OSG Storage Architectures Tuesday Afternoon Brian Bockelman, OSG Staff University of Nebraska-Lincoln.

Slides:



Advertisements
Similar presentations
Jens G Jensen Atlas Petabyte store Supporting Multiple Interfaces to Mass Storage Providing Tape and Mass Storage to Diverse Scientific Communities.
Advertisements

Categories of I/O Devices
The EPIKH Project (Exchange Programme to advance e-Infrastructure Know-How) gLite Grid Services Abderrahman El Kharrim
Oxford Jan 2005 RAL Computing 1 RAL Computing Implementing the computing model: SAM and the Grid Nick West.
Take An Internal Look at Hadoop Hairong Kuang Grid Team, Yahoo! Inc
Chapter 3.1:Operating Systems Concepts 1. A Computer Model An operating system has to deal with the fact that a computer is made up of a CPU, random access.
OSG End User Tools Overview OSG Grid school – March 19, 2009 Marco Mambelli - University of Chicago A brief summary about the system.
CERN - IT Department CH-1211 Genève 23 Switzerland t Monitoring the ATLAS Distributed Data Management System Ricardo Rocha (CERN) on behalf.
Distributed Storage Wednesday morning, 9:00am Derek Weitzel OSG Graduate Research Assistant University of Nebraska – Lincoln.
Connecting OurGrid & GridSAM A Short Overview. Content Goals OurGrid: architecture overview OurGrid: short overview GridSAM: short overview GridSAM: example.
OSG Public Storage and iRODS
US ATLAS Western Tier 2 Status and Plan Wei Yang ATLAS Physics Analysis Retreat SLAC March 5, 2007.
SRM at Clemson Michael Fenn. What is a Storage Element? Provides grid-accessible storage space. Is accessible to applications running on OSG through either.
Distributed File Systems
OSG Site Provide one or more of the following capabilities: – access to local computational resources using a batch queue – interactive access to local.
Introduction to Hadoop and HDFS
f ACT s  Data intensive applications with Petabytes of data  Web pages billion web pages x 20KB = 400+ terabytes  One computer can read
StoRM Some basics and a comparison with DPM Wahid Bhimji University of Edinburgh GridPP Storage Workshop 31-Mar-101Wahid Bhimji – StoRM.
Scalable Web Server on Heterogeneous Cluster CHEN Ge.
A. Sim, CRD, L B N L 1 OSG Applications Workshop 6/1/2005 OSG SRM/DRM Readiness and Plan Alex Sim / Jorge Rodriguez Scientific Data Management Group Computational.
Distributed Storage Tuesday afternoon Horst Severini University of Oklahoma Slides courtesy of Derek Weitzel University.
Large Scale Test of a storage solution based on an Industry Standard Michael Ernst Brookhaven National Laboratory ADC Retreat Naples, Italy February 2,
Introduction to dCache Zhenping (Jane) Liu ATLAS Computing Facility, Physics Department Brookhaven National Lab 09/12 – 09/13, 2005 USATLAS Tier-1 & Tier-2.
Enabling Grids for E-sciencE EGEE-III INFSO-RI Using DIANE for astrophysics applications Ladislav Hluchy, Viet Tran Institute of Informatics Slovak.
UMD TIER-3 EXPERIENCES Malina Kirn October 23, 2008 UMD T3 experiences 1.
Architecture and ATLAS Western Tier 2 Wei Yang ATLAS Western Tier 2 User Forum meeting SLAC April
Enabling Grids for E-sciencE Introduction Data Management Jan Just Keijser Nikhef Grid Tutorial, November 2008.
US LHC OSG Technology Roadmap May 4-5th, 2005 Welcome. Thank you to Deirdre for the arrangements.
1 Andrea Sciabà CERN Critical Services and Monitoring - CMS Andrea Sciabà WLCG Service Reliability Workshop 26 – 30 November, 2007.
6/23/2005 R. GARDNER OSG Baseline Services 1 OSG Baseline Services In my talk I’d like to discuss two questions:  What capabilities are we aiming for.
Jens G Jensen RAL, EDG WP5 Storage Element Overview DataGrid Project Conference Heidelberg, 26 Sep-01 Oct 2003.
SLACFederated Storage Workshop Summary For pre-GDB (Data Access) Meeting 5/13/14 Andrew Hanushevsky SLAC National Accelerator Laboratory.
Distributed Storage Rob Quick Indiana University Slides courtesy of Derek Weitzel University of Nebraska – Lincoln.
INFSO-RI Enabling Grids for E-sciencE Introduction Data Management Ron Trompert SARA Grid Tutorial, September 2007.
Distributed Data for Science Workflows Data Architecture Progress Report December 2008.
The CMS Top 5 Issues/Concerns wrt. WLCG services WLCG-MB April 3, 2007 Matthias Kasemann CERN/DESY.
The new FTS – proposal FTS status. EMI INFSO-RI /05/ FTS /05/ /05/ Bugs fixed – Support an SE publishing more than.
Doug Benjamin Duke University. 2 ESD/AOD, D 1 PD, D 2 PD - POOL based D 3 PD - flat ntuple Contents defined by physics group(s) - made in official production.
The GridPP DIRAC project DIRAC for non-LHC communities.
EGI-Engage Data Services and Solutions Part 1: Data in the Grid Vincenzo Spinoso EGI.eu/INFN Data Services.
1 A Scalable Distributed Data Management System for ATLAS David Cameron CERN CHEP 2006 Mumbai, India.
T3g software services Outline of the T3g Components R. Yoshida (ANL)
CMS: T1 Disk/Tape separation Nicolò Magini, CERN IT/SDC Oliver Gutsche, FNAL November 11 th 2013.
Data Analysis w ith PROOF, PQ2, Condor Data Analysis w ith PROOF, PQ2, Condor Neng Xu, Wen Guan, Sau Lan Wu University of Wisconsin-Madison 30-October-09.
The GridPP DIRAC project DIRAC for non-LHC communities.
1 DIRAC Data Management Components A.Tsaregorodtsev, CPPM, Marseille DIRAC review panel meeting, 15 November 2005, CERN.
Active-HDL Server Farm Course 11. All materials updated on: September 30, 2004 Outline 1.Introduction 2.Advantages 3.Requirements 4.Installation 5.Architecture.
INFSO-RI Enabling Grids for E-sciencE File Transfer Software and Service SC3 Gavin McCance – JRA1 Data Management Cluster Service.
A Data Handling System for Modern and Future Fermilab Experiments Robert Illingworth Fermilab Scientific Computing Division.
OSG STORAGE AND DATA MOVEMENT Tanya Levshina. Talk Outline  Data movement methods and limitation  Open Science Grid (OSG) Storage  Storage Resource.
SAM architecture EGEE 07 Service Availability Monitor for the LHC experiments Simone Campana, Alessandro Di Girolamo, Nicolò Magini, Patricia Mendez Lorenzo,
VO Box discussion ATLAS NIKHEF January, 2006 Miguel Branco -
Data Infrastructure in the TeraGrid Chris Jordan Campus Champions Presentation May 6, 2009.
BIG DATA/ Hadoop Interview Questions.
OSG STORAGE OVERVIEW Tanya Levshina. Talk Outline  OSG Storage architecture  OSG Storage software  VDT cache  BeStMan  dCache  DFS:  SRM Clients.
A. Sim, CRD, L B N L 1 Production Data Management Workshop, Mar. 3, 2009 BeStMan and Xrootd Alex Sim Scientific Data Management Research Group Computational.
Open Science Grid Consortium Storage on Open Science Grid Placing, Using and Retrieving Data on OSG Resources Abhishek Singh Rana OSG Users Meeting July.
VO Experiences with Open Science Grid Storage OSG Storage Forum | Wednesday September 22, 2010 (10:30am)
Riccardo Zappi INFN-CNAF SRM Breakout session. February 28, 2012 Ingredients 1. Basic ingredients (Fabric & Conn. level) 2. (Grid) Middleware ingredients.
a brief summary for users
Large Output and Shared File Systems
StoRM: a SRM solution for disk based storage systems
Vincenzo Spinoso EGI.eu/INFN
Data Bridge Solving diverse data access in scientific applications
Introduction to Data Management in EGI
Sergio Fantinel, INFN LNL/PD
Simulation use cases for T2 in ALICE
Large Scale Test of a storage solution based on an Industry Standard
Australia Site Report Sean Crosby DPM Workshop – 13 December 2013.
Presentation transcript:

OSG Storage Architectures Tuesday Afternoon Brian Bockelman, OSG Staff University of Nebraska-Lincoln

OSG Summer School 2010 Outline Typical Application Requirements. The “Classic SE”. The OSG Storage Element. Simple Data Management on the OSG. Advanced Data Management Architectures 2

OSG Summer School 2010 Storage Requirements Computation rarely happens in a vacuum – it’s often data driven, and sometimes data intensive. OSG provides basic tools to manage your data.  These aren’t as mature as Condor, but have been used successfully by many VOs.  Most of these tools relate to transferring files between sites. 3

OSG Summer School 2010 Common Scenarios Simulation (small configuration input, large output file).  Simulation with input (highly dynamic metadata). Data processing (large input, large output). Data analysis (large input, small output). Common factors:  Relatively static input.  Fine data granularity (each job accesses only a few files).  File size of 2GB and under. 4

OSG Summer School 2010 Scenarios which are un-OSG-like What kind of storage patterns are unlikely to work on the OSG?  Very large files.  Large number of input/output files  Requiring POSIX.  Jobs which require a working set larger than 10GB. 5

OSG Summer School 2010 Storage at OSG CEs All OSG sites have some kind of shared, POSIX-mounted storage (typically NFS).* This is almost never a distributed or high-performance file system This is mounted and writable on the CE.* This is readable (though sometimes read-only) from the OSG worker nodes. 6 *Exceptions apply! Sites ultimately decide

OSG Summer School 2010 Storage at the OSG CE There are typically three places you can write and read data from. These are defined by variables in the job environment (never hardcode these!).  $OSG_APP: Install applications here; shared.  $OSG_DATA: Put data here; shared  $OSG_WN_TMP: Put data here; local disk 7

OSG Summer School 2010 First Stab at Data Management How would you process BLAST queries at a grid site?  Install BLAST application to $OSG_APP via the CE (pull).  Upload data to $OSG_DATA using the CE’s built-in GridFTP server (push).  The job will run the executable from $OSG_APP and read in data from $OSG_DATA. Outputs go back to $OSG_DATA. 8

OSG Summer School 2010 Picture 9 Now – go off and do this! Data Management Exercises 1

OSG Summer School 2010 Why Not? This setup is called the “classic SE” setup, because this is how the grid worked circa  Why didn’t this work? Everything through CE interface is not scalable. High-performance filesystems not reliable or cheap enough. Difficult to manage space. 10

OSG Summer School 2010 Storage Elements In order to make storage and transfers scalable, sites set up a separate system for storage (the Storage Element). Most sites have an attached SE, but there’s a wide range of scalability. These are separated from the compute cluster; normally, you interact it via a get or put of the file.  Not POSIX! 11

OSG Summer School 2010 Storage Elements on the OSG 12 User point of View!

OSG Summer School 2010 User View of the SE Users interact with the SE using the SRM endpoint.  SRM is a web services protocol that does metadata operation at the server, but delegates file movement to other servers.  To use it, you need to know the “endpoint” and the directory you write into.  At many sites, file movement is done via multiple GridFTP servers, load-balanced by the SRM server.  Appropriate for accessing files within the local compute cluster’s LAN or the WAN.  Some sites have specialized internal protocols or access methods, such as dCap, Xrootd, or POSIX – but we won’t discuss them today as there is no generic method. 13

OSG Summer School 2010 Example At Firefly, the endpoint is:  srm://ff-se.unl.edu:8443/srm/v2/server The directory you write into is:  /panfs/panasas/CMS/data/osgedu So, putting them together, we get:  srm://ff- se.unl.edu:8443/srm/v2/server?SFN=/panf s/panasas/CMS/data/osgedu 14

OSG Summer School 2010 Example Reading a file from SRM:  User invokes srm-copy with a SRM URL it would like to read.  srm-copy contacts remote server with a “prepareToGet” call.  SRM server responds with a either a “wait” response or a URL for transferring (TURL).  srm-copy contacts the GridFTP server referenced in the TURL. Performs download.  srm-copy notifies SRM server it is done. 15

OSG Summer School 2010 SE Internals 16

OSG Summer School 2010 SE Internals A few things about the insides of large SEs:  All the SEs we deal with have a single namespace server. This limits the number of total metadata operations per second they can perform (don’t do a recursive “ls”!)  There are tens or hundreds of data servers, allowing for maximum throughput of data for internal protocols.  There are tens of GridFTP servers for serving data with SRM. 17

OSG Summer School 2010 SE Internals Not all SEs are large SEs!  For example, the OSG-EDU BestMan endpoint is simply a (small) NFS server.  Most SEs are scaled to fit the site. Larger sites will have the larger SEs.  Often, it’s a function of the number of worker nodes at the site.  There are many variables involved with using a SE; when in doubt, check with the site before you do strange workflows. 18

OSG Summer School 2010 Simple SE Data Management 19

OSG Summer School 2010 Simple Data Management Use only 1 dependable SRM endpoint (your “home”).  All files are written to here and read from here.  Each file has one URL associated with it.  You thus know where everything is! No synchronizing!  Pay dearly for this simplicity with efficiency (lose data locality).  I would argue, for moderate data sizes (up to hundreds of GB), this isn’t so bad – everyone is on a fast network.  Regardless of what cluster the job runs at, pull in from the storage “home”. This system is scalable if not all people call the same place “home”. This model is simple, but we mostly provide low-level tools. Using this model prevents you from having to code too much on your own. 20

OSG Summer School 2010 Advanced Data Management Topics 21 How do you utilize all these boxes?

OSG Summer School 2010 Data Management Different Techniques Abound  Cache-based: jobs ping the local SRM endpoint and if a file is missing, it downloads from a known “good” source. (SAM)  File transfer systems: You determine a list of transfers to do, and “hand off” the task of doing the transfer to this system. (Stork, FTS)  Data placement systems: Built on top of file transfer systems. Files are grouped into datasets and humans determine where the datasets should go. (PhEDEx, DQ2).  These are built up by the largest organizations. 22

OSG Summer School 2010 Recent PhEDEx activity 23

OSG Summer School 2010 Storage Discovery As opportunistic users, you need to be able to locate usable SEs for your VO. The storage discovery tools query the OSG central information store, the BDII, for information about deployed storage elements.  They then return a list of SRM endpoints you are allowed to utilize. Finding new resources is an essential element of putting together new transfer systems for your VO. 24

OSG Summer School 2010 Parting Advice (Most) OSG sites do not provide a traditional high-performance file system.  The model is “storage cloud”. I think of each SRM endpoint as a storage depot.  You get/put the files you want into some depot. Usually, one is “nearby” to your job. Only use the NFS servers for application installs. Using OSG storage is nothing like using a traditional HPC cluster’s storage.  Think Amazon S3, not Lustre. 25

OSG Summer School 2010 Questions? Questions? Comments? Feel free to ask me questions later: Brian Bockelman, 26