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 Peter Dinda Department of Computer Science Northwestern University

2. Overview • Motivation behind parallel programs in a VM environment • Goal: To infer the communication behavior • Offline implementation • Evaluating with parallel benchmarks • Online Monitoring in a VM environment • Conclusions

3. Virtuoso: A VM based abstraction for a Grid environment

5. Motivation • A distributed computing environment based on Virtual Machines • Raw machines connected to user’s network • Our Focus: Middleware support to hide the Grid complexity

8. Motivation • A distributed computing environment based on Virtual Machines • Raw machines connected to user’s network • Our Focus: Middleware support to hide the Grid complexity • Our goal here: Efficient execution of Parallel applications in such an environment

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

10. VNET • Abstraction: A set of VMs on same Layer 2 network • Virtual Ethernet LAN

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

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

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

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

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

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

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

18. The offline method Synced Parallel Traffic Monitoring Traffic Filtering and Matrix Generation Matrix Analysis and Topology Characterization PVMPOV Inference

19. Synced Parallel Traffic Monitoring Traffic Filtering and Matrix Generation Matrix Analysis and Topology Characterization Infer.pl

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

21. 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

22. Application benchmarks • NAS PVM benchmarks • Popular benchmarks for parallel computing • 5 benchmarks • PVM-POV : Distributed Ray Tracing • Many others possible… • The inference not PVM specific • Applicable to all communication . • e.g. MPI, even non-parallel apps

23. Patterns application 3-D Toroid 3-D Hypercube 2-D Mesh Reduction Tree All-to-All

24. PVM NAS benchmarks Parallel Integer Sort

25. Traffic Matrix for PVM IS benchmark

26. Traffic Matrix for PVM IS benchmark Placement of host1 is crucial on the network

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

28. Overall Design • VNET • Abstraction: A set of VMs on same Layer 2 network • Virtual Ethernet LAN

29. A VNET virtual layer A Virtual LAN over wide area VNET Layer Physical Layer

30. Overall Design • VNET • Abstraction: A set of VMs on same Layer 2 network • Extend VNET to include the required features • Monitoring at Ethernet packet level • The Challenge here • Lacks manual control • Detecting interesting parallel program communication ?

31. Detecting interesting phenomenon • 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 ? Reactive Mechanisms Proactive Mechanisms Like a Burglar Alarm Video Surveillance

32. 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

33. 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 (When do you push the traffic matrix ?) • Operation is associative: reduction trees for scalability The proxy daemon

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

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

36. Current Work • Capabilities for dynamic adaptation into VNET • Spatial Inference Network Adaptation for Improved Performance • Prelim Results: Improved performance upto 40% in execution time • Looking into benefits of Dynamic Adaptation

37. For more information • http://virtuoso.cs.northwestern.edu • VNET is available for download • PLAB web site: plab.cs.northwestern.edu