Scalable Traffic Engineering for Higher Throughput in Heavily-loaded Software Defined Networks

09/20/2019
by   Che Zhang, et al.
0

Existing traffic engineering (TE) solutions performs well for software defined network (SDN) in average cases. However, during peak hours, bursty traffic spikes are challenging to handle, because it is difficult to react in time and guarantee high performance even after failures with limited flow entries. Instead of leaving some capacity empty to guarantee no congestion happens due to traffic rerouting after failures or path updating after demand or topology changes, we decide to make full use of the network capacity to satisfy the demands for heavily-loaded peak hours. The TE system also needs to react to failures quickly and utilize the priority queue to guarantee the transmission of loss and delay sensitive traffic. We propose TED, a scalable TE system that can guarantee high throughput in peak hours. TED can quickly compute a group of maximum number of edge-disjoint paths for each ingress-egress switch pair. We design two methods to select paths under the flow entry limit. We then input the selected paths to our TE to minimize the maximum link utilization. In case of large traffic matrix making the maximum link utilization larger than 1, we input the utilization and the traffic matrix to the optimization of maximizing overall throughput under a new constrain. Thus we obtain a realistic traffic matrix, which has the maximum overall throughput and guarantees no traffic starvation for each switch pair. Experiments show that TED has much better performance for heavily-loaded SDN and has 10 all (> 99.99 than Smore under the same flow entry limit.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/17/2020

Reliability Aware Multiple Path Installation in Software Defined Networking

Being a state-of-the-art network, Software Defined Networking (SDN) deco...
research
04/24/2020

CFR-RL: Traffic Engineering with Reinforcement Learning in SDN

Traditional Traffic Engineering (TE) solutions can achieve the optimal o...
research
09/07/2019

Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks

In this paper, we propose a destination-aware adaptive traffic flow rule...
research
12/21/2022

Robust Path Selection in Software-defined WANs using Deep Reinforcement Learning

In the context of an efficient network traffic engineering process where...
research
01/15/2021

Boosting performance for software defined networks from traffic engineering perspective

Paths selection algorithms and rate adaptation objective functions are u...
research
05/27/2021

On the Complexity of Weight-Dynamic Network Algorithms

While operating communication networks adaptively may improve utilizatio...
research
05/23/2023

Mitigating the Performance Impact of Network Failures in Public Clouds

Some faults in data center networks require hours to days to repair beca...

Please sign up or login with your details

Forgot password? Click here to reset