A Connectionist Approach to Dynamic Resource Management for Virtualised Network Functions
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1. The document proposes a connectionist approach using graph neural networks for dynamic resource management in virtualized network functions to improve efficiency while ensuring reliability. 2. It aims to predict VNF resource requirements to avoid unnecessary resources being kept active/standby through topology-aware resource management using a GNN model. 3. The GNN model takes VNFC features and states as input and computes VNFC states over iterations before outputting resource forecasts to dynamically manage resources.
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A Connectionist Approach to Dynamic Resource Management for Virtualised Network Functions
- 1. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion A Connectionist Approach to Dynamic Resource Management for Virtualised Network Functions Rashid Mijumbi∗, Sidhant Hasija∗, Steven Davy∗, Alan Davy∗, Brendan Jennings∗ and Raouf Boutaba† ∗Telecommunications Software and Systems Group, Waterford Institute of Technology, Ireland †D.R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada Montreal, Canada, November 1, 2016
- 2. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Presentation Outline 1 Introduction: Network Functions Virtualisation 2 Problem: Efficient vs Reliable Resource Management 3 Proposed Approach: Graph Neural Networks 4 Solution Model: GNN-based Dynamic Resource Management 5 Performance Evaluation 6 Conclusion and Future Work
- 3. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Network Functions Virtualisation Increasing CAPEX and OPEX The short lifetime of the NAs leads to increased Capital Expenses (CAPEXs). When NAs are specialised, they require specialised maintenance and limits flexibility, leading to increased Operating Expenses (OPEXs). Declining Revenues Competition with over-the-top providers Inability to quickly provide new services Separation between infrastructure and Service Optimization of resource Usage and routing beyond BGP
- 4. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Network Functions Virtualisation Physical Resources Virtual Resources Services Network Function Virtualization Infrastructure ManagementandOrchestration Computing, Storage, Network Resources Virtual Network Functions Computing, Storage, Network Resources ManagementandOrchestration VNF 1 VNF 2 VNF 3 VNF n. . . . Source: R. Mijumbi, J. Serrat, J. L. Gorricho, N. Bouten, F. De Turck, R. Boutaba, ”Network Function Virtualization: State-of-the-art and Research Challenges”, IEEE Communications Surveys and Tutorials. 2016.
- 5. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Problem: Efficiency vs Reliability NFV Essential for 5G, Supporting Critical Applications NFV will be an important building block for 5G 5G is expected to support critical infrastructure Efficiency and reliability are important KPIs for 5G Source: http://telematicswire.net/ec-plans-future-of-5g-for-automotive/
- 6. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion State-of-the-art 1 High VM provisioning time threatens reliability in critical applications such as M2M 1 10 100 1000 1 2 4 8 16 32 Number of Virtual Machines TotalProvisioningTime(s) Eucalyptus OpenStack OpenNebula . Adapted from: Mike Jones et al. ”Scalability of VM Provisioning Systems”, 20th Annual IEEE High Performance Extreme Computing Conference(HPEC), September 2016, Waltham, MA USA.
- 7. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Our Proposal Objective Predict VNF Resource Requirements so as: To avoid resources are not unnecessarily kept active/standby While ensuring reliable performance Idea Topology-aware Resource Management Motivation: VNFC Dependencies Virtualization container such as a VM VNFC 1 VNF 1 VNF 2 VNFC 1 VNFC 2 VNFC 3 VNF 4 VNFC 1 VNF 3 Service Function Chain based on Virtualised Network Functions VNFC 3 VNFC 4 VNFC 2 VNFC 5 VNFC 1VNFC 1
- 8. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Graph Neural Networks (GNN) A supervised learning model aimed at solving problems in the graphical domain. Node, 𝑛4 Node, 𝑛3 Node, 𝑛1 Node, 𝑛2 Node, n VNFC Features, 𝑓𝑛 Neighbourhood, , 𝑛∗ Using fn and n , a state sn, and an output on for each node n are determined using equations (1) and (2) respectively. sn = m∈n hw fn, fm, sm , ∀n (1) on = gw sn, fn , ∀n (2)
- 9. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion GNN-based Dynamic Resource Management Features 𝑓𝑛 of VNFC Features 𝑓𝑚 of all VNFC’s Neighbours ℎ 𝑤 𝑔 𝑤 VNFC State 𝑠 𝑛 Output (Resource Forecast) FNN FNN States 𝑠 𝑚 of all Neighbours VNFC States SFC Features Output Computation State Computation 3 4 1 2 Comprised of four main components: (1) SFC features, (2) VNFC states, (3) state computation, and (4) output computation.
- 10. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion SFC Features Observations or monitoring data from the VNFCs Include network parameters (such as CPU or RAM utilisation levels) that can be measured. fn = cn mn dn (3) fnm = bnm dnm (4) - SFC modelled as a directed graph G(N, L) Virtualization container such as a VM VNFC 1 VNF 1 VNFC 3 VNFC 2 𝑛2 𝑛3 𝑛4 𝑛5 𝑙32 𝑙21 𝑙14 𝑙46𝑙31 𝑙15 𝑙51 𝑛0 𝑛7 𝑛8 VNF 1 VNF 2 VNF 3 VNF 4 𝑛5 𝑙41 𝑛6𝑛0 𝑛1 𝑙12 𝑙13 𝑙23 𝑙03 VNFC 1
- 11. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion VNF States 4 𝑥4 VNF 2 𝑛2 VNFC State VNFC Features 𝑛3 𝑛1 𝑠3 𝑓3 𝑠2 𝑓2 𝑛4 𝑛5 𝑠1𝑓1 𝑠1 𝑓1 𝑠3 𝑓3 𝑠2 𝑓2 𝑠4 𝑓4 𝑠1 𝑓1 𝑠1 𝑓1 𝑠5 𝑓5 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3 𝑠4 𝑠1 ℎ 𝑤 ℎ 𝑤 𝑠2 𝑓2 𝑓3 𝑓1 𝑓4 ℎ 𝑤𝑠5 𝑓5 𝑛4 𝑛3 𝑛2 𝑛1 𝑛5
- 12. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion State Computation (1) ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3 𝑠4 𝑠1 ℎ 𝑤 ℎ 𝑤 𝑠2 𝑓2 𝑓3 𝑓1 𝑓4 ℎ 𝑤𝑠5 𝑓5 𝑛4 𝑛3 𝑛2 𝑛1 𝑛5 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3(1) 𝑠2(1) 𝑠1(1) 𝑠5(1) 𝑠4(1) 𝑠2(0) 𝑠1(0) 𝑠3(0) 𝑠1(0) 𝑠3(0) 𝑠1(0) 𝑠5(0) 𝑠4(0) 𝑠1(0) 𝑠1(0) 𝑓2 , 𝑓3 𝑓1 , 𝑓2
- 13. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion State Computation (2) ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3(2) 𝑠2(2) 𝑠1(2) 𝑠5(2) 𝑠4(2) 𝑠2(1) 𝑠1(1) 𝑠3(1) 𝑠1(1) 𝑠3(1) 𝑠2(1) 𝑠5(1) 𝑠4(1) 𝑠1(1) 𝑠1(1) 𝑓2 , 𝑓3 𝑓1 , 𝑓2 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3(1) 𝑠2(1) 𝑠1(1) 𝑠5(1) 𝑠4(1) 𝑠2(0) 𝑠1(0) 𝑠3(0) 𝑠1(0) 𝑠3(0) 𝑠2(0) 𝑠5(0) 𝑠4(0) 𝑠1(0) 𝑠1(0) 𝑓2 , 𝑓3 𝑓1 , 𝑓2 Iteration 1 Iteration 2
- 14. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion State Computation (3) ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3(1) 𝑠2(1) 𝑠1(1) 𝑠5(1) 𝑠4(1) 𝑠2(0) 𝑠1(0) 𝑠3(0) 𝑠1(0) 𝑠3(0) 𝑠2(0) 𝑠5(0) 𝑠4(0) 𝑠1(0) 𝑠1(0) 𝑓2 , 𝑓3 𝑓1 , 𝑓2 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3(2) 𝑠2(2) 𝑠1(2) 𝑠5(2) 𝑠4(2) ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3(3) 𝑠2(3) 𝑠1(3) 𝑠5(3) 𝑠4(3) ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3(𝑇) 𝑠2(𝑇) 𝑠1(𝑇) 𝑠5(𝑇) 𝑠4(𝑇) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Iteration 1 Iteration 3Iteration 2 Iteration T State Computation
- 15. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Output computation ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3(1) 𝑠2(1) 𝑠1(1) 𝑠5(1) 𝑠4(1) 𝑠2(0) 𝑠1(0) 𝑠3(0) 𝑠1(0) 𝑠3(0) 𝑠2(0) 𝑠5(0) 𝑠4(0) 𝑠1(0) 𝑠1(0) 𝑓2 , 𝑓3 𝑓1 , 𝑓2 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3(2) 𝑠2(2) 𝑠1(2) 𝑠5(2) 𝑠4(2) ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3(3) 𝑠2(3) 𝑠1(3) 𝑠5(3) 𝑠4(3) ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 ℎ 𝑤 𝑠3(𝑇) 𝑠2(𝑇) 𝑠1(𝑇) 𝑠5(𝑇) 𝑠4(𝑇) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 𝑂5 𝑔 𝑤 𝑂4𝑔 𝑤 𝑂1 𝑔 𝑤 𝑂2 𝑔 𝑤 𝑂3 𝑔 𝑤 Iteration 1 Iteration 3Iteration 2 Iteration T State Computation Output Computation
- 16. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Summary 1 2 3 0
- 17. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Implementation Setup Bono Sprout Ralf Homer Homestead HSS Mirror cassandra XDMS cassandra Rf CTF memcached I/S-CSCF BGCF memcached P-CSCF, WebRTC Clearwater virtualised IMS SNMPUEs SIPp GNN-based Dynamic Resource Management DNS Heat Orchestration SIP CACTI Monitoring SUT
- 18. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Evaluation Details Setup Parameters and Comparisons 1 100K Users, Call initiation/end based on Poisson/Exponential, 2 Each call transmits media extracted from real Skype traffic traces 3 All VNFCs polled every 15s, History/Forecasting is 20 episodes, 4 Experiment 1: 10,000 data points for training FNNs 5 Experiment 2: Trained System used to determine accuracy on 1,000 measurements 6 Experiment 3: Predictions used to effect resource allocations (Spin-up at 40%, Spin down at 20%) 7 Comparisons: Static, Manual, Automated
- 19. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Evaluations (1) 0 10 20 30 40 50 60 0 200 400 600 800 1000 RMSE Training Iteration, each involving 10,000 examples Ralf Bono Sprout Homestead Homer Total 0.00 0.20 0.40 0.60 0.80 1.00 0 200 400 600 800 1000 %CPUUtlisation Test Number Actual Output Expected Output 0.00 0.50 1.00 1.50 2.00 2.50 3.00 0 200 400 600 800 1000 Delay(ms) Test Number Actual Output Expected Output 0.5 0.6 0.7 0.8 0.9 1 0 200 400 600 800 1000 %DelayPredictionError Test Number Error 100 period Mov. Avg.
- 20. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Evaluations (2) 0 0.2 0.4 0.6 0.8 1 100 400 700 1000 %CPUUtilisation Test Number Static Manual Automated 0.00 0.50 1.00 1.50 2.00 2.50 3.00 100 400 700 1000 Delay(ms) Test Number Static Manual Automated 0 2 4 6 8 100 400 700 1000 DroppedCallsThousands Test Number Static Manual Automated 0 10 20 30 40 100 400 700 1000 DroppedCallsThousands Test Number Static Manual Automated
- 21. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Conclusion and Future Work Conclusion Topology-aware approach to automated and dynamic resource management approach for NFV environments. Implemented in a real environment involving a virtualised IMS, and using real VoIP traces, Prediction accuracy of about 90%, and enhance the processing delay and call drop rate by 29% and 27% respectively. Future Work Improve generalisation accuracy by considering error functions with different penalty terms. More efficient ways of training the SFC encoding network.
- 22. Introduction Problem Proposed Approach Solution Model Evaluation Conclusion Thank You THANK YOU! Contact: rmijumbi@tssg.org