Data for NFVSDN experiments
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Rankothge, W, Le, F, Russo, A, Lobo, J. Data for NFVSDN experiments. Universitat Pompeu Fabra; 2017. doi: 10.34810/data481
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Abstract
Network Function Virtualization (NFV) proposes to move packet processing from dedicated hardware middle-boxes to software running on commodity servers: virtualized Network Function (NFs) (i.e, Firewall, Proxy, Intrusion Detection System etc.). We have been developing an experimental platform called Network Function Center (NFC) to study issues related to NFV and NFs, assuming that the NFC will deliver virtualized NFs as a service to clients on a subscription basis. Our studies specially focus on dynamic resource allocation for NFs and we have proposed two new resource allocation algorithms based on Genetic Programming (GP) [1] and currently working on another algorithm based on Iterative Local Search. For a more realistic evaluation of these algorithms, testing data is a fundamental component, but unfortunately, public traffic data specifically referring to virtualized NFs chains is not readily available. Therefore, we developed a model to generate the specific data we needed, based on the available general traffic data [2].
This repository contains all the details about how we modelled general data into the specific data we wanted, with along the software we used and the assumptions we made during the data modelling process. Using this data and programs, the evaluation results presented in our publications can be easily reproduced.
[1] W. Rankothge, J. Ma, F. Le, A. Russo, and J. Lobo, [“Towards making network function virtualization a cloud computing service,”] (http://repositori.upf.edu/handle/10230/26035) in IM 2015.
[2] W. Rankothge, F. Le, A. Russo, and J. Lobo, [“Experimental results on the use of genetic algorithms for scaling virtualized network functions,”] (http://repositori.upf.edu/handle/10230/26036) in IEEE SDN/NFV 2015.
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Description
##Project Structure:
1. GeneratePolicies.
2. DistributeTrafficOverPolicies.
3. PoliciesToChange.
4. TopologyCreator.
5. ExampleDataSet.
##Guidelines to use the data and programs in the repository.
There are two ways that this repository can be useful for anyone that needs data about VNFs and their traffic on the cloud.
1.Directly use the already generated data set.
2.Generate your own data set using the given programs.
##How to use the already generated data set: ExampleDataSet.
We have generated data for:
1.Possible policy requests with initial traffic passing through them defined.
2.Scaling requirements for each 15 minutes for 2 days.
3.Topology data (nodes, links, paths) for K-Fat Tree, BCube and VL2 architectures with 64 servers.
You can use these data directly as inputs for your experiments.
##How to use the programs and generate the required data sets.
If you want to generate your own data sets according to your requirements, you can use the given programs.
1) First step is to generate the policy requests data set using the policy requests generation program: GeneratePolicies.
- Inputs to the program: number of large scaled enterprise networks.
- Output of the program: a set of policies for each enterprise with 100 NFs.
2) After we have created the policy requets data set, the seconds step is to create the traffic data set for the policies using the initial traffic distribution program: DistributeTrafficOverPolicies.
- Inputs to the program: the set of policies, initial traffic load.
- Output of the program: distribution of the traffic load over policies.
3) The third step is to create the scaling requirements data set to reflect the traffic changes over the time using the scaling requirements over the time program: PoliciesToChange.
4) The last step is to generate the required topology data for different network architectures (K-Fat tree, BCube, VL2) using the topology generation program: TopologyCreator.
- Inputs to the program: network architecture and number of servers.
- Output of the program: the topology: nodes, links and paths.
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Related Publication/Data
Publicació relacionada: Rankothge, Windhya. Towards virtualized network functions as a service. 2017
http://hdl.handle.net/10803/402892
Publicació relacionada: Rankothge W, Ma J, Le F, Russo A, Lobo J. Experimental results on the use of genetic algorithms for scaling virtualized network functions. In: 2015 IEEE Conference on Virtualization and Software Defined Network (NFV-SDN); 2015 Nov 18-21; San Francisco, CA. IEEE; 2015. p. 47-53. DOI 10.1109/NFV-SDN.2015.7387405. http://hdl.handle.net/10230/26036
Publicació relacionada: Rankothge W, Ma J, Le F, Russo A, Lobo J. Towards making network function virtualization a cloud computing service. In: Proceedings of the/n2015 IFIP/IEEE International Symposium on Integrated Network Management (IM); 2015 May 11-15; Ottawa, Canada. IEEE; 2015. p. 89-97. DOI 10.1109/INM.2015.7140280. http://hdl.handle.net/10230/26035
Publicació relacionada: Rankothge W, Ma J, Le F, Russo A, Lobo J. Experimental results on the use of genetic algorithms for scaling virtualized network functions. In: 2015 IEEE Conference on Virtualization and Software Defined Network (NFV-SDN); 2015 Nov 18-21; San Francisco, CA. IEEE; 2015. p. 47-53. DOI 10.1109/NFV-SDN.2015.7387405. http://hdl.handle.net/10230/26036
Publicació relacionada: Rankothge W, Ma J, Le F, Russo A, Lobo J. Towards making network function virtualization a cloud computing service. In: Proceedings of the/n2015 IFIP/IEEE International Symposium on Integrated Network Management (IM); 2015 May 11-15; Ottawa, Canada. IEEE; 2015. p. 89-97. DOI 10.1109/INM.2015.7140280. http://hdl.handle.net/10230/26035
