A practical algorithm for balancing the max-min fairness and throughput objectives in traffic engineering

Danna, Emilie; Mandal, Subhasree; Singh, Arjun

doi:10.1109/infcom.2012.6195833

Cited by 76 publications

(28 citation statements)

References 13 publications

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“…These approaches optimize for metrics such as congestion, throughput, latency, fault tolerance, fairness etc. Optimal techniques typically have high overheads (Danna et al 2012), but oblivious (Kodialam et al 2009;Applegate and Cohen 2003) and hybrid approaches with near-optimal performance (Hong et al 2013;Jain et al 2013) have recently been adopted.…”

Section: Related Workmentioning

confidence: 99%

Cantor meets Scott: semantic foundations for probabilistic networks

Smolka

Kumar

Foster

et al. 2017

Proceedings of the 44th ACM SIGPLAN Symposium on Principles of Programming Languages

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ProbNetKAT is a probabilistic extension of NetKAT with a denotational semantics based on Markov kernels. The language is expressive enough to generate continuous distributions, which raises the question of how to compute effectively in the language. This paper gives an new characterization of ProbNetKAT's semantics using domain theory, which provides the foundation needed to build a practical implementation. We show how to use the semantics to approximate the behavior of arbitrary ProbNetKAT programs using distributions with finite support. We develop a prototype implementation and show how to use it to solve a variety of problems including characterizing the expected congestion induced by different routing schemes and reasoning probabilistically about reachability in a network.

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Section: Related Workmentioning

confidence: 99%

Cantor meets Scott: semantic foundations for probabilistic networks

Smolka

Kumar

Foster

et al. 2017

Proceedings of the 44th ACM SIGPLAN Symposium on Principles of Programming Languages

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“…It is worth noting that the minimum rate is picked here because it assures a fair rate allocation between data flows, and such utility has been adopted by many recent works; e.g., [8,15]. Other objective functions can be used as well, for example the sum rate of all users, or the proportional fairness criteria.…”

Section: System Modelmentioning

confidence: 99%

Semi-asynchronous routing for large scale hierarchical networks

Liao

Mei

Farmanbar

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We consider the distributed network routing problem in a large-scale hierarchical network whereby the nodes are partitioned into subnet works. each managed by a network controller (NC). and there is a central NC to coordinate the operation of the distributed NCs. We propose a semi-asynchronous routing algorithm for such a network. whereby the computation is distributed across the NCs and is parallel within each Ne. A key feature of the algorithm is its ability to han dle a certain degree of asynchronism: the distributed NCs can per form their local computation asynchronously at different processing speed. The efficiency of the proposed algorithm is validated through numerical experiments.

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“…Specifically, Danna et al [8] have presented an efficient bandwidth allocation algorithm to achieve a flexible tradeoff between fairness and throughput for traffic engineering. Joe-Wong et al [18] have proposed a unifying framework with fairness and efficiency requirements specified by two parameters for a given multi-resource allocation problem.…”

Section: Related Workmentioning

confidence: 99%

On the Fairness-Efficiency Tradeoff for Packet Processing with Multiple Resources

Wang

Feng

et al. 2014

Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies

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Middleboxes are widely deployed in today's networks. They apply a variety of complex network functions to transform, filter, and optimize incoming traffic based on the payload of packets. These functions require the support of multiple types of resources, such as CPU and link bandwidth, for processing incoming packets. Hence, a multi-resource packet scheduling algorithm is needed to allow flows to share these resources fairly and efficiently. However, unlike traditional fair queueing where bandwidth is the only concern, we show in this paper that fairness and efficiency are conflicting objectives that cannot be achieved simultaneously in the presence of multiple resources. Ideally, a scheduling algorithm should allow network operators to flexibly specify their fairness and efficiency requirements, so as to meet the Quality of Service demands while keeping the system at a high utilization level. Yet, existing multi-resource scheduling algorithms focus on fairness only, and may lead to poor resource utilization. In this paper, we propose a new scheduling algorithm to achieve a flexible tradeoff between fairness and efficiency for packet processing, consuming both CPU and link bandwidth. Experimental results based on both real-world implementation and trace-driven simulation suggest that trading off a modest level of fairness can potentially improve the efficiency to the point where the system capacity is almost saturated.

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A practical algorithm for balancing the max-min fairness and throughput objectives in traffic engineering

Cited by 76 publications

References 13 publications

Cantor meets Scott: semantic foundations for probabilistic networks

Cantor meets Scott: semantic foundations for probabilistic networks

Semi-asynchronous routing for large scale hierarchical networks

On the Fairness-Efficiency Tradeoff for Packet Processing with Multiple Resources

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