Cloud network systems and applications are tested in simulation and emulation environments prior to physical deployments, at different stages of development. Software-Defined Networking (SDN) enables separating logic and execution from the data plane consisting of switches and hosts, to a logically centralized control plane. The global view and control available to the controller enable incremental updates, management, and allocation of resources to the networks. However, unlike the physical networks or the networks emulated by the emulators, current network simulators still lack integration with the SDN controllers. Hence, currently it is impossible to efficiently orchestrate a simulated network through a centralized controller, or realistically model the controller algorithms and SDN architectures without having the resources for a one-to-one emulation. To address this, this paper presents SDNSim, an SDN simulation middleware, which leverages the principles of SDN for continuous development of cloud and data center networks. SDNSim is an “SDN-aware” network simulator that integrates with the controller through plugins for southbound protocols such as OpenFlow, to execute the algorithms incrementally thus deployed in the control plane.

1. Software-Defined Simulations for
Continuous Development of
Cloud and Data Center Networks
Pradeeban Kathiravelu, Lu´ıs Veiga
INESC-ID Lisboa
Instituto Superior T´ecnico, Universidade de Lisboa
Lisbon, Portugal
24th
International Conference on Cooperative Information Systems (CoopIS 2016)
28th
October 2016, Rhodes, Greece.
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2. Introduction
Introduction
Software-Defined Networking (SDN) is extending its scope.
Separating and unifying the networking control plane from data plane.
Programmable networks → continuous development.
Very high scalability.
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3. Introduction
Introduction
Software-Defined Networking (SDN) is extending its scope.
Separating and unifying the networking control plane from data plane.
Programmable networks → continuous development.
Very high scalability.
Network architectures and algorithms simulated or emulated at early
stages of development.
Native integration of network emulators into SDN controllers.
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4. Motivation
How well the SDN simulators fare?
Network simulators supporting SDN and emulation capabilities.
NS-3.
Cloud simulators extended for cloud networks with SDN.
CloudSim → CloudSimSDN.
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5. Motivation
How well the SDN simulators fare?
Network simulators supporting SDN and emulation capabilities.
NS-3.
Cloud simulators extended for cloud networks with SDN.
CloudSim → CloudSimSDN.
However..
Lack of “SDN-Native” network simulators.
Simulators not following the Software-Defined Systems paradigm.
Policy/algorithmic code locked in simulator-imperative code.
Need for easy migration and programmability.
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6. Motivation
Goals
A simulator for Software-Defined Networking Systems.
Run the control plane code in the actual controller (portability).
Simulate the data plane (scalability, resource efficiency).
by programmatically invoking the southbound of SDN controller.
Extend and leverage the SDN controllers in simulations.
Bring the benefits of SDN to its own simulations!
Reusability, Scalability, Easy migration, . . .
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7. Motivation
“Software-Defined Simulations”
Separation of control plane and (simulated) data plane.
Integration with SDN controllers.
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8. SDNSim Architecture
SDNSim
A Framework for Software-Defined Simulations.
1 Network system to be simulated.
Expressed in “descriptors”.
XML-based description language.
Parsed and executed in SDNSim simulation sandbox.
A Java middleware.
2 Simulated application logic.
Deployed into controller.
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9. SDNSim Architecture
Contributions and SDNSim Approach
1. Reusable simulation building blocks.
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10. SDNSim Architecture
Contributions and SDNSim Approach
1. Reusable simulation building blocks.
Simulating complex and large-scale SDN systems.
Service Function Chaining.
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11. SDNSim Architecture
Contributions and SDNSim Approach
1. Reusable simulation building blocks.
Simulating complex and large-scale SDN systems.
Service Function Chaining.
As a case of Network Function Virtualization (NFV).
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12. SDNSim Architecture
Contributions and SDNSim Approach
2. Support for continuous development and iterative deployment.
Checkpointing and versioning of simulated application logic.
Incremental updates: changesets as OSGi bundles in the control plane.
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13. SDNSim Architecture
Contributions and SDNSim Approach
3. State-aware simulations.
Adaptive scaling through shared state.
Horizontal scalability through In-Memory Data Grids.
State of the simulations for scaling decisions.
Pause-and-resume simulations.
Multi-tenanted parallel executions.
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14. SDNSim Architecture
Contributions and SDNSim Approach
4. Expressiveness.
Data plane: XML-based representations of the network.
Control plane: Java API.
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15. SDNSim Prototype
Prototype Implementation
Oracle Java 1.8.0 - Development language.
Apache Maven 3.1.1 - Build the bundles and execute the scripts.
Infinispan 7.2.0.Final - Distributed cluster.
Apache Karaf 3.0.3 - OSGi run time.
OpenDaylight Beryllium - Default controller.
Multiple deployment options:
As a stand-alone simulator.
Distributed execution with an SDN controller.
As a bundle in an OSGi-based SDN controller.
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16. Evaluation
Evaluation Deployment Configurations
Intel R CoreTM i7-4700MQ
CPU @ 2.40GHz 8 processor.
8 GB memory.
Ubuntu 14.04 LTS 64 bit operating system.
A cluster of up to 5 identical computers.
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17. Evaluation
Evaluation Strategy
Benchmark against
CloudSimSDN.
Cloud2
Sim for distributed
execution.
Simulating routing algorithms in
fat-tree topology.
Experiments repeated 6 times.
Data center simulations of up to
100,000 nodes.
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18. Evaluation
Performance and Problem Size
Higher performance in larger simulations.
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19. Evaluation
Horizontal scalability
Smart scale-out.
Higher horizontal scalability.
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20. Evaluation
Performance with Incremental Updates
Smaller simulations: up to 1000 nodes.
SDNSim: controller and middleware execution completion time.
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21. Evaluation
Performance with Incremental Updates
Initial execution takes longer - Initializations.
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22. Evaluation
Performance with Incremental Updates
Faster executions once the system is initialized.
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23. Evaluation
Incremental Updates: Test-driven development
Faster executions once the system is initialized.
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24. Evaluation
Incremental Updates: Test-driven development
Even faster executions for subsequent simulations.
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25. Evaluation
Incremental Updates: Test-driven development
No change in simulated environment - Deploy changesets to
controller.
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26. Evaluation
Incremental Updates: Test-driven development
No change in simulated environment - Revert changeset.
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27. Evaluation
Performance with Incremental Scaling
No change in controller - scale the simulated environment.
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28. Conclusion
Conclusion
Conclusions
SDNSim is an SDN-aware network simulator
Built following the SDN paradigm
Separation of data layer from the control layer and application logic.
Enabling an incremental modelling of cloud networks.
Performance and scalability.
Complex network systems simulations.
Reuse the same controller code algorithm developers created to
simulate much larger scale deployments.
Adaptive parallel and distributed simulations.
Future Work
Extension points for easy migrations.
More emulator and controller integrations.
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29. Conclusion
Conclusion
Conclusions
SDNSim is an SDN-aware network simulator
Built following the SDN paradigm
Separation of data layer from the control layer and application logic.
Enabling an incremental modelling of cloud networks.
Performance and scalability.
Complex network systems simulations.
Reuse the same controller code algorithm developers created to
simulate much larger scale deployments.
Adaptive parallel and distributed simulations.
Future Work
Extension points for easy migrations.
More emulator and controller integrations.
Thank you!
Questions?
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30. Additional Slides
Additional Slides
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31. Additional Slides
Network Construction with Mininet and SDNSim
Adaptive Emulation and Simulation.
Simulate when resources are scarce for emulation.
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32. Additional Slides
Automated Code Migration: Simulation → Emulation
Time taken to progreammatically convert an SDNSim simulation
script into a Mininet script.
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