Applying LSTM to Enable Cache Prefetching to Optimize Flow Table Update Efficiency in SDN Switches

Li, Liangding; Chi, Jiapeng; Wang, Jim

doi:10.1145/3377170.3377218

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2024

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Cited by 2 publications

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A Hardware based RISC-V Extension Instruction Implementation Mechanism and Implementation Example

Jia,

Huang,

Yang

et al. 2024

2024 5th International Conference on Computer Engineering and Application (ICCEA)

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A Hardware based RISC-V Extension Instruction Implementation Mechanism and Implementation Example

Jia,

Huang,

Yang

et al. 2024

2024 5th International Conference on Computer Engineering and Application (ICCEA)

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ALBLP: Adaptive Load-Balancing Architecture Based on Link-State Prediction in Software-Defined Networking

Chen

Wang

Xuefeng

et al. 2022

Wireless Communications and Mobile Computing

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Load-balancing optimization in software-defined networking (SDN) has been researched for a long time. Researchers have proposed many solutions to the load-balancing problem but have rarely considered the impact of transmission delay between controllers and switches under high-load network conditions. In this paper, we propose an adaptive load-balancing architecture based on link-state prediction (ALBLP) in SDN that can solve the influence of transmission delay between controllers and switches on network load balancing. ALBLP constructs the prediction model of the network link status, adopts the long-term and short-term memory neural network (LSTM) algorithm to predict the network link-state value, and then uses the predicted value as the Dijkstra weight to calculate the optimal path between network hosts. The proposed architecture can adaptively optimize network load balancing and avoid the empty window period, in which the switch flow table does not exist by actively issuing the flow table. Under the network architecture, we collect the data set of the network link-state by simulating the GÉANT network, and we verify the effectiveness of the proposed algorithm. The experiment results show that the ALBLP proposed in this paper can optimize load balancing in SDN and solve the problem of transmission delay between controllers and switches. It has a maximum load-balancing improvement of 23.7% and 11.7% in comparison with the traditional Open Shortest Path First (OSPF) algorithm and the reinforcement learning method based on Q-Learning.

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Applying LSTM to Enable Cache Prefetching to Optimize Flow Table Update Efficiency in SDN Switches

Cited by 2 publications

References 6 publications

A Hardware based RISC-V Extension Instruction Implementation Mechanism and Implementation Example

A Hardware based RISC-V Extension Instruction Implementation Mechanism and Implementation Example

ALBLP: Adaptive Load-Balancing Architecture Based on Link-State Prediction in Software-Defined Networking

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