Automised flow rule formation by using machine learning in software defined networks based edge computing

Iqbal, Saleem; Maryam, Hira; Qureshi, Kashif Naseer; Javed, Ibrahim Tariq; Crespi, Noël

doi:10.1016/j.eij.2021.10.001

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…In future work, we plan to propose an advanced network monitoring algorithm for edge computing architecture, which is more efficient in the resource-limited environment and considers network congestion and QoS. Apart from the network congestion issue, we have observed in the [32] that machine learning were used to reduce network latency in SDN. This is worth considering and referencing for our algorithm.…”

Section: Future Workmentioning

confidence: 99%

A Low-overhead Network Monitoring for SDN-Based Edge Computing

Tao,

Huang,

Shen

2023

2023 IEEE Symposium on Computers and Communications (ISCC)

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Using Software-Defined Networking (SDN) in edge computing environments allows for more flexible flow monitoring than traditional networking methods. In SDN, the controller collects statistics from all switches and can communicate with switches to dynamically manage the entire network. However, monitoring per-flow or per-switch mechanisms to obtain the flow statistics from all of the switches may significantly increase bandwidth costs between switches and the control plane. In this paper, we propose a Bandwidth Cost First (BCF) algorithm to reduce the number of monitored switches and therefore lower the monitoring cost. The experiment results show that our algorithm outperforms the existing technique by reducing the number of monitored switches by 56%, leading to a reduction in bandwidth overhead of 41% and switch processing delay by 25%.

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Section: Future Workmentioning

confidence: 99%

A Low-overhead Network Monitoring for SDN-Based Edge Computing

Tao,

Huang,

Shen

2023

2023 IEEE Symposium on Computers and Communications (ISCC)

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“…However, routing management in such networks addresses various problems, tasks, and parameters. Iqbal et al (2022) [17] considered forming flow rules using machine learning in SDN-based edge computing. Simultaneous processing of multiple streams by substitution rules is problematic, which dramatically reduces the network performance.…”

Section: -1-literature Review and Analysismentioning

confidence: 99%

Development of Algorithm for Calculating Data Packet Transmission Delay in Software-Defined Networks

et al. 2022

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The relevance of this type of network is associated with the development and improvement of protocols, methods, and tools to verify routing policies and algorithmic models describing various aspects of SDN, which determined the purpose of this study. The main purpose of this work is to develop specialized methods to estimate the maximum end-to-end delay during packet transmission using SDN infrastructure. The methods of network calculus theory are used to build a model for estimating the maximum transmission delay of a data packet. The basis for this theory is obtaining deterministic evaluations by analyzing the best and worst-case scenarios for individual parts of the network and then optimally combining the best ones. It was found that the developed method of theoretical evaluation demonstrates high accuracy. Consequently, it is shown that the developed algorithm can estimate SND performance. It is possible to conclude the configuration optimality of elements in the network by comparing the different possible configurations. Furthermore, the proposed algorithm for calculating the upper estimate for packet transmission delay can reduce network maintenance costs by detecting inconsistencies between network equipment settings and requirements. The scientific novelty of these results is that it became possible to calculate the achievable upper data delay in polynomial time even in the case of arbitrary tree topologies, but not only when the network handlers are located in tandem. Doi: 10.28991/ESJ-2022-06-05-010 Full Text: PDF

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“…Reinforcement learning may be used to generate flow rules automatically. Automated rule creation may eventually be accomplished via different types of supervised learning approaches [26].…”

Section: Previous Workmentioning

confidence: 99%

Effective Flow Table Space Management Using Policy-Based Routing Approach in Hybrid SDN Network

Paliwal

Nagwanshi

2022

IEEE Access

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Software-defined networking makes forwarding devices easier to manage and provides centralized control. Because of the centralization, a network administrator can programme the devices cheaply. Network administrators make attempts to convert their entire network into SDN-compatible switches. A good balance of SDN and legacy switching functions can lead to a successful network scenario in network architecture. In this study, a hybrid network scenario is provided in which the external boundary forwarding devices of the service provider network are replaced with SDN devices. Still, the other internal forwarding devices continue to operate traditionally. The benefits of both SDN and legacy network design are combined, allowing the network administrator to reap the benefits of both. The network architecture employs a policy-based routing algorithm that takes advantage of free IP addresses from the free IP pool. The technique enables efficient use of available flow table space, which is critical in SDN architecture due to the small flow table size. The algorithm's efficiency is assessed using performance metrics such as network path stretch, throughput, latency delay, and so on, compared to traditional SDN controllers such as OpenDayLight, NOX, and POX. According to the experimental results, the suggested approach outperforms specific similar state-of-the-art techniques in the hybrid SDN domain.

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Automised flow rule formation by using machine learning in software defined networks based edge computing

Cited by 11 publications

References 14 publications

A Low-overhead Network Monitoring for SDN-Based Edge Computing

A Low-overhead Network Monitoring for SDN-Based Edge Computing

Development of Algorithm for Calculating Data Packet Transmission Delay in Software-Defined Networks

Effective Flow Table Space Management Using Policy-Based Routing Approach in Hybrid SDN Network

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