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Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer Science Northwestern University http:// virtuoso.cs.northwestern.edu

Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Page 1: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

Increasing Application Performance In Virtual Environments Through Run-time Inference

and Adaptation

Ananth I. Sundararaj

Ashish Gupta

Peter A. Dinda

Prescience Lab

Department of Computer Science

Northwestern University

http://virtuoso.cs.northwestern.edu

Page 2: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Summary• Dynamically adapt unmodified applications on unmodified

operating systems in virtual environments to available resources

• The adaptation mechanisms are application independent and controlled automatically without user or developer help

• Demonstrate the feasibility of adaptation at the level of collection of VMs connected by Virtual Networks

• Show that its benefits can be significant for two classes of applications

Page 3: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Outline

• Virtual machine grid computing

• Virtuoso system

• Networking challenges in Virtuoso

• Enter VNET

• VNET, VTTIF Adaptive virtual network

• Evaluation

• Summary

Page 4: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Aim

Grid Computing

New Paradigm

Traditional Paradigm

Deliver arbitrary amounts of computational power to perform distributed and parallel computations

Problem1:

Grid Computing using virtual machines

Problem2:

Solution

How to leverage them?

Virtual Machines What are they?

6b

6a

5

4

3b3a

2

1

Resource multiplexing using OS level mechanism

Complexity from resource user’s perspective

Complexity from resource owner’s perspective

Virtual Machine Grid Computing

Page 5: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Virtual MachinesVirtual machine monitors (VMMs)

•Raw machine is the abstraction

•VM represented by a single image

•VMware GSX Server

Page 6: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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The Simplified Virtuoso Model

Orders a raw machine

User

Specific hardware and performance

Basic software installation available

User’s LAN

VM

Virtual networking ties the machine back to user’s home network

Virtuoso continuously monitors and adapts

Page 7: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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User’s View in Virtuoso Model

User

User’s LAN

VM

Page 8: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Outline

• Virtual machine grid computing

• Virtuoso system

• Networking challenges in Virtuoso

• Enter VNET

• VNET, VTTIF Adaptive virtual network

• Evaluation

• Summary

Page 9: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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User’s friendlyLAN

Foreign hostile LAN

Virtual Machine

VNET: A bridge with long wires

Host

Proxy

X

Virtual NetworksVM traffic going out on foreign LAN

IP network

A machine is suddenly plugged into a foreign network. What happens?

• Does it get an IP address?• Is it a routeable address?• Does firewall let its traffic through? To any port?

Page 10: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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HostHost

vmnet0

Ethernet Packet Tunneledover TCP/SSL Connection

Ethernet Packet Captured by Interface in Promiscuous mode

“Host Only” Network

Ethernet Packet is Matched against the Forwarding Table on that VNET

Ethernet Packet is Matched against the Forwarding Table on that VNET

First link Second link (to proxy)

Local traffic matrix inferred by VTTIF

Periodically sent to the VNET on the Proxy

VNET

ethz

VM 2

“eth0”

VNET

ethy

IP Network

VM 1“eth0”

vmnet0

A VNET Link

Page 11: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Virtual Topology and Traffic Inference Framework (VTTIF) Operation

Application topology is recovered using normalization and pruning algorithms

Ethernet-level traffic monitoring

VNET daemons collectively aggregate a global traffic matrix for all VMs

Page 12: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Dynamic Topology Inference by VTTIF

1. Fast updates

Smoothed Traffic Matrix

2. Low Pass Filter Aggregation

3. Threshold change detection

Topology change output

VNET Daemons on Hosts

VNET Daemon at Proxy

AggregatedTraffic Matrix

Page 13: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Outline

• Virtual machine grid computing

• Virtuoso system

• Networking challenges in Virtuoso

• Enter VNET

• VNET, VTTIF Adaptive virtual network

• Evaluation

• Summary

Page 14: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Monitoring and inference

Application performance measure

Adaptation algorithm

Adaptation mechanisms

AdaptationApplications

Optimization metric

1. Overlay topology

2. Forwarding rules

3. VM migration

1. Single hop

2. Worst fit

1. BSP

2. Transactional ecommerce

1. Application throughput

1. VTTIF

2. Network monitoring

1. Single metric

2. Combined metric

Page 15: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Optimization Problem (1/2) Topology Only

Informally stated:

• Input– Network traffic load matrix of application

• Output– Overlay topology connecting hosts– Forwarding rules on the topologySuch that the application throughput is

maximized

The algorithm is described in detail in the paper

Page 16: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Foreign hostLAN 1

User’sLAN

Host 2+

VNET

Proxy+

VNET

IP network

Host 3+

VNET

Host 4+

VNET

Host 1+

VNET

Foreign host LAN 3

Foreign host LAN 4

Foreign host LAN 2

VM 1

VM 4VM 3

VM 2

Resilient Star Backbone

Merged matrix as inferred by VTTIF

Illustration of Topology Adaptation in Virtuoso

Fast-path links amongst the VNETs hosting VMs

Page 18: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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0

0.5

1

1.5

2

2.5

3

3.5

Sec

onds

0.94

1.6

3.23

2.92

2.268

Reaction Time

Page 19: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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ide

al

com

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test

ar 1 2 3 4 5 6 7 8 9 10

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0

0.5

1

1.5

2

2.5

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3.5

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4.5

5

Ite

ratio

ns/

seco

nd

Number of Fast Path Links in Virtual Topology

No Fast Path Topology

Full all-to-all network afterstartup measurement+ reconfiguration cost

Full all-to-all frombeginning of run

Dynamic measurement andreconfiguration

Benefits of AdaptationBenefits accrued as a function of the number of fast-path links added

•Patterns has an all-all topology

•Eight VMs are used

•All VMs are hosted on the same cluster

Page 20: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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com

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test

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0

0.5

1

1.5

2

2.5

Ite

ratio

ns/

seco

nd

Number of Fast Path Links in Virtual Topology

No Fast Path Topology

Full all-to-all network afterstartup measurement+ reconfiguration cost

Full all-to-all frombeginning of run

Dynamic measurement andreconfiguration

•Patterns has an all-all topology

•Eight VMs are used

• VMs are spread over WAN

Benefits of AdaptationBenefits accrued as a function of the number of fast-path links added

Page 21: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Informally stated:• Input

– Network traffic load matrix of application– Topology of the network

• Output– Mapping of VMs to hosts– Overlay topology connecting hosts– Forwarding rules on the topologySuch that the application throughput is maximized

Optimization Problem (2/2) Topology + Migration

The algorithm is described in detail in the paper

Page 22: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Evaluation

• Applications– Patterns: A synthetic BSP benchmark– TPC-W: Transactional web ecommerce

benchmark

• Benefits of adaptation (performance speedup)– Adapting to compute/communicate ratio– Adapting to external load imbalance

Page 23: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Effect on BSP Application Throughput of Adapting to Compute/Communicate Ratio

Page 24: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Effect on BSP Application Throughput of Adapting to External Load Imbalance

Page 25: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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TPCW Throughput (WIPS) With Image Server Facing External Load

No Topology Topology

No Migration 1.216 1.76

Migration 1.4 2.52

Page 26: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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Outline

• Virtual machine grid computing

• Virtuoso system

• Networking challenges in Virtuoso

• Enter VNET

• VNET, VTTIF Adaptive virtual network

• Evaluation

• Summary

Page 27: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

27

Summary• Dynamically adapt unmodified applications on unmodified

operating systems in virtual environments to available resources

• The adaptation mechanisms are application independent and controlled automatically without user or developer help

• Demonstrate the feasibility of adaptation at the level of collection of VMs connected by Virtual Networks

• Show that its benefits can be significant for two classes of applications

Page 28: Increasing Application Performance In Virtual Environments Through Run-time Inference and Adaptation Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience

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• Future Work– Free network measurement (Wren) – Collaboration with CS, W&M– Applicability of a single optimization scheme

• Related Talk at HPDC 2005– J. Lange, A. Sundararaj, P. Dinda, “Automatic Dynamic Run-time

Optical Network Reservations”– Wednesday, July 27, 2:00 P.M.

• Please visit– Prescience Lab (Northwestern University)

• http://plab.cs.northwestern.edu

– Virtuoso: Resource Management and Prediction for Distributed Computing using Virtual Machines

• http://virtuoso.cs.northwestern.edu• VNET is publicly available from above URL

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