Inferring the Topology and Traffic Load of Parallel Programs in a VM environment Ashish Gupta Peter Dinda Department of Computer Science Northwestern University

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

Virtuoso: A VM based abstraction for a Grid environment

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Patterns application 2-D Mesh 3-D Toroid3-D Hypercube Reduction TreeAll-to-All

PVM NAS benchmarks Parallel Integer Sort

Traffic Matrix for PVM IS benchmark

Placement of host 1 is crucial on the network

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

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

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

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 ?

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 ? Like a Burglar AlarmVideo Surveillance

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

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

Evaluation Used 4 Virtual Machines over VNET NAS IS benchmark

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

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

For more information VNET is available for download PLAB web site: plab.cs.northwestern.edu