBR in fast-varying networks, which we aim to solve with BBR-S, is inherent in its bandwidth estimation procedure: since capacity samples are filtered using a simple max filter, any temporary upswing in the path capacity influences BBR's bandwidth estimate until the sample exits the max filter window [25]. The window itself has no fixed length, but expires after 10 subsequent sentinel packets, whose RTT can be much larger than the minimum RTT of the connection.…”

Section: B Bbr's Issues In Mobile Networkmentioning

confidence: 99%

“…The derivation of ( 34) follows from the fact that E C 2 t = RCC ,t . In the following step, we use (25) to remove the Kalman gain from the equation. We can now subtract the expected value in ( 35) from ( 32) to write the unbiased estimator for the process noise covariance:…”

Section: B Tracking Capacity With the Atkfmentioning

confidence: 99%

“…The full ATKF is given by the full estimator in (42)-(44), along with the standard update formulas in ( 22)- (25). While it is more complex than a max filter, its complexity is still linear in the number of packets, and an efficient implementation can run in real time with no issues even in mobile processor.…”

Section: B Tracking Capacity With the Atkfmentioning

confidence: 99%

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BBR-S: A Low-Latency BBR Modification for Fast-Varying Connections

et al. 2021

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The new possibilities offered by 5G and beyond networks have led to a change in the focus of congestion control from capacity maximization for web browsing and file transfer to latency-sensitive interactive and real-time services, and consequently to a renaissance of research on the subject, whose most well-known result is Google's Bottleneck Bandwidth and Round-trip propagation time (BBR) algorithm. BBR's promise is to operate at the optimal working point of a connection, with the minimum Round Trip Time (RTT) and full capacity utilization, striking the balance between resource use efficiency and latency performance. However, while it provides significant performance improvements over legacy mechanisms such as Cubic, it can significantly overestimate the capacity of fast-varying mobile connections, leading to unreliable service and large potential swings in the RTT. Our BBR-S algorithm replaces the max filter that causes this overestimation issue with an Adaptive Tobit Kalman Filter (ATKF), an innovation on the Kalman filter that can deal with unknown noise statistics and saturated measurements, achieving a 40% reduction in the average RTT over BBR, which increases to 60% when considering worst-case latency, while maintaining over 95% of the throughput in 4G and 5G networks.

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Section: B Bbr's Issues In Mobile Networkmentioning

confidence: 99%

Section: B Tracking Capacity With the Atkfmentioning

confidence: 99%

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BBR-S: A Low-Latency BBR Modification for Fast-Varying Connections

et al. 2021

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“…[24] a tuning in such CCAs might be needed in order to keep buffers appropriately filled in LTE networks. Although recently proposed client-centric CCAs such as PBE-CC [4] or CQIC [5] can overcome BBR limitations in LTE and adapt to fast variations in link capacity by measuring the available bandwidth in the RAN, they require client-side modifications and introduce additional energy consumption in the UE.…”

Section: Related Workmentioning

confidence: 99%

“…Retrieving and making such radio information available to upper layers require either additional services at the base station (eNB) or modifications in the UE. The latter approach is used by recently proposed crosslayer CCAs such as PBE-CC [4] or CQIC [5] that rely on client-side radio measurements to adjust their sending rate. Although these approaches guarantee higher link utilization, they also introduce computational overhead and additional power consumption at the UE.…”

Section: Introductionmentioning

confidence: 99%

Cross-layer Loss Discrimination Algorithms for MEC in 4G networks

Diarra

Dabbous

Ismail

et al. 2021

2021 IEEE 22nd International Conference on High Performance Switching and Routing (HPSR)

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Traditional loss-based Congestion Control Algorithms (CCAs) suffer from performance issues over wireless networks mostly because they fail to distinguish wireless random losses from congestion losses. Different loss discrimination algorithms have been proposed to tackle this issue but they are not efficient for 4G networks since they do not consider the impact of various link layer mechanisms such as adaptive modulation and coding and retransmission techniques on congestion in LTE Radio Access Networks (RANs). We propose MELD (MEC-based Edge Loss Discrimination), a novel server-side loss discrimination mechanism that leverages recent advancements in Multi-access Edge Computing (MEC) services to discriminate packet losses based on real-time RAN statistics. Our approach collects the relevant radio information via MEC's Radio Network Information Service and uses it to correctly distinguish random losses from congestion losses. Our experimental study made with the QUIC transport protocol shows over 80% higher goodput when MELD is used with New Reno and 8% higher goodput when used with Cubic.

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2020

Network Traffic Engineering

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Performance evaluation of CQIC and TCP BBR in mobile network

Cited by 12 publications

References 11 publications

BBR-S: A Low-Latency BBR Modification for Fast-Varying Connections

BBR-S: A Low-Latency BBR Modification for Fast-Varying Connections

Cross-layer Loss Discrimination Algorithms for MEC in 4G networks

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