Explicit Control in a Batch-aware Distributed File System
John Bent Douglas Thain
Andrea Arpaci-Dusseau Remzi Arpaci-Dusseau
Miron LivnyUniversity of Wisconsin, Madison
Grid computing
Physicists invent distributed computing!Astronomers develop virtual supercomputers!
Grid computing
Home storage
Internet
If it looks like a duck . . .
Are existing distributed file systems adequate for batch computing
workloads?• NO. Internal decisions inappropriate
• Caching, consistency, replication
• A solution: Batch-Aware Distributed File System (BAD-FS)• Combines knowledge with external storage control
• Detail information about workload is known• Storage layer allows external control• External scheduler makes informed storage decisions
• Combining information and control results in• Improved performance• More robust failure handling• Simplified implementation
Outline
• Introduction• Batch computing
• Systems• Workloads• Environment• Why not DFS?
• Our answer: BAD-FS• Design• Experimental evaluation
• Conclusion
Batch computing• Not interactive computing• Job description languages• Users submit• System itself executes• Many different batch systems
• Condor• LSF• PBS• Sun Grid Engine
Internet
Batch computing
Scheduler
Compute node
CPUManager
Compute node
CPUManager
Compute node
CPUManager
Compute node
CPUManager
Job queue
1 2
3 4Home storage
1 2
3 4
Batch workloads
• General properties• Large number of processes• Process and data dependencies• I/O intensive
• Different types of I/O• Endpoint• Batch• Pipeline
• Our focus: Scientific workloads• More generally applicable• Many others use batch computing
• video production, data mining, electronic design, financial services, graphic rendering
““Pipeline and Batch Sharing in Grid Pipeline and Batch Sharing in Grid Workloads,”Workloads,” Douglas Thain, John Bent, Douglas Thain, John Bent, Andrea Arpaci-Dusseau, Remzi Arpaci-Andrea Arpaci-Dusseau, Remzi Arpaci-Dussea, Miron Livny. HPDC 12, 2003.Dussea, Miron Livny. HPDC 12, 2003.
Batch workloads
Endpoint
Endpoint
EndpointBatch
dataset
Batch dataset
Pipeline
Pip
elin
e
Endpoint Endpoint
EndpointEndpointEndpointEndpoint
Pipeline Pipeline
Pipeline Pipeline Pipeline
PipelinePipeline
Cluster-to-cluster (c2c)
• Not quite p2p• More organized• Less hostile• More homogeneity• Correlated failures
• Each cluster is autonomous• Run and managed by different entities
• An obvious bottleneck is wide-area
How to manage flow of data into, within and out of these clusters?
InternetHomestore
Why not DFS?
• Distributed file system would be ideal• Easy to use• Uniform name space• Designed for wide-area networks
• But . . . • Not practical• Embedded decisions are wrong
InternetHomestore
DFS’s make bad decisions
• Caching • Must guess what and how to cache
• Consistency • Output: Must guess when to commit• Input: Needs mechanism to invalidate cache
• Replication • Must guess what to replicate
BAD-FS makes good decisions
• Removes the guesswork• Scheduler has detailed workload knowledge• Storage layer allows external control• Scheduler makes informed storage decisions
• Retains simplicity and elegance of DFS• Practical and deployable
Outline
• Introduction• Batch computing
• Systems• Workloads• Environment• Why not DFS?
• Our answer: BAD-FS• Design• Experimental evaluation
• Conclusion
• User-level; requires no privilege • Packaged as a modified batch system
• A new batch system which includes BAD-FS• General; will work on all batch systems• Tested thus far on multiple batch systems
Practical and deployable
Internet
SGE SGE
SGE SGE SGE
SGE SGE
SGEBAD-
FSBAD-
FSBAD-
FSBAD-
FSBAD-
FSBAD-
FSBAD-
FSBAD-
FS
Homestore
Contributions of BAD-FS
Scheduler
Compute node
CPUManager
Compute node
CPUManager
Compute node
CPUManager
Compute node
CPUManager
Job queue
1 2
3 4Home storage
Job queue
3) Expanded job description language
BAD-FSScheduler
4) BAD-FS scheduler
1) Storage managers
2) Batch-Aware Distributed File System
StorageManager
StorageManager
StorageManager
StorageManager BAD-FS BAD-FS BAD-FS
BAD-FS knowledge
• Remote cluster knowledge• Storage availability• Failure rates
• Workload knowledge• Data type (batch, pipeline, or endpoint)• Data quantity• Job dependencies
Control through volumes
• Guaranteed storage allocations• Containers for job I/O
• Scheduler• Creates volumes to cache input data
• Subsequent jobs can reuse this data
• Creates volumes to buffer output data• Destroys pipeline, copies endpoint
• Configures workload to access containers
Knowledge plus control
• Enhanced performance• I/O scoping• Capacity-aware scheduling
• Improved failure handling• Cost-benefit replication
• Simplified implementation• No cache consistency protocol
I/O scoping• Technique to minimize wide-area traffic• Allocate storage to cache batch data• Allocate storage for pipeline and endpoint• Extract endpoint
AMANDA:200 MB pipeline500 MB batch 5 MB endpoint
BAD-FSScheduler
Compute node Compute node
Internet Steady-state:Only 5 of 705 MB traverse wide-area.
Capacity-aware scheduling
• Technique to avoid over-allocations• Scheduler runs only as many jobs as fit
Endpoint
Endpoint
Batch dataset
Batch dataset
Pipeline
Pipeline
Pipeline
Endpoint
Endpoint
EndpointPipeline
Pipeline
Pipeline
Endpoint
Endpoint
EndpointPipeline
Pipeline
Pipeline
Endpoint
Endpoint
EndpointPipeline
Pipeline
Pipeline
Endpoint
Batch dataset
Batch dataset
Capacity-aware scheduling
• 64 batch-intensive synthetic pipelines• Vary size of batch data
• 16 compute nodes
Capacity-aware scheduling
Improved failure handling
• Scheduler understands data semantics• Data is not just a collection of bytes• Losing data is not catastrophic
• Output can be regenerated by rerunning jobs
• Cost-benefit replication• Replicates only data whose replication cost is
cheaper than cost to rerun the job
• Results in paper
Simplified implementation
• Data dependencies known• Scheduler ensures proper ordering
• No need for cache consistency protocol in cooperative cache
Real workloads
• AMANDA• Astrophysics study of cosmic events such as gamma-ray
bursts• BLAST
• Biology search for proteins within a genome• CMS
• Physics simulation of large particle colliders• HF
• Chemistry study of non-relativistic interactions between atomic nuclei and electors
• IBIS• Ecology global-scale simulation of earth’s climate used to
study effects of human activity (e.g. global warming)
Real workload experience
• Setup• 16 jobs• 16 compute nodes• Emulated wide-area
• Configuration• Remote I/O• AFS-like with /tmp• BAD-FS
• Result is order of magnitude improvement
BAD Conclusions• Existing DFS’s insufficient• Schedulers have workload knowledge• Schedulers need storage control
• Caching• Consistency• Replication
• Combining this control with knowledge• Enhanced performance• Improved failure handling• Simplified implementation
For more information
• http://www.cs.wisc.edu/adsl• http://www.cs.wisc.edu/condor
• Questions?
Why not BAD-scheduler and traditional DFS?
• Cooperative caching• Data sharing
• Traditional DFS• assume sharing is exception• provision for arbitrary, unplanned sharing
• Batch workloads, sharing is rule• Sharing behavior is completely known
• Data committal• Traditional DFS must guess when to commit
• AFS uses close, NFS uses 30 seconds• Batch workloads precisely define when
Is cap aware imp in real world?1. Heterogeneity of remote resources2. Shared disk3. Workloads changing, some are very,
very large.
Capacity-aware scheduling
• Goal• Avoid overallocations
• Cache thrashing• Write failures
• Method• Breadth-first• Depth-first• Idleness
Capacity-aware scheduling evaluation• Workload
• 64 synthetic pipelines• Varied pipe size
• Environment• 16 compute nodes
• Configuration• Breadth-first• Depth-first• BAD-FS
Failures directly correlate to workload throughput.
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