Efficiency Optimization for Software Defined Networks

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Efficiency Optimization for Software Defined Networks"

By

Mr. Zhiyang SU


Abstract

The rapid growth of cloud computing, network virtualization and big data brings 
new challenges for computer networks. By decoupling the control plane and the 
data plane, Software Defined Networking (SDN) becomes an emerging paradigm to 
enable network innovation with unprecedented programmability. The major 
concerns are performance issue for large networks and how to facilitate network 
management by SDN visibility.

Initially, we explore the extra flow setup latency by the controller and switch 
communication. To eliminate the overhead, we propose a system which predicts 
frequent communication pair and proactively installs forwarding wildcard rules.

Then, we concentrate on software defined measurement and propose three novel 
schemes to optimize network monitoring efficiency in a top-down approach. 
First, we present a cross-layer optimization for sketch-based measurement. We 
observe the diminishing marginal utility property of sketch-based measurement. 
By trading a little accuracy, we dramatically shrink the measurement resource 
usage, and develop a two-stage heuristic to efficiently assign concurrent 
measurement tasks to underlying switches.

Second, we propose schemes to optimize flow statistics collection. We point out 
flow statistics polling is a fundamental primitive for software defined 
measurement. Based on this observation, we propose a generic optimization which 
is compatible with all existing measurement frameworks. Two monitoring schemes 
are presented to achieve different levels of measurement granularity.

Finally, we propose a measurement-aware controller placement which reduces the 
overhead in the physical layer. Our proposal is cost-effective and 
application-agnostic. The placement model takes both the synchronization and 
flow statistics polling cost into account. Two heuristics are presented to 
efficiently generate near-optimal placements for large-sized networks.

We demonstrate the effectiveness of our proposals by conducting experiments on 
various network topologies with real-world traffic traces.


Date:			Saturday, 2 April 2016

Time:			10:00am - 12:00noon

Venue:			Room 5510
  			Lifts 25/26

Chairman:		Prof. Guohua Chen (CBME)

Committee Members:	Prof. Mounir Hamdi (Supervisor)
  			Prof. Kai Chen
  			Prof. Jogesh Muppala
  			Prof. Chi-Ying Tsui (ECE)
  			Prof. Hussein Mouftah (U of Ottawa)


**** ALL are Welcome ****