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

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.

Predicting High Delays in Mobile Broadband Networks

“…The proposed RNNs achieved a root mean square error (RMSE) of 1.543. In addition, [34] presented a hybrid neuro-fuzzy approach for client-cloud server communication round-trip time (RTT) prediction, achieving an accuracy of 79.36%. [35] presented a Markov model with two states to predict the probability density function of RTT instead of the actual value in LTE and WiFi networks.…”