Enhanced Congestion Management for Minimizing Network Performance Degradation in OBS Networks

Abstract

Current global data traffic is increasingly dominated by delay and loss intolerant IP traffic which generally displays a structural self-similarity. This has necessitated the introduction of optical burst switching (OBS) as a supporting optical backbone network switching technology. Due to the buffer-less nature of optical burst switched (OBS) networks, contention/congestion in the core network can quickly lead to degradation in overall network performance at moderate to high traffic levels due to heavy burst loses. Several approaches have been explored to address this problem, notably measures that would minimize burstification delays, congestion, blocking at the same time enhancing end-to-end throughput as well as rational and fair utilization of the links. The aim is to achieve a consistent quality of service (QoS). Noting that congestion minimization is key to a consistent QoS provisioning, in this paper we propose a congestion management approach called enhanced congestion management (ECM) that seeks to guarantee a consistent QoS as well as rational and fair use of available links. It is primarily a service differentiation based scheme that aims at congestion, blocking and latency minimization, by way of combining time averaged delay segmented burstification as well as random shortest path selection based deflection routing and wavelength assignment. Simulation results show that ECM can effectively minimise congestion and at the same time improve both throughput and effective utilization, under moderate to high network traffic conditions. Overall, we show that the approach guarantees a consistent QoS.

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

“…In this way, the network states might end up fluctuating with high variability since this kind of traffic is usually far from a renewal process due to the positive dependence between successive arrival times. The escalation of such a situation, which is as a result of moderate-to-high traffic levels, can quickly lead to the degradation of the overall network performance [23]. As a consequence of this, a catastrophe of heavy burst packet losses may result.…”

QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

“…Nowadays, global mobile data traffic is increasingly dominated by delay-and loss-intolerant traffic streams, which means that traffic congestion in the core network is an inevitable occurrence. This can quickly lead to overall network performance degradation resulting from the moderate-to-high traffic levels [11]. The consequence of this is the catastrophe of heavy burst losses, as the whole network might degenerate into chaos.…”