Virtual Links Mapping in Future SDN-enabled Networks

Transcription

1 Security Level: Virtual Links Mapping in Future SDN-enabled Networks R. Trivisonno, I. Vaishnavi, R. Guerzoni, Z. Despotovic A. Hecker, S. Beker, D. Soldani SDN4FNS, Trento, Italy 11 th -13 th November 2013 HUAWEI TECHNOLOGIES CO., LTD.

2 Emerging SDN technology SDN is a new networking paradigm based on the physical separation of the network control plane from the forwarding plane, and where a control plane controls several devices Key SDN features: Directly programmable Agile Centrally managed Programmatically configured Open standards-based and vendor-neutral (*) From

3 Traffic Engineering Problem in SDN-enabled networks Traffic engineering Problem Virtual Link Embedding request #1 Saturated Virtual Link Embedding request #2 Goal: maximising the number of virtual links embedded in a given physical infrastructure

4 TE in SDN Network - VLM Algorithm According to SDN paradigm, we assume a central controller with whole physical network knowledge in charge of solving the TE problem, implementing the VLM algorithm VLM solves the path computation problem via a MILP formulation VLM exploit the full network knowledge owned by the SDN controller VLM goals and key features It handles G Virtual Links Embedding Requests simultaneously The g-th embedding request is made by Kg 2 nodes directed virtual graphs It provides embedding problem solution even if not all G requests can be satisfied It aims at maximising the number of virtual links embedded, minimising at the same time the usage of physical resources ( links bandwidth )

5 VLM MILP formulation (1/5) Phy infrastructure model The physical infrastructure is modeled as a non directed physical graph where: set of phy nodes set of phy links Connectivity matrix are also defined and Link Capacity matrix, where denotes the available capacity of the link connecting to

6 VLM MILP formulation (2/5) Virtual Link Request Model Let G be the number of Virtual Link Embedding requests managed by VLM, the g-th request is modeled by a set of 2-node directed virtual graph with set of 2 nodes identifying source and destination virtual link connecting source to destination with bandwidth equal to 2 1 3

7 VLM MILP formulation (3/5) Problem Variable Definition For each virtual link mapping request g, associated to the k-th 2-nodes directed virtual graphs, the following boolean variables are defined where

8 VLM MILP formulation (4/5) Constraints Definition Topology Constraints: A link can be used only if it exists The bandwidth allocated on a link can not exceed the link capacity In case of directed graph: In case of undirected graph Multi-commodity flow like constraint: with

9 VLM MILP formulation (5/5) Objective Function Definition The objective function is defined to maximize, at each VLM iteration, the number of Virtual Links embedded in the physical substrate out of the G requests, while minimizing the physical resource utilization Maximize the number of yg variables set to 1 This can be accomplished pursuing: Where Minimize the number of links used, i.e. variables xij set to 1

10 VLM Algorithms Performance Evaluation Simulation (main) Parameters Setting Substrate Network Parameter Value/Range R1 R7 Number of Substrate Nodes ( Graph connectivity = 0.5 ) Virtual Link Embedding Requests Parameter Value/Range Virtual Node factor Virtual Link factor 0.2 Network Offered Load Orchestrator Configuration Parameter 20 (low load), 40 (high load) Value/Range G (lookahead) factor 5, 10, 20 Algorithm KPIs R1 R2 R3 Percentage of Successful Embedded Requests (%) Over the Average Offered Load Orchestrator Embedding Gain (%) Over the Average Offered Load Substrate Network Resource Utilisation (%) over the Average Embedded Load R4 Meaning R5 R6 R7 Ability of the algorithm to embed requests in the available infrastructure Measure of Orchestrator Effectiveness Measure of Orchestrator Efficiency

11 Increasing G VLM Algorithms Performance Evaluation Acceptance Rate VLM outperforms SPF at any load condition and for any substrate size Performance improvements increase with G Low Load Condition NodeStress=20, Link Factor=0.2 High Load Condition NodeStress=40, Link Factor=0.2

12 VLM Algorithms Performance Evaluation Average Embedding Gain Embedding Gain vs SPF is up to 30% at low load conditions and for G=20 At high load the physical substrate saturates, and this limits VLM gain and the boosting effect of G At low load, for G=20, 60%+ link utilisation is achieved

13 VLM Algorithms Performance Evaluation Rationale behind performance improvement: Resource Utilisation An increase of G factor is reflected in a higher average resource utilisation The link utilisation CDF highlights the different way link bandwidth is utilised by VLM

14 Conclusions and Future Work VLM MILP formulation leads to a significant performance improvement compared to traditional SPF-based TE approach Embedding Acceptance Rate improves in all scenarios simulated Increasing the lookahead factor G boosts VLM performance Up to 30% gain is achieved in low load conditions, for G=20 Performance improvement is due to the better resource utilisation VLM allows Future Work Performance comparison vs heuristic deviation of SPF Implementation issues analysis, including: path setup constraints & requirements algorithm scalability VLM algorithm enhancement to support: QoS features Virtual Request resiliency requirements

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