Open AccessDetermination of optimized sleep interval for 10 gigabit-passive optical network using learning intelligence
Author(s) -
A. M. Zin,
Sevia Mahdaliza Idrus,
N. A. Ismail,
Arnidza Ramli,
F. M. Atan
Publication year - 2022
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v12i3.pp2663-2671
Subject(s) - gigabit , computer science , flexibility (engineering) , artificial neural network , interval (graph theory) , artificial intelligence , telecommunications , mathematics , statistics , combinatorics
The overall aim of this project is to investigate the application of a machine learning method in finding the optimized length of asleep time interval (T AS ) in a cyclic sleep mechanism (CSM). Since past decade, the implementations of CSM in the optical network unit (ONU) to reduce the energy consumption in 10 gigabit-passive optical network (XG-PON) were extensively researched. However, the newest era sees the emergence of various network traffic with stringent demands that require further improvements on the T AS selection. Since conventional methods utilize complex algorithm, this paper presents the employment of an artificial neural network (ANN) to facilitate ONU to determine the optimized T AS values using learning from past experiences. Prior to simulation, theoretical analysis was done using the M/G/1 queueing system. The ANN was than trained and tested for the XG-PON network for optimal T AS decisions. Results have shown that towards higher network load, a decreasing T AS trend was observed from both methods. A wider T AS range was recorded from the ANN network as compared to the theoretical values. Therefore, these findings will benefit the network operators to have a flexibility measure in determining the optimal T AS values at current network conditions.