Explicit Control in a Batch-aware Distributed File System John Bent Douglas Thain Andrea Arpaci-Dusseau Remzi Arpaci-Dusseau Miron Livny University 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 CPU Manager Compute node CPU Manager Compute node CPU Manager Compute node CPU Manager Job queue Home storage 12 34

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 Workloads,” Douglas Thain, John Bent, Andrea Arpaci-Dusseau, Remzi Arpaci- Dussea, Miron Livny. HPDC 12, 2003.

Batch workloads Endpoint Batch dataset Pipeline Endpoint Pipeline

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? Internet Home store

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 Internet Home store

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 BAD- FS BAD- FS BAD- FS BAD- FS BAD- FS BAD- FS BAD- FS BAD- FS Home store

Contributions of BAD-FS Scheduler Compute node CPU Manager Compute node CPU Manager Compute node CPU Manager Compute node CPU Manager Job queue Home storage Job queue 3) Expanded job description language BAD-FS Scheduler 4) BAD-FS scheduler 1) Storage managers 2) Batch-Aware Distributed File System Storage Manager Storage Manager Storage Manager Storage Manager 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 pipeline 500 MB batch 5 MB endpoint BAD-FS Scheduler 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 Batch dataset Pipeline Endpoint Pipeline Endpoint Pipeline Endpoint Pipeline Endpoint 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 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 resources 2.Shared disk 3.Workloads changing, some are very, very large.

User burden Additional info needed in declarative lang. User probably already knows this info Or can readily obtain Typically, this info already exists Scattered across collection of scripts, Makefiles, etc. BAD-FS improves current situation by collecting this info into one central location

Enhanced performance I/O scoping Scheduler knows I/O types Creates storage volumes accordingly Only endpoint I/O traverses wide-area Capacity-aware scheduling Scheduler knows I/O quantities Throttles workloads, avoids over-allocations

Improved failure handling Scheduler understands data semantics Lost data is not catastrophic Pipe data can be regenerated Batch data can be refetched Enables cost-benefit replication Measure replication cost data generation cost failure rate Replicate only data whose replication cost is cheaper than expected cost to reproduce Improves workload throughput

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.

Workload example: AMANDA Astrophysics study of cosmic events such as gamma-ray bursts Four stage pipeline 200 MB pipeline I/O 500 MB batch I/O 5 MB endpoint I/O Focus Scientific workloads Many others use batch computing video production, data mining, electronic design, financial services, graphic rendering

BAD-FS and scheduler BAD-FS Allows external decisions via volumes A guaranteed storage allocation Size, lifetime, and a type Cache volumes Read-only view of an external server Can be bound together into cooperative cache Scratch volumes Private read-write name space Batch-aware scheduler Rendezvous of control and information Understands storage needs and availability Controls storage decisions

Scheduler controls storage decisions What and how to cache? Answer: batch data and cooperatively Technique: I/O scoping and capacity-aware scheduling What and when to commit? Answer: endpoint data when ready Technique: I/O scoping and capacity-aware scheduling What and when to replicate? Answer: data whose cost to regenerate is high Technique: cost-benefit replication

I/O scoping Goal Minimize wide-area traffic Means Information about data type Storage volumes Method Create coop cache volumes for batch data Create scratch volumes to contain pipe Result Only endpoint data traverses wide-area Improved workload throughput

I/O scoping evaluation Workload 64 synthetic pipelines 100 MB of I/O each Varied data mix Environment 32 compute nodes Emulated wide-area Configuration Remote I/O Cache volumes Scratch volumes BAD-FS Wide-area traffic directly correlates to workload throughput.

Capacity-aware scheduling Goal Avoid over-allocations of storage Means Information about data quantities Information about storage availability Storage volumes Method Use depth-first scheduling to free pipe volumes User breadth-first scheduling to free batch Result No thrashing due to over-allocations of batch No failures due to over-allocations of pipe Improved throughput

Capacity-aware scheduling evaluation Workload 64 synthetic pipelines Pipe-intensive Environment 16 compute nodes Configuration Breadth-first Depth-first BAD-FS

Capacity-aware scheduling evaluation Workload 64 synthetic pipelines Pipe-intensive Environment 16 compute nodes Configuration Breadth-first Depth-first BAD-FS Failures directly correlate to workload throughput.

Cost-benefit replication Goal Avoid wasted replication overhead Means Knowledge of data semantics Data loss is not catastrophic Can be regenerated or refetched Method Measure Failure rate, f, within each cluster Cost, p, to reproduce data − Time to rerun jobs to regenerate pipe data − Time to refetch batch data from home Cost, r, to replicate data Replicate only when p*f > r Result Data is replicated only when it should be Can improve throughput

Cost-benefit replication evaluation Workload Synthetic pipelines of depth 3 Runtime 60 seconds Environment Artificially injected failures Configuration Always-copy Never-copy BAD-FS Trade-off overhead in environment without failure to gain throughput in environment with failure.

Real workloads Workload Real workloads 64 pipelines Environment 16 compute nodes Emulated wide-area Cold and warm First 16 are cold Subsequent 48 warm Configuration Remote I/O AFS-like BAD-FS

Experimental results not shown here I/O scoping Capacity planning Cost-benefit replication Other real workload results Large in the wild demonstration Works in c2c Works across multiple batch systems

Existing approaches Remote I/O Interpose and redirect all I/O home CON: Quickly saturates wide-area connection Pre-staging Manually push all input endpoint and batch Manually pull all endpoint output Manually configure workload to find pre-staged data CON: Repetitive, error-prone, laborious Traditional distributed file systems Locate remote compute nodes within same name space as home (e.g. AFS) Not an existing approach; impractical to deploy

Declarative language Existing languages express process specification requirements dependencies Add primitives to describe I/O behavior Modified language can express data dependencies type (i.e. endpoint, batch, pipe) quantities

Example: AMANDA on AFS Caching Batch data redundantly fetched Callback overhead Consistency Pipeline data committed on close Replication No idea which data is important AMANDA: 200 MB pipeline I/O 500 MB batch I/O 5 MB endpoint I/O This is slide in which I’m most interested in feedback. 500 MB 200 MB

Overview Practical Ease of use CachingConsistencyReplication Remote I/O √√ X √ X Pre-staging √ X √√ X Trad. DFS X √ XXX

I/O Scoping

Capacity-aware scheduling, batch-intense

Capacity-aware scheduling evaluation Workload 64 synthetic pipelines Pipe-intensive Environment 16 compute nodes

Failure handling

Workload experience

In the wild

Example workflow language: Condor DAGman Keyword job names file w/ execute instrs Keywords parent, child express relations … no declaration of data job A “instructions.A” job B “instructions.B” job C “instructions.C” job D “instructions.D” parent A child B parent C child D A B C D

Adding data primitives to a workflow language New keywords for container operations volume: create a container scratch: specify container type mount: how the app addresses the container extract: the desired endpoint output User must provide complete, exact I/O information to the scheduler Specify which procs use which data Specify size of data read and written

Extended workflow language job A “instructions.A” job B “instructions.B” job C “instructions.C” job D “instructions.D” parent A child B parent C child D volume B1 ftp://home/data 1GB volume P1 scratch 500 MB volume P2 scratch 500 MB A mount B1 /data C mount B1 /data A mount P1 /tmp B mount P1 /tmp C mount P2 /tmp D mount P2 /tmp extract P1/out ftp://home/out.1 extract P2/out ftp://home/out.2 out A B C D ftp://home /data out.1out.2 B1 out P2P1

Terminology Application Process Workload Pipeline I/O Batch I/O Endpoint I/O Pipe-depth Batch-width Scheduler Home storage Catalogue

Remote resources

Example scenario Workload Width 100, depth 2 1 GB batch 1 GB pipe 1 KB endpoint Environment Batch data archived at home Remote compute cluster available 1 KB 1 GB 1 KB 1 GB 1 KB 1 GB Home store Compute cluster

Ideal utilization of remote storage Minimize wide-area traffic by scoping I/O Transfer batch data once and cache Contain pipe data within compute cluster Only endpoint data should traverse wide-area Improve throughput through space mgmt Avoid thrashing due to excessive batch Avoid failure due to excessive pipe Cost-benefit checkpointing and replication Track data generation and replication costs Measure failure rates Use cost-benefit checkpointing algorithm Apply independent policy for each pipeline

Remote I/O Simplest conceptually Requires least amount of remote privilege But... Batch data fetched redundantly Pipe I/O unnecessarily crosses wide-area Wide-area bottleneck quickly saturates

Pre-staging Requires large user burden Needs access to local file sys for each cluster Manually pushes batch data May configures workload to use /tmp Must manually pulls endpoint outputs Good performance through I/O scoping but Tedious, repetitive, mistake-prone Availability of /tmp can’t be guaranteed Scheduler lacks knowledge to checkpoint