Stability and Performance of Compound TCP With a Proportional Integral Queue Policy

Manjunath, Sreelakshmi; Raina, Gaurav

doi:10.1109/tcst.2018.2855141

Cited by 7 publications

(8 citation statements)

References 36 publications

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“…To this end, consider a convex optimization problem: (1). (19) Careful examination can tell that (11)- (17) are equivalent to the KKT conditions for the problem (19) [cf. (2- 7], with q acting as Lagrange multipliers for the constraints (C1) and (C2).…”

Section: B the Mappingmentioning

confidence: 99%

“…The BS implements the scheduling policy (10). Taking into account the feedback delay, we run TCP-Reno [11], TCP-Vegas [8], TCP-Cubic [20], TCP-Compound [19], FAST-TCP [4], and the proposed QUIC-TCP (ρ = 0.5). The window update step size for FAST-TCP and QUIC-TCP is set as κ = 1.5.…”

Section: B Ns-2 Simulationsmentioning

confidence: 99%

“…Following the similar lines proving [29,Claims 3], we take a sequence w (n) such that w (n) → w and let x (n) = F (w (n) ). By the compactness of the constraint set in (19), we can have a subsequence w (n k ) → w so that the corresponding x (n k ) converges to, say x.…”

Section: Appendix a Proof For Properties Of The Mappingmentioning

confidence: 99%

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End-to-End TCP Congestion Control for Mobile Applications

2020

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This paper addresses the re-engineering of congestion control for TCP applications over networks with coupled wireless links. Using queueing delay as a congestion measure, we show that optimal TCP congestion control can be achieved by developing window-control oriented implicit primal-dual solvers for intended network utility maximization problem. Capitalizing on such an idea, we prove the existence of scalable, easy-to-deploy, yet optimal end-to-end congestion control schemes for networks with wireless links, given that the wireless access point appropriately schedules packet transmissions. A class of so-called QUIC-TCP congestion control algorithms are developed. Relying on a Lyapunov method, we rigorously establish the global convergence/stability of the proposed QUIC-TCP to optimal equilibrium in the network fluid model. Numerical results corroborate the merits of the proposed schemes in IPv6-based Internet environments. INDEX TERMS Congestion control, wireless-link scheduling, convex optimization, network fluid model, Lyapunov method.

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Section: B the Mappingmentioning

confidence: 99%

Section: B Ns-2 Simulationsmentioning

confidence: 99%

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End-to-End TCP Congestion Control for Mobile Applications

2020

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“…, ω I } for maximizing the utility, the control entity constructs a coding cache queue. The challenge of the classic Proportional Integral (PI) model for active queue control [34] is to adaptively set two key parameters: a proportional coefficient k P and a integral coefficient k I . k P can change the system state quickly, and k I can reduce the system static errors.…”

Section: ) Coding Cache Queue Control With Qpsomentioning

confidence: 99%

“…E d (t) is the variable deviation. q D (t) is the packet dropping rate, and ∆q D (t) is the increment of the packet dropping rate; these variables are shown as [34]:…”

Section: ) Coding Cache Queue Control With Qpsomentioning

confidence: 99%

Dynamic Resource Scheduling Optimization With Network Coding for Multi-User Services in the Internet of Vehicles

et al. 2020

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For Internet of Vehicles (IoV) systems with multiple users, network coding can be introduced to provide efficient error control and throughput improvement services. However, if the heterogeneity characteristics and requirements of the end users (vehicles) are neglected, it will be difficult for an IoV system to provide each end user with fair system services, without which the advantages of network coding cannot be fully achieved and the performance of the multiuser diversity system will be degraded. In this paper, we propose a Dynamic Resource Scheduling Optimization (DRSO) algorithm, a dynamic fair scheduling algorithm combined with network coding for system resource allocation in a multiuser IoV system. We construct a general solution framework for service scheduling: first, we estimate the fairness index for each end user (vehicle) with the key information on Quality of Service (QoS). Second, we construct a service scheduling control model based on the service capability of control entities (multiaccess edge computing servers), and propose a new utility evaluation function. Third, based on the fairness index, we select end users into multiple network coding sets. Network coding sets are the basic units of service scheduling. The optimization objective of the scheduling service is to maximize the total utility of all the network coding sets (the utility of the control entity). Finally, we establish a coding cache queue in the control entity based on the scheduling decision. To obtain the global optimal solution for active queue control, we combine a Quantum Particle Swarm Optimization (QPSO) algorithm with a Proportional Integral (PI) model. Then, the optimal scheduling decision can be made. Extensive simulation results show that DRSO outperforms related scheduling algorithms in varying traffic loads, demonstrating that DRSO can effectively guide service resource allocation. INDEX TERMS multiuser , fairness control, network coding set, cache queue, Internet of Vehicles

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Robust control of congestion in computer networks: An adaptive fractional-order approach

Nasiri

Nikdel

2022

Expert Systems with Applications

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Stability and Performance of Compound TCP With a Proportional Integral Queue Policy

Cited by 7 publications

References 36 publications

End-to-End TCP Congestion Control for Mobile Applications

End-to-End TCP Congestion Control for Mobile Applications

Dynamic Resource Scheduling Optimization With Network Coding for Multi-User Services in the Internet of Vehicles

Robust control of congestion in computer networks: An adaptive fractional-order approach

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