Inferring the Topology and Traffic Load of Parallel Programs in a VM environment

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

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

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

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

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

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

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

8. The offline method h1 h2 h3 h4 h1 7.7 7.6 7.8 h2 13.1 6.6 6.5 h3 13.5 6.4 6.6 h4 13.2 6.5 6.5 *numbers indicate MB of data transferred. Synced Parallel Traffic Monitoring Traffic Filtering and Matrix Generation Matrix Analysis and Topology Characterization

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

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

12. Parallel Benchmarks used 1 2 3 • 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

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

14. Patterns application

15. PVM NAS benchmarks Parallel Integer Sort

16. h1 h2 h3 h4 h5 h6 h7 h8 h1 19.0 19.6 19.2 19.6 18.8 13.7 19.3 h2 22.6 10.7 10.8 10.7 10.9 9.7 10.5 h3 22.2 8.78 11.2 10.4 10.1 10.5 10.5 h4 22.4 8.9 9.5 11.1 10.8 10.6 10.2 h5 22.3 10.0 9.51 9.72 11.7 10.9 11.9 h6 24.0 8.9 10.7 9.9 10.8 12.2 12.1 h7 23.2 10.0 9.7 9.5 10.3 10.2 12.0 h8 24.9 11.2 11.0 11.8 11.5 11.2 10.7 *numbers indicate MB of data transferred.

17. 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

18. 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 ?

19. Detecting interesting phenomenon Reactive Mechanisms Proactive 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 ?

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

21. 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

22. Evaluation • Used 4 Virtual Machines over VNET • NAS IS benchmark

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