Optimal resource pooling over legacy equal-split load balancing schemes

Németh, Krisztián; Kő̈rösi, Attila; Rétvári, Gábor

doi:10.1016/j.comnet.2017.08.017

Cited by 1 publication

(2 citation statements)

References 30 publications

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“…[28][29][30] Apart from these, the other issues include switch deployment for efficient energy consumption, 31 controller placement problem, and load balancing flow requests among multiple controllers. 32,33 There is a possibility to deploy a multipath concept in a virtual environment. They have developed an open platform for the practice testbed.…”

Section: Literature Survey and Related Workmentioning

confidence: 99%

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Intelligence‐enabled approach for load balancing in software‐defined data center networks

Fancy

Pushpalatha

2021

Int J Communication

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Summary Software‐defined network provides eminent solutions for many complex network management functionalities in a data center network (DCN). One of the major tasks in any network is the load balancing in the available links. Due to dynamic data traffic nature in network, it is necessary to perform deep learning approaches for the long‐term and the short‐term data. This paper proposes a splitting policy‐based RL network (SPRLN) approach, a reinforcement learning‐based proactive load balancing algorithm that avoids the poll to the controller after the switch encounters an abnormality. The proposed method has been tested with simulations and found successful in improving the overall network performance by taking appropriate action for reward maximization. The testbed environment is treated as a Q‐learning algorithm; here, the optimality is defined as the path having the least score so that the overloaded path in that particular time can be avoided. An artificial neural network is needed because the data are uncertain all the time. Thus, the proposed SPRLN method yields 30% increased throughput and with 80% reduced data loss, when compared to existing approaches.

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Section: Literature Survey and Related Workmentioning

confidence: 99%

“…This has been addressed by many of the researchers 28–30 . Apart from these, the other issues include switch deployment for efficient energy consumption, 31 controller placement problem, and load balancing flow requests among multiple controllers 32,33 …”

Section: Literature Survey and Related Workmentioning

confidence: 99%

Intelligence‐enabled approach for load balancing in software‐defined data center networks

Fancy

Pushpalatha

2021

Int J Communication

View full text Add to dashboard Cite

show abstract

Optimal resource pooling over legacy equal-split load balancing schemes

Cited by 1 publication

References 30 publications

Intelligence‐enabled approach for load balancing in software‐defined data center networks

Intelligence‐enabled approach for load balancing in software‐defined data center networks

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