Increasing Revenue by Applying Machine Learning to Congestion Management in SDN

With the advent of 5G, IoT and 4k videos, online gaming, movie streaming and other data intensive applications, the demand for data is sky rocketing. Due to this surge in data, the load on the network increases. This heightened network load causes degradation in network performance. Which can lead to the customer Service Provider (CSP)s loosing revenue if the Service Level Agreement (SLA) are not met. This report describes how machine learning techniques such as tit for tat can be applied to telecom networks. Machine learning applied to telecom networks help detect congestion and maintain SLAs while increasing yield (revenue). Several experiments are run with varying conditions on the network, such as low, medium and high loads; different levels of SLA for bandwidth and delay. Once the original conditions are tested without applying any smart blocking techniques, machine learning is applied to detect congestion in the network and block flows to maintain SLA and increase the number of flows that generate revenue

QoS-based Active Dropping Mechanism for NGN Video Streaming Optimization

[[abstract]]Video streaming over mobile wireless networks is getting popular in recent years. High video quality relies on large bandwidth provisioning, however, it decreases the number of supported users in wireless networks. Thus, effective bandwidth utilization becomes a crucial issue in wireless network as the bandwidth resource in wireless environment is precious and limited. The NGN quality of service mechanisms should be designed to reduce the impact of traffic burstiness on buffer management. For this reason, we propose an active dropping mechanism to deal with the effective bandwidth utilization in this paper. We use scalable video coding extension of H.264/AVC standard to provide different video quality for users of different levels. In the proposed dropping mechanism, when the network loading exceeds the threshold, the dropping mechanism starts to drop data of the enhancement layers for users of low service level. The dropping probability alters according to the change in network loading. With the dropping mechanism, the base station increases the system capability and users are able to obtain better service quality when the system is under heavy loading. We also design several methods to adjust the threshold value dynamically. By using the proposed mechanism, better quality can be provided when the network is in congestion.[[notice]]補正完畢[[incitationindex]]SC