Explicit Control in a Batch-aware Distributed File System John Bent
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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 Astronomers develop distributed virtual supercomputers! computing! Grid computing Home storage 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 Batch computing Compute node Compute node Compute node CPU CPU CPU CPU Manager Manager Manager Manager Internet Home storage Compute node 1 2 3 4 Job queue 1 2 Scheduler 3 4 Batch workloads “Pipeline and Batch Sharing in Grid Workloads,” Douglas Thain, John Bent, Andrea Arpaci-Dusseau, Remzi ArpaciDussea, Miron Livny. HPDC 12, 2003. • 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 Batch workloads Endpoint Endpoint Endpoint Endpoint Batch dataset Pipeline Endpoint Pipeline Pipeline Pipeline Pipeline Pipeline Pipeline Pipeline Endpoint Endpoint Endpoint Endpoint Batch dataset Cluster-to-cluster (c2c) • Not quite p2p • • • • More organized Less hostile More homogeneity Correlated failures Internet Home store • 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? Why not DFS? Internet Home store • Distributed file system would be ideal • Easy to use • Uniform name space • Designed for wide-area networks • But . . . • Not practical • Embedded decisions are wrong 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 Practical and deployable • User-level; requires no privilege • Packaged as a modified batch system SGE SGE SGE SGE BADFS Internet SGE SGE SGE SGE Home store • A new batch system which includes BAD-FS • General; will work on all batch systems • Tested thus far on multiple batch systems Contributions of BAD-FS Compute node Compute node Compute node Compute node CPU CPU CPU CPU Manager Manager Manager Manager Storage Manager BAD-FS Storage Manager BAD-FS Storage Manager BAD-FS Storage Manager 1) Storage managers 2) Batch-Aware Distributed File System 3) Expanded job description language 1 2 Home storage 3 4 Job queue 4) BAD-FS scheduler BAD-FS Scheduler Scheduler 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 Compute node Compute node Extract endpoint AMANDA: 200 MB pipeline 500 MB batch 5 MB endpoint BAD-FS Scheduler 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 Capacity-aware scheduling Endpoint Endpoint Endpoint Endpoint Pipeline Endpoint Pipeline Endpoint Pipeline Endpoint Pipeline Endpoint Pipeline Pipeline Pipeline Pipeline Pipeline Pipeline Pipeline Batch dataset Batch dataset Batch dataset Pipeline Batch dataset Endpoint Endpoint Endpoint Endpoint Capacity-aware scheduling • 64 batch-intensive synthetic pipelines • Vary size of batch data • 16 compute nodes 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 gammaray 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 resources 2. Shared disk 3. 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.
