1. Scalability of Software Defined Network
Presented by
Lin Zhou&Lei Zhang

2. Agenda
Background of our project
The Design of our project

3. Brief Introduction to SDN
Distributed Data plane and Control plane
Data plane:enquiring,forwarding
Control plane:management,route
Networklization
Intellectualization
Virtualization
Computationlization
Networklization
Intellectualization
Virtualization
Computationlization

4. The Origin of the Scalability Problems in SDN
Early benchmarks on NOX, which showed it could only handle 30,000 flow initiations per second while maintaining a sub-10 ms flow install time.

5. Current Solutions to the Probelm
DIFANE
DevoFlow
HyperFlow
Kandoo

6. DIFANE Model

7. DIFANE
Advantages:
(i) DIFANE achieves small delay for the first packet of a flow by always keeping packets in the fast path.
(ii)DIFANE achieves significantly higher throughput than NOX.
Disadvantages:
(i)A number of authority switches are needed for the large networks we evaluated.
(ii)DIFANE does not address the issue of global visibility of flow states and statistics.

8. DevoFlow
Reducing the number of flows that interact with the control-plane.
By pushing responsibility over most flows to switches and adding efficient statistics collection mechanisms to identify significant flows, which are the only flows managed by the central controller

9. DevoFlow
Advantage:
It can reduce the load of the controller so that it
will enlarge the scalability of the controller.
Disadvantages:
(i)How many flows would be sufficient to achieve the desired results in different environments still is a question.
(ii)It is hard to build a efficient statistics collection mechanisms.

10. HyperFlow Logically centralized Physically distributed
Does not require any changes to the OpenFlow standard

11. HyperFlow Model

12. HyperFlow Advantages:
(i)Enables network operators deploy any number of controllers to tune the performance of the control plane based on their needs.
(ii)Keeps the network control logic centralized and localizes all decisions to each controller to minimize control plane response time.
Disadvantage:
HyperFlow doesn't influence the number of the
switches of one controller.

13. Kandoo Kandoo creates a two-level structure for controllers:
(i) Local controllers execute local applications as close as possible to switches
(ii) A logically centralized root controller runs non-local control applications.

14. Kandoo Model

15. Kandoo Advantages: Preserving scalability without changing switches.
Good at dealing with local flows.
Disadvantages:
Can not help any control applications that require networkwide state

16. The Design of our project
Goals
a controller system can serve as many switches as possible like Hyperflow
a root controller with network-view state can serve as many switches as possible like Kandoo

17. The Design of our project
Model Design--HMKH

18. Details of the HMKH Assumptions
1. The communications are all wireless and wire-
less communication detail is not the coverage
of this report.
2. Any switches controlled by a root controller in
a site is in the control range of that root con-
troller.
3. The direct neighbours of any root controller
are within the communication range of the root
controller.

19. The Design of our project
Implementations of HMKH
Initiation
Periodic Communications and Failure-free Mechanism
Network Change Information

20. Implementations of HMKH
Initiation
root controller broadcast to get its local controllers
local controllers broadcast to get its switches

21. Implementations of HMKH
Periodic Communications and Failure-free Mechanism
root controllers periodically broadcast
local controller once getting this will respond
local controller failure
root controller failure

22. Implementations of HMKH
Network-view State Synchronization
-switches failure
-split a network
-interconnect networks

23. The Design of our project
traditional
SDN
with one controller
incapable if n*m exceeds k
Kandoo
with one root controller
incapable if n*m*p exceeds k
Hyperflow
with n*m/k root controllers and each serve k/m switches
capable
Our Model
with n*m*p/k root controllers and each serve k/(m*p) switches
Evaluations of HMKH
the process ability for controller is k msgs/sec
n switches,each sends m msgs/sec and p percent of them need network-view state

24. Conclusions
The scalability lies not only in how many switches a controller system can serve,but also how many switches a controller with network-view state can serve(overhead)
The number of root controllers and local controllers should be estimated or calculated given the requests from switches to minimize the overhead while satisfy the requirements

25. Thanks and QA!