Determination of optimized sleep interval for 10 gigabit-passive optical network using learning intelligence

Abstract

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.