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

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 (TAS) 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 TAS 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 TAS 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 TAS decisions. Results have shown that towards higher network load, a decreasing TAS trend was observed from both methods. A wider TAS 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 TAS values at current network conditions

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

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 (TAS) 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 TAS 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 TAS 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 TAS decisions. Results have shown that towards higher network load, a decreasing TAS trend was observed from both methods. A wider TAS 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 TAS values at current network conditions

Performance evaluation of XG-PON with DBA based-watchful sleep mode

The continuously increasing bandwidth demand of information and communication technology (ICT) is leading a rapid evolution towards next generation passive optical networks (NG-PON). However, the power consumed at the access network is experiencing a tremendous growth due to the employment of the Optical Network Units (ONU). For energy conservation in this PON, watchful sleep mode is the newest standardized mechanism that simplifies the features of both cyclic sleep and doze modes to provide higher energy savings. To exploits its functionality, this sleep mode with a dynamic bandwidth assignment (DBA) scheme is a promising solution. Therefore, this study presents the performance of watchful sleep mode in a 10 GB-capable PON (XG-PON) network employing the Immediate Allocation with Colorless Grant (IACG) DBA. This study firstly review the current progress of the power saving techniques in XG-PON. Our simulation results show a better performances compared to the standard in term of upstream (US) and downstream (DS) delays. We also achieve 41 % of energy savings at 0.47 network load