1. Inferring the Topology and Traffic Load of Parallel Programs in a VM environment Ashish Gupta Resource Virtualization Winter Quarter Project

2. Overview Motivation Goal Offline implementation : Proof of concept Evaluating with parallel benchmarks –Synthetic benchmarks –Application benchmarks The NAS benchmarks Monitoring in a VM environment Conclusions

3. Motivation A distributed computing environment based on Virtual Machines Goal: Efficient execution of Parallel applications in such an environment

4. Parallel Application Behavior Intelligent Placement and virtual networking of parallel applications VM Encapsulation Virtual Networks With VNET

5. Goal of this project Through low level packet traffic monitoring and analysis Inferring communication properties of parallel applications –Topology –Bandwidth requirements –Other ?

6. Goal of this project Low Level Traffic Monitoring ? An online topology inference framework for a VM environment

7. Approach Design an offline framework Evaluate with parallel benchmarks If successful, design an online framework for VMs

8. An offline topology inference framework Goal: A test-bed for traffic monitoring and evaluating topology inference methods

9. The offline method Synced Parallel Traffic Monitoring Traffic Filtering and Matrix Generation Matrix Analysis and Topology Characterization

10. The offline method Synced Parallel Traffic Monitoring Traffic Filtering and Matrix Generation Matrix Analysis and Topology Characterization h1h2h3h4 h1 7.77.67.8 h213.1 6.66.5 h313.56.4 6.6 h413.26.5 *numbers indicate MB of data transferred.

11. The offline method Synced Parallel Traffic Monitoring Traffic Filtering and Matrix Generation Matrix Analysis and Topology Characterization

13. Parallel Benchmarks Evaluation Goal: To test the practicality of low level traffic based inference

14. Parallel Benchmarks used Synthetic benchmarks: Patterns –N-dimensional mesh-neighbor –N-dimensional toroid-neighbor –N-dimensional hypercubes –Tree reduction –All-to-All Scheduling mechanism to generate deadlock free and efficient schemes 123

15. Application benchmarks NAS PVM benchmarks –Popular benchmarks for parallel computing –5 benchmarks PVM-POV : Distributed Ray Tracing Many others…

16. Patterns application

17. PVM NAS benchmarks Parallel Integer Sort

18. h1h2h3h4h5h6h7h8 h1 19.019.619.219.618.813.719.3 h222.6 10.710.810.710.99.710.5 h322.28.78 11.210.410.110.5 h422.48.99.5 11.110.810.610.2 h522.310.09.519.72 11.710.911.9 h624.08.910.79.910.8 12.212.1 h723.210.09.79.510.310.2 12.0 h824.911.211.011.811.511.210.7 *numbers indicate MB of data transferred.

19. An Online Topology Inference Framework Goal: To automatically detect, monitor and report the global traffic matrix for a set of VMs running on a overlay network

20. Overall Design Extend VNET to include the required features –Allows a set of VMs to be on same Layer 2 domain –Monitoring at ethernet packet level Challenge –Lacks manual control –Detecting interesting parallel program communication ?

21. Detecting interesting phenomenon Reactive MechanismsProactive Mechanisms Certain address properties Based on Traffic rate Etc. Provide support for queries by external agent Rate based monitoring Non-uniform discrete event sampling What is the Traffic Matrix for the last n seconds ?

22. Traffic Analyzer Rate based Change detection Traffic Matrix Query Agent VM Network Scheduling Agent VNET daemon VM VNET overlay network To other VNET daemons Physical Host

23. Traffic Matrix Aggregation Each VNET daemon keeps track of local traffic matrix –Need to aggregate this information for a global view –When the rate falls, the local daemons push the traffic matrix The proxy daemon

24. Evaluation Used 4 Virtual Machines over VNET NAS IS benchmark

25. Conclusions Possible to infer the topology with low level traffic monitoring A Traffic Inference Framework for Virtual MachinesReady to move on to future steps