Paper Info
Title
A model predictive control approach to flow pacing for TCP.
Abstract
A key challenge in the management of Internet traffic is the design of algorithms that complement well-established protocols, such as the Transmission Control Protocol (TCP), and simultaneously address their limitations. The challenge becomes greater in the context of large so-called "elephant" flows over long paths that often transition from higher to lower bandwidth connections. At these transition points either persistent queues are formed when buffers are over-provisioned or packet loss occurs when buffers are under-provisioned; both cases lead to degraded and/or highly variable end-to-end performance. Ideally, for such scenarios, the source should "learn" and set a pacing rate that matches the lower bandwidth connection. In this paper, we adopt a model-based receding horizon control strategy to design a pacing control method. Each new round-trip time (RTT) measurement is first incorporated into a linear time-varying (LTV) predictive model. Subsequently, we solve a one-step look-ahead optimization problem which finds the pacing rate which optimally trades off RTT, variance in RTT, and throughput according to the most up-to-date model. We implemented our proof-of-concept control strategy on the Linux operating system alongside the existing CoDel queuing discipline (qdisc) and HTCP congestion-control algorithm. Our preliminary results indicate significant reduction in the variances of the RTT and the throughput, resulting in more predictable performance overall.
Year
Venue
Field
2017
2017 55TH ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON)
Computer science,Model predictive control,Packet loss,Computer network,Transmission Control Protocol,Queueing theory,Bandwidth (signal processing),CoDel,Throughput,Internet traffic,Distributed computing
DocType
ISSN
Citations 
Conference
2474-0195
1
PageRank 
References 
Authors
0.36
9
6
Name
Order
Citations
PageRank
David Fridovich-Keil1247.39
Nathan Hanford2112.33
Chapman, Margaret P.333.15
Claire J. Tomlin41491158.05
Matthew Farrens551569.21
Dipak Ghosal62848163.40