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Short time prediction of cloud server round-trip time using a hybrid neuro-fuzzy network

Robertas Damaševičius1, *, Tatjana Sidekerskienė2

Corresponding Author:

Robertas Damaševičius

Affiliation(s):

1 Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania. Email: [email protected]u.lt
2 Department of Applied Mathematics, Kaunas University of Technology, Kaunas, Lithuania. Email: [email protected]
*Corresponding Author: Robertas Damaševičius, Email: [email protected]

Abstract:

The paper presents a cloud server roundtrip time prediction approach for cloud datacenters using neuro-fuzzy network with eight probability distribution functions (Normal, Rayleigh, Weibull, Gamma, Birnbaum-Saunders, Extreme Value, and Generalized Pareto) used for fuzzification and defuzzification. We predict the Round-Trip Time (RTT), i.e., the time for a network packet to travel from a client to a server and back. The proposed approach can achieve significant reduction in the short-time RTT prediction error, achieving an accuracy of 79.36%. The approach could be useful for increasing the efficiency of client-cloud systems, for example, when taking effective decisions for computational offloading, and contribute to the development of smart cloud computing.

Keywords:

Neuro-fuzzy network, probability distributions; round trip time; Quality of Service (QoS); smart cloud computing.

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Cite This Paper:

Robertas Dama?evi?ius, Tatjana Sidekerskien? (2020). Short time prediction of cloud server round-trip time using a hybrid neuro-fuzzy network. Journal of Artificial Intelligence and Systems, 2, 133–148. https://doi.org/10.33969/AIS.2020.21009.

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