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Traffic classification for efficient load balancing in server cluster using deep learning technique

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Abstract

Extensive use of multimedia services and Internet Data Center applications demand distributed deployment of these applications. It is implemented using edge computing with server clusters. To increase the availability of the services, applications are deployed redundantly in server clusters. In this situation, an efficient server allocation strategy is essential to improve execution fairness in server cluster. Categorizing the incoming traffic at server cluster is desired for the improvement of QoS. The traditional traffic classification models categorize the incoming traffic according to their applications’ type. They are ineffective in selection of suitable server, as they do not consider the characteristics of the server. Hence this paper proposes a classifier to assist the dispatcher to distribute the requests to appropriate server in server cluster. The proposed deep learning classification model based on incoming traffic characteristics and server status is reinforced with extended labelling using correlation based approach. The experimental results of the proposed classifier have shown considerable performance enhancement in terms of classification measures and waiting time of the requests compared to existing machine learning models.

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Acknowledgements

The authors acknowledge the valuable discussions and suggestions given by Dr. N. P. Gopalan, Professor, National Institute of Technology, Tiruchirappalli for this paper.

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  1. Department of Computer Science and Engineering, Saranathan College of Engineering, Tiruchirappalli, India

    V. Punitha

  2. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India

    C. Mala

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  1. V. Punitha
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Punitha, V., Mala, C. Traffic classification for efficient load balancing in server cluster using deep learning technique. J Supercomput 77, 8038–8062 (2021). https://doi.org/10.1007/s11227-020-03613-3

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  • DOI: https://doi.org/10.1007/s11227-020-03613-3

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