Software-Defined Simulations for Continuous Development of Cloud and Data Center Networks

Software-Defined Simulations forContinuous Development of

Cloud and Data Center Networks

Pradeeban Kathiravelu, Luıs Veiga

INESC-ID LisboaInstituto Superior Tecnico, Universidade de Lisboa

Lisbon, Portugal

24 th International Conference on Cooperative Information Systems (CoopIS 2016)28 th October 2016, Rhodes, Greece.

Pradeeban Kathiravelu Software-Defined Simulations 1 / 32

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.


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 earlystages of development.

Native integration of network emulators into SDN controllers.


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.


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.


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, . . .


Motivation

“Software-Defined Simulations”

Separation of control plane and (simulated) data plane.Integration with SDN controllers.


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.


SDNSim Architecture

Contributions and SDNSim Approach

1. Reusable simulation building blocks.


SDNSim Architecture


1. Reusable simulation building blocks.Simulating complex and large-scale SDN systems.

Service Function Chaining.


SDNSim Architecture


1. Reusable simulation building blocks.Simulating complex and large-scale SDN systems.

Service Function Chaining.

As a case of Network Function Virtualization (NFV).


SDNSim Architecture


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.


SDNSim Architecture


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.


SDNSim Architecture


4. Expressiveness.

Data plane: XML-based representations of the network.Control plane: Java API.


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.


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.


Evaluation

Evaluation Strategy

Benchmark againstCloudSimSDN.

Cloud2Sim for distributedexecution.

Simulating routing algorithms infat-tree topology.

Experiments repeated 6 times.

Data center simulations of up to100,000 nodes.


Evaluation

Performance and Problem Size

Higher performance in larger simulations.Pradeeban Kathiravelu Software-Defined Simulations 18 / 32

Evaluation

Horizontal scalability

Smart scale-out.

Higher horizontal scalability.


Evaluation

Performance with Incremental Updates

Smaller simulations: up to 1000 nodes.SDNSim: controller and middleware execution completion time.


Evaluation


Initial execution takes longer - Initializations.


Evaluation


Faster executions once the system is initialized.


Evaluation

Incremental Updates: Test-driven development

Faster executions once the system is initialized.


Evaluation


Even faster executions for subsequent simulations.


Evaluation


No change in simulated environment - Deploy changesets tocontroller.


Evaluation


No change in simulated environment - Revert changeset.


Evaluation

Performance with Incremental Scaling

No change in controller - scale the simulated environment.


Conclusion

Conclusion

Conclusions

SDNSim is an SDN-aware network simulatorBuilt 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 tosimulate much larger scale deployments.Adaptive parallel and distributed simulations.

Future Work

Extension points for easy migrations.

More emulator and controller integrations.


Conclusion

Conclusion

ConclusionsSDNSim is an SDN-aware network simulator

Built following the SDN paradigmSeparation 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 tosimulate much larger scale deployments.Adaptive parallel and distributed simulations.

Future WorkExtension points for easy migrations.

More emulator and controller integrations.

Thank you!Questions?


Additional Slides

Additional Slides


Additional Slides

Network Construction with Mininet and SDNSim

Adaptive Emulation and Simulation.

Simulate when resources are scarce for emulation.


Additional Slides

Automated Code Migration: Simulation → Emulation

Time taken to progreammatically convert an SDNSim simulationscript into a Mininet script.


Software-Defined Simulations for Continuous Development of Cloud and Data Center Networks

Technology

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