Online-Based Learning for Predictive Network Latency in Software-Defined Networks | IEEE Conference Publication | IEEE Xplore

Online-Based Learning for Predictive Network Latency in Software-Defined Networks


Abstract:

In Software Defined Networking, due to the significant bandwidth and latency requirements, predicting available resources (i.e., latency, bandwidth) is crucial for enhanc...Show More

Abstract:

In Software Defined Networking, due to the significant bandwidth and latency requirements, predicting available resources (i.e., latency, bandwidth) is crucial for enhancing performances, resource utilization and power consumption of the data plane. In this paper, we propose an efficient rules placement algorithm based on predictive network latency using online learning. Our proposal aims to dynamically predict the latency for updating the flow rules in network devices. To do so, we first formulate the flow rules placement as an Integer Linear Program (ILP) that aims to minimize the total network delay. Then, we propose a simple yet efficient heuristic algorithm to solve the formulated ILP problem with low time complexity. Experimental results using ONOS controller and Mininet show the efficiency of our proposal in decreasing network latency, packet loss and rising the network throughput.
Date of Conference: 09-13 December 2018
Date Added to IEEE Xplore: 21 February 2019
ISBN Information:

ISSN Information:

Conference Location: Abu Dhabi, United Arab Emirates

References

References is not available for this document.