OpenFlow-Based Server Load Balancing
GoneWild
Author : Richard Wang, Dana Butnariu, Jennifer Rexford
Publisher : Hot-ICE'11 Proceedings of the 11th USENIX
conference
Presenter: Tung-yin Chi
Date: 2015/4/22
Department of Computer Science and Information Engineering
National Cheng Kung University, Taiwan R.O.C.
Introduction (1/3)

The OpenFlow standard enables an alternative approach
where the commodity network switches divide traffic over
the server replicas, based on packet-handling rules
installed by a separate controller

However, the simple approach of installing a separate rule
for each client connection (or “microflow”) leads to a
huge number of rules in the switches and a heavy load on
the controller
National Cheng Kung University CSIE
Computer & Internet Architecture Lab
2
Introduction (2/3)

We argue that the controller should exploit switch support
for wildcard rules for a more scalable solution that directs
large aggregates of client traffic to server replicas

We present algorithms that compute concise wildcard
rules that achieve a target distribution of the traffic, and
automatically adjust to changes in load-balancing policies
without disrupting existing connections
National Cheng Kung University CSIE
Computer & Internet Architecture Lab
3
Introduction (3/3)

We implement these algorithms on top of the NOX
OpenFlow controller, evaluate their effectiveness, and
propose several avenues for further research
National Cheng Kung University CSIE
Computer & Internet Architecture Lab
4
Core Ideas

Figure 1: Basic model from load balancer switch’s view
National Cheng Kung University CSIE
Computer & Internet Architecture Lab
5
Relevant OpenFlow Features

The controller can install rules that match on
certain packet-header fields and perform
actions on the matching packets

A microflow rule matches on all fields,
where a wildcard rule can have “don’t care”
bits in some fields
National Cheng Kung University CSIE
Computer & Internet Architecture Lab
6
Relevant OpenFlow Features

the switch performs an action of
• rewriting the server IP address
• forwarding the packet to the output port associated
with the chosen replica

Relying on wildcard rules that match on the
client IP addresses
National Cheng Kung University CSIE
Computer & Internet Architecture Lab
7
Partitioning the Client Traffic



The partitioning algorithm must divide client
traffic in proportion to the load-balancing
weights, while relying only on features
available in the OpenFlow switches
Initially assume that traffic volume is
uniform across client IP addresses
The goal is to generate a small set of
wildcard rules that divide the entire client IP
address space
National Cheng Kung University CSIE
Computer & Internet Architecture Lab
8
Minimizing the Number of
Wildcard Rules
Minimizing the Number of
Wildcard Rules
Transitioning Quickly With
Microflow Rules
Implementation and Evaluation



Built a prototype using OpenVswitch (a
software OpenFlow switch) and NOX (an
OpenFlow controller platform), running
in Mininet.
The prototype runs the partitioning
algorithm and our transitioning
algorithm.
We use Mininet to build the topology in
Figure 1 with a set of 3 replica servers, 2
switches, and a number of clients.
Implementation and Evaluation




3 replica servers host the same 16MB file
For this experiment, use 36 clients with
randomly-chosen IP addresses.
Each client issues wget requests for the
file
We assign α1 =3, α2 = 4, and α3 = 1
Adapting to new load-balancing
weights
Overhead of transitions



To evaluate the overhead and delay on
the controller during transitions, we have
ten clients simultaneously download a
512MB file from two server replicas.
We start with all traffic directed to R1,
and then (in the middle of the ten
downloads) start a transition to replica
R2.
In the experiments, we didn’t see any
noticeable degradation in throughput
during the transition period
Non-Uniform Client Traffic
Network of Multiple Switches
Conclusion


Our “partitioning” algorithm determines
a minimal set of wildcard rules to install,
while our “transitioning” algorithm
changes these rules to adapt the new
load balancing weights.
Our evaluation shows that our system
can indeed adapt to changes in target
traffic distribution and that the few
packets directed to the controller have
minimal impact on throughput.