Hybrid IP/SDN Routing for Inter-Data Center Communications

Pereira, Vítor; Rocha, Miguel; Sousa, Pedro

doi:10.1109/cloudnet47604.2019.9064118

Cited by 1 publication

(2 citation statements)

References 8 publications

(6 reference statements)

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“…Current studies on flow table optimization also jointed with the optimization of network delay and resource usage. As references [30] and [31] reduced the number of flow entries and the load imbalance on link resource utilization; reference [32] optimized the value of idle timeouts of flow entries to jointly minimize the flow table resource usage and the controller computing resource cost; reference [33] formulated an optimization problem that found the best active links to minimize the flow table size and power consumption; and reference [34] tried to find the Pareto frontier of routing routes that optimize both congestion metric and link utilization.…”

Section: Multi-objective Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Flow Entry Timeouts Optimization over Software Defined Networks Supporting Elephant Flow Classification

Zhao¹,

Zhao²,

Liao³

et al. 2022

Journal of Internet Technology

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<p>Elephant flow (elephant) classification is significant for network performance management and resource optimization. Classifying elephants over Software Defined Networks (SDNs) often relies on statistics counted by switches and transferred to controllers, leading to a huge control channel bandwidth occupation that degrades network latency and user experience. Classifying elephants using flow packets forwarded to controllers completely avoids moving statistics from switches to controllers, but couples flow entry timeouts to elephant classification accuracy, time efficiency, flow table size, control channel bandwidth usage, and network latency. This paper aims to find the best flow entry timeouts to optimize the objectives by modeling the objectives and formulating a multi-objective optimization problem. The number of objectives is further reduced to 3 and the problem is solved by Machine Learning (ML) approaches. Extensive evaluations are made over real packet traces. To the best of our knowledge, this work is the first effort that explores all the objectives related to flow entry timeouts when using flow packets forwarded to controllers to classify elephants over SDNs.</p> <p> </p>

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Section: Multi-objective Problemsmentioning

confidence: 99%

“…GAs and NSGA-IIs can be used to solve any optimization problems in general. In SDNs, GAs have been applied to select features of flows to model flows for accurate flow classification [38], and NSGA-IIs have been involved to optimize various metrics [33][34].…”

Section: Ml-based Searchmentioning

confidence: 99%