An adaptive power management scheme for Ethernet Passive Optical Networks

Abstract

Undoubtedly, energy consumption in communication networks poses a significant threat to the environmental stability. Access networks contribute to this consumption by being composed of numerous energy inefficient devices and network equipment. Passive Optical Networks (PONs), one of the most promising candidates in the field of access networking, should avoid this bottleneck in the backhaul power consumption by lowering the energy use of the optical devices. In this paper, we move towards that direction by introducing an energy efficient power management scheme that encompasses two major goals: a) to reduce the energy consumption by allowing the optical devices to enter the sleep mode longer, and b) to concurrently maintain the network performance. To this end, we focus on the energy consumed by the optical network units (ONUs). The intelligence of the ONUs is stimulated by enhancing the decision making in determining the duration of the sleep period with learning from experience mechanism. Learning automata (LAs) are charged to address this challenge. The evaluation of the proposed enhanced power management scheme reveals considerable improvements in terms of energy savings, while at the same time the network performance remains in high levels.

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

“…This limits the energy savings or lessens the network efficiency [13]. Thus, an adaptive or dynamic TAS is more feasible to support unpredictable network changes [14]. The computation of the adaptive TAS can be done by monitoring both OLT and ONU buffer status, as reported in [11], [13], [15]- [17].…”

“…In fact, the ML techniques are now adopted in optical networks, as extensively reviewed in [27] and [28]. Sarigiannidis et al [14] determine the TAS using the learning automata (LA) scheme by increasing the probability of the learning feedback. The LA produces a random variable based on the probability vector, which corresponds to specific sleep duration.…”

“…To maximize energy savings, maximum delay is considered but with lower QoS experienced by users. Intelligencebased [14], and [29]- [31] Newest technology adopted in network communications. Using a learning from experiences approach to support ONU to take the best possible TAS based on current network status.…”