Snowbird:Interactive Resource-Intensive

Applications Made Easy

H. Andrés Lagar-Cavilla*

Niraj Tolia† ‡, Eyal de Lara*,

M. Satyanarayanan‡ & Dave O’Hallaron‡

*University of Toronto, †HP Labs,‡Carnegie Mellon University

Middleware, November 2007

Bimodal Applications

• Interactive– Cognitive phase

• Resource-Intensive– Crunch phase

• Digital Animation• Scientific Computing• Engineering Design• Bio/Pharma• Video Editing• ….

Maya

(digital animation)

Dichotomy• Crunch (computation)

– Short completion time

– Remote computing resources

• Cognitive (interaction)– Crisp interactive performance

– User attention

Execution Alternatives– Thick Clients (Desktop PCs)

Cognitive: excellent interactive performance

× Crunch: resource constrained

– Thin Clients (VNC, Remote Desktop) Crunch: use of remote resources (CPU, Data)

× Cognitive: latency and jitter impact interactions

– Custom Applications Pipeline: placement over different nodes

× Requires significant developer resources

Execution Alternatives

Cognitive: excellent interactive performance

Crunch: use of remote resources (CPU, Data)

Internet

ImprovedCompute Power

Compute Cluster

Snowbird: Agent Abstraction

Applications encapsulated within an agent– Agent: processes, libraries, IPC, OS, config data…– Migration: performance goals achieved by morphing

• a thin client for resource intensive crunch phases• a thick client for highly interactive cognitive phases

ImprovedData Access

User’s Desktop Data Repository

ImprovedInteractivity

Agent

Bimodal Applications Made Easy

• Develop apps as monolithic blocks– Don’t worry about what executes where– Agent is migrated to satisfy each phase

• Seamless and transparent behavior

• Legacy support– Different OS’s (and versions/features)– Different languages (Fortran!)– No need for recompilation, relinking, etc…– Closed source apps just work

Implementation

Design Criteria• VM-based migration

– x86 interface most widely deployed– Transparently support OS, lang, etc…

• Internet scale– WAN migration– Long fat pipes: 50 Mbp/s…, 50-200 ms RTT

• Use of graphics HW acceleration a must– For the cognitive mode of bimodal apps

• Server-less design (P2P)– All hosts symmetric– Can execute anywhere

Components

• VMM: suspend, resume, live migration – Xen 3.0.1

• Interaction-aware migration manager– Transparent

• Support for 3D acceleration in VMs– Vital for crisp interaction

• Virtual disk that maximizes locality– WAN area migration

Agent Profiles: Migration Manager

• FSM that models an agent’s behavior• Provided by expert users, admins, or developers

– Default system-wide profile available

• Only deployment additional effort

CPUIntensive

cycles.org

NetIntensive

data.edu

InteractionIntensiveusr.home

Net > 4 Mbit/s

CPU > 95%

FPS < 20 &&

Input > 15

Internet

ImprovedCompute Power

Compute Cluster

ImprovedData Access

User’s Desktop Data Repository

ImprovedInteractivity

Agent

What’s this?

Interaction-aware

• Novelty in our approach

• Measure the quality of the interactive response

• We use frames per seconds– More in sync with operations in bimodal apps– Stretch object, rotate, zoom, etc…– Pure latency not enough

• Ample space for future work

Frames Per Second

• Non work-conserving (VNC): FPS = 2/latency

• Local: FPS = n/latency

• Work-conserving (X): FPS = n/(latency*k)

The Rest

• VMGL: support for 3D acceleration in VMs– Coming live to this conference in 5 mins– Follow up: VEE 2007, >4K downloads

• WANDisk: virtual disk maximizes locality– Minimizes WAN communication– Simplifies state synchronization

• More details in paper

Evaluation

Benchmarks

Maya (closed source)ADF (closed source)

QuakeViz Kmenc15

• Broad set of domains

– Scientific Computing, Bio, Video Editing, Animation

• Closed and open source

• Straightforward installation

• Able to use generic profile on all four

• Partitioned mode for comparison

– ADF & Maya

Methodology• Crunch + cognitive benchmarks• Performing “crunch” experiments is easy• Replaying long interactive traces is not

– Can’t expect a user to do it– Must be able to compare results

• VNC-Redux: record and replay interactive user sessions– Record input and screen state – This is matched during replay for accuracy

ComputeServer

StorageServer User Desktop

LANEmulated

WAN

Experimental Setup

• Thick: No virtualization, on User Desktop– User Desktop: UP with graphics acceleration

• Thin: No virtualization, on Compute Server– Compute Server: 4-way SMP– 100 Mbit/s WAN, RTT: 33, 66, and 100 ms

• Partitioned– App-specific developer-brewed: Maya & ADF

• Snowbird– Agents are initially launched on User Desktop

– Xen+Migration Manager+VMGL+WANDisk

Results: Crunch Phase

45

45

67

21

96

10

7

12

5

434

8

58

76

24

45

0

20

40

60

80

100

120

140

Maya QuakeViz ADF Kmenc15

Min

ute

s

ThinThickSnowbird 66Partitioned

Snowbird’s crunch performance– Much better than thick – Comparable to thin/partitioned

370…Ouch!

• Better interactivity than thin clients• Is Snowbird any worse than a thick client?• > 20 FPS is ok, < 8 FPS is unusable

Results: Cognitive Phase

Take Home Messages

• Bimodal applications – What they are and why they matter

• Thin clients are not almighty– There is no replacement for local interaction

• Best of both worlds: thick and thin clients– Necessary in an Internet world with remote computing

resources

• VM-based app migration is feasible– And with many advantages

• Future trends align well…

Futurism

• More bandwidth: cheaper VM migration

• What about latency?– The earth is not shrinking– Speed of light is not increasing– More routers, overlays, firewalls– Toronto-London UK: ~109ms– Toronto-LA: ~84ms

• Insurmountable obstacle for thin clients

Questions?

Thanks

H. Andrés Lagar-Cavillaandreslc@cs.toronto.edu

Niraj Tolia, Eyal de Lara, Satya & Dave O’HallaronU of Toronto, HP Labs, Carnegie Mellon

Backup

VMGL: 3D Acceleration in VMs

• OpenGL virtualization

• Hardware specs closed, unavailable

• Focus instead on software standards– OpenGL -> cross-platform– Direct3D -> MS-only

• Intercept GL calls and forward them to the host. Proprietary driver renders there.

• More details: VEE 2007

Open GL for X11 Apps

VMGL for X11 Apps

Admin VM

LocalChunk Store

WANDisk Manager

Kernel Module

WANDisk: Virtual Disk

foo.toronto.edu

Admin VM

LocalChunk Store

WANDisk Manager

Kernel Module

bar.cmu.edu

ChunkTable

ChunkMisses

Agent

Block DevOS

Application

baz.europe.org

Admin VM

LocalChunk Store

WANDisk Manager

Kernel Module

Why Another Storage System?• Exploits Snowbird characteristics

• P2P model– No server interposition– Single-writer: simple metadata, no locks

• Minimizes WAN talk– Locality: persistent replicas– Differential transfers: rsync– On-demand fetching

Experimental Setup

• Thick: No virtualization, on User Desktop– User Desktop: UP with graphics acceleration

• Thin: No virtualization, on Compute Server– Compute Server: 4-way SMP– 100 Mbit/s WAN, RTT: 33, 66, and 100 ms

• Partitioned– App-specific developer-brewed: Maya & ADF

• Snowbird– Agents are initially launched on User Desktop

ADF Migration Time (secs)

Latency (ms) Detection Migration Pause

33 12.5 62 4.9

66 11.5 62 6.2

100 13.1 64.9 6.7

Applicability of Snowbird

• Morphing time: our implementation• Speedup: application & resources• C: Crunch phase time locally

Snowbird Limitations

• Parallelism up to SMP level– What about cluster-scale?

• SSE, 3DNow!– i.e. x86 is not that uniform

• Overlapping phases– Hysteresis, priority in migration manager

• Very short phases– Cost/benefit analysis

Our Current Interests

• VM support for large parallel tasks– Relevant to commodity computing– Migration, the cloud, etc…

• How to measure interactive performance– Thin clients, desktop consolidation, VMs– Unknown effects for modern (3D-heavy) GUIs

Really Backup Backup

Keywords Of This Presentation

• Thin clients– Remote execution

• Interactive Performance– Thick clients

• Virtual machine migration– Application migration

• Bimodal applications – What they are and why they matter

Frames Per Second

• Non work-conserving: same latency, less frames

• Local: FPS = frames/latency

• Work-conserving: same frames, more latency

What We Need

• System support for bimodal applications

• Combine best of both worlds– Thick client, local execution

• Interaction

– Thin client, remote execution• Computation

• Make development easy

Results: Crunch Phase

0

20

40

60

80

100

120

140

Maya QuakeViz ADF Kmenc15

Application

Tim

e (

Min

ute

s)

ThinThickSnowbird 33Snowbird 66Snowbird 100

Snowbird’s crunch performance– Much better than thick – Comparable to thin/partitioned

Talk Pointers

• Be more explicit demo: thin, frames

• Proxy FPS & input in migr manager