Snowbird: Interactive Resource-Intensive Applications Made Easy H. Andrés Lagar-Cavilla * Niraj...
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Transcript of Snowbird: Interactive Resource-Intensive Applications Made Easy H. Andrés Lagar-Cavilla * Niraj...
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 [email protected]
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