Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish...

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Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer Science Northwestern University http:// virtuoso.cs.northwestern.edu
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Page 1: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

Dynamic Topology Adaptation of Virtual Networks of Virtual Machines

Ananth I. Sundararaj

Ashish Gupta

Peter A. Dinda

Prescience Lab

Department of Computer Science

Northwestern University

http://virtuoso.cs.northwestern.edu

Page 2: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Summary

• Dynamically adapt applications in virtual environments to available resources

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

• Show that its benefits can be significant for the case of BSP applications

• Studying the extent of applications for which our approach is effective

Page 3: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Outline• Virtual machine grid computing

• Virtuoso system

• Networking challenges in Virtuoso

• Enter VNET

• VNET, VTTIF Adaptive virtual network

• Experiments

• Current Status

• Conclusions

Page 4: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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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: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Virtual Machines

Virtual machine monitors (VMMs)

•Raw machine is the abstraction

•VM represented by a single image

•VMware GSX Server

Page 6: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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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: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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User’s View in Virtuoso Model

User

User’s LAN

VM

Page 8: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Outline• Virtual machine grid computing

• Virtuoso system

• Networking challenges in Virtuoso

• Enter VNET

• VNET, VTTIF Adaptive virtual network

• Experiments

• Current Status

• Conclusions

Page 10: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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User’s friendlyLAN

Foreign hostile LAN

Virtual Machine

VNET: A bridge with long wires

Host

Proxy

X

Why VNET? A Scenario VM 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 12: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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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

“eth0”

VNET

ethy

IP Network

VM“eth0”

vmnet0

A VNET Link

Page 13: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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VTTIF

• Topology inference and traffic characterization for applications

• Ethernet-level traffic monitoring

• VNET daemons collectively aggregate a global traffic matrix for all VMs

• Application topology is recovered using normalization and pruning algorithms

Page 14: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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VTTIF Operation

Synced Parallel Traffic Monitoring

Traffic Filtering and Matrix Generation

Matrix Analysis and Topology Characterization

Page 15: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Dynamic Topology Inference

1. Fast updates

Smoothed Traffic Matrix 2. Low Pass Filter

Aggregation

3. Threshold change detectionTopology change output

VNET Daemons VNET

Daemons

VTTIF parameters•Update rate•Smoothing interval•Detection threshold

Page 16: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Rep

orte

d C

hang

e R

ate

(Hz)

Topology Change Rate (Hz)

Knee of Curve Depends

On VTTIF Threshold

and Smoothing Parameters

Reaction time of VTTIF

Page 17: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Outline• Virtual machine grid computing

• Virtuoso system

• Networking challenges in Virtuoso

• Enter VNET

• VNET, VTTIF Adaptive virtual network

• Experiments

• Current Status

• Conclusions

Page 18: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Adaptation

Virtuoso presents tremendous opportunities and challenges

Adapt to available resources

ChallengesNetwork and host monitoring

Monitor application

Infer goals of application

Adequacy of available mechanisms

Challenges interrelated

To determine subset of applications for which such adaptation succeeds

We demonstrate that the subset is not empty

Page 19: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Experiments

• Focus on a specific instance– Application : Patterns, a synthetic benchmark– Monitoring : Application topology inferred by VTTIF– Aim : Minimize running time of patterns– Mechanism : Add links and corresponding

forwarding rules to VNET topology

Performance of BSP applications significantly enhanced by adapting VNET topology, guided by topology inferred by VTTIF

Page 21: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Evaluation

• Reaction time of VNET

• Benefits of adaptation (performance speedup)– Eight VMs on a single cluster, all-all

topology– Eight VMs spread over two clusters over

MAN, bus topology– Eight VMs spread over WAN, all-all

topology

Page 22: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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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 23: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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idea

lco

mpl

ete

star 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

0

200

400

600

800

1000

1200

1400

1600

1800

Run

Tim

e (S

econ

ds)

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 24: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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idea

l

com

ple

te

sta

r 1 2 3 4 5 6 70

100

200

300

400

500

600

700

800

900

Run

Tim

e (

Se

con

ds)

Number of Fast Path Links in Virtual Topology

No Fast Path Topology

Full bus network after startupmeasurement + reconfiguration cost

Full bus from beginning of run

Dynamic measurement andreconfiguration

•Patterns has a bus topology

•Eight VMs are used

•VMs spread over two clusters over a MAN

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

Page 25: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

25idea

lco

mpl

ete

star 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

0

200

400

600

800

1000

1200

1400

1600

1800

Run

Tim

e (S

eco

nds)

Number of Fast Path Links in Virtual Topology

No Fast Path Topology

Full all-to-all network after startupmeasurement + 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 26: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Outline• Virtual machine grid computing

• Virtuoso system

• Networking challenges in Virtuoso

• Enter VNET

• VNET, VTTIF Adaptive virtual network

• Experiments

• Current Status

• Conclusions

Page 27: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Current Status

• Applications: Transactional web ecommerce application

• Mechanisms: VM migration

Page 28: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Conclusions

• Demonstrated the feasibility of adaptation at the level of collection of VMs connected by VNET

• Showed that its benefits can be significant for the case of BSP applications

• Studying the extent of applications for which our approach is effective

• Moving ahead to use other adaptation mechanisms

Page 29: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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• For More Information– 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• http://virtuoso.cs.northwestern.edu

Page 30: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Isn’t It Going to Be Too Slow?Application Resource ExecTime

(10^3 s)

Overhead

SpecHPC Seismic

(serial, medium)

Physical 16.4 N/A

VM, local 16.6 1.2%

VM, Grid virtual FS

16.8 2.0%

SpecHPC

Climate

(serial, medium)

Physical 9.31 N/A

VM, local 9.68 4.0%

VM, Grid virtual FS

9.70 4.2%

Experimental setup: physical: dual Pentium III 933MHz, 512MB memory, RedHat 7.1,30GB disk; virtual: Vmware Workstation 3.0a, 128MB memory, 2GB virtual disk, RedHat 2.0NFS-based grid virtual file system between UFL (client) and NWU (server)

Small relativevirtualizationoverhead;compute-intensive

Relativeoverheads < 5%

Page 31: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Isn’t It Going To Be Too Slow?

0

0.5

1

1.5

2

2.5

3

0

0.5

1

1.5

2

2.5

3

No Load Light Load Heavy Load

Tasks onPhysicalMachine

Tasks onVirtual

Machine

Tasks onPhysicalMachine

Tasks onVirtual

Machine

Tasks onPhysicalMachine

Tasks onVirtual

Machine

Synthetic benchmark: exponentially arrivals of compute bound tasks, background load provided by playback of traces from PSC

Relative overheads < 10%

Page 32: Dynamic Topology Adaptation of Virtual Networks of Virtual Machines Ananth I. Sundararaj Ashish Gupta Peter A. Dinda Prescience Lab Department of Computer.

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Isn’t It Going To Be Too Slow?

• Virtualized NICs have very similar bandwidth, slightly higher latencies

– J. Sugerman, G. Venkitachalam, B-H Lim, “Virtualizing I/O Devices on VMware Workstation’s Hosted Virtual Machine Monitor”, USENIX 2001

• Disk-intensive workloads (kernel build, web service): 30% slowdown– S. King, G. Dunlap, P. Chen, “OS support for Virtual Machines”,

USENIX 2003

However: May not scale with faster NIC or disk