| Title |
Short time prediction of cloud server round-trip time using a hybrid neuro-fuzzy network / |
| Authors |
Damaševičius, Robertas ; Sidekerskienė, Tatjana |
| DOI |
10.33969/AIS.2020.21009 |
| Full Text |
|
| Is Part of |
Journal of artificial intelligence and systems.. Brighton, CO : Institute of electronics and computer. 2020, vol. 2, iss. 1, p. 133-148.. ISSN 2642-2859 |
| Keywords [eng] |
neuro-fuzzy network ; probability distributions ; round trip time ; Quality of Service (QoS) ; smart cloud computing |
| Abstract [eng] |
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. |
| Published |
Brighton, CO : Institute of electronics and computer |
| Type |
Journal article |
| Language |
English |
| Publication date |
2020 |
| CC license |
|
