Inferring the Topology and Traffic Load of Parallel Programs in a VM environment
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Transcript of Inferring the Topology and Traffic Load of Parallel Programs in a VM environment
Inferring the Topology and Traffic Load of Parallel Programs in a VM
environment
Ashish GuptaPeter Dinda
Department of Computer ScienceNorthwestern 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
ParallelApplication 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
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
1 2 3
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 Toroid 3-D Hypercube
Reduction Tree All-to-All
PVM NAS benchmarks
Parallel Integer Sort
Traffic Matrix for PVM IS benchmark
Traffic Matrix for PVM IS benchmark
Placement of host1 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 Mechanisms Proactive Mechanisms
•Certain address properties
•Based on Traffic rate
•Etc.
Provide support for queries by external agent
Rate based monitoringNon-uniform discrete event sampling
What is the Traffic Matrix for the last n seconds ?
Like a Burglar Alarm Video Surveillance
Traffic Analyzer
Rate based Change detection
Traffic MatrixQuery 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
• http://virtuoso.cs.northwestern.edu• VNET is available for download
• PLAB web site:
plab.cs.northwestern.edu