SDM-Center talk

Optimizing shared access Optimizing shared access to tertiary storageto tertiary storage

Arie ShoshaniArie Shoshani

Alex SimAlex Sim

July 10, 2001July 10, 2001

Scientific Data Management GroupScientific Data Management GroupComputing Science DirectorateComputing Science Directorate

Lawrence Berkeley National LaboratoryLawrence Berkeley National Laboratory

SDM-Center talk

Outline

• Short High Energy Physics overview (of data handling problem)

• Description of the Storage Coordination System• File tracking• File sharing between file access requests

— coordination of “file bundles”• The Hierarchical Storage Manager (HRM)

— tertiary storage queuing and tape coordination— transfer time for query estimation

SDM-Center talk

Optimizing Storage Management forOptimizing Storage Management for High Energy Physics Applications High Energy Physics Applications

Collaboration# members/institutions

Date offirst data # events/year

total datavolume/year-TB

STAR 350/35 2000 107-10

8 300

PHENIX 350/35 2000 109 600

BABAR 300/30 1999 109 80

CLAS 200/40 1997 1010 300

ATLAS 1200/140 2004 109 2000

Data Volumes for planned HENP experiments

STAR: Solenoidal Tracker At RHICRHIC: Relativistic Heavy Ion Collider

SDM-Center talk

Particle Detection Systems

STAR detector at RHIC Phenix at RHIC

SDM-Center talk

Result of Particle Collision (event)

SDM-Center talk

Typical Scientific Exploration Process

• Generate large amounts of raw data— large simulations— collect from experiments

• Post-processing of data— analyze data (find particles produced, tracks)— generate summary data

• e.g. momentum, no. of pions, transverse energy

• Number of properties is large (50-100)

• Analyze data— use summary data as guide— extract subsets from the large dataset

• Need to access events based on partialproperties specification (range queries)

• e.g. ((0.1 < AVpT < 0.2) ^ (10 < Np < 20)) v (N > 6000)

— apply analysis code

SDM-Center talk

• STAR experiment

— 108 events over 3 years

— 1-10 MB per event: reconstructed data

— events organized into 0.1 - 1 GB files

— 1015 total size

— 106 files, ~30,000 tapes (30 GB tapes)

• Access patterns

— Subsets of events are selected by region in high-dimensional property space for analysis

— 10,000 - 50,000 out of total of 108

— Data is randomly scattered all over the tapes

• Goal: Optimize access from tape systems

Size of Data and Access Patterns

SDM-Center talk

EXAMPLE OF EVENT PROPERTY VALUES

I event 1I N(1) 9965I N(2) 1192I N(3) 1704I Npip(1) 2443I Npip(2) 551I Npip(3) 426I Npim(1) 2480I Npim(2) 541I Npim(3) 382I Nkp(1) 229I Nkp(2) 30I Nkp(3) 50I Nkm(1) 209I Nkm(2) 23I Nkm(3) 32I Np(1) 255I Np(2) 34

I Np(3) 24I Npbar(1) 94I Npbar(2) 12I Npbar(3) 24I NSEC(1) 15607I NSEC(2) 1342I NSECpip(1) 638I NSECpip(2) 191I NSECpim(1) 728I NSECpim(2) 206I NSECkp(1) 3I NSECkp(2) 0I NSECkm(1) 0I NSECkm(2) 0I NSECp(1) 524I NSECp(2) 244I NSECpbar(1) 41I NSECpbar(2) 8

R AVpT(1) 0.325951R AVpT(2) 0.402098R AVpTpip(1) 0.300771R AVpTpip(2) 0.379093R AVpTpim(1) 0.298997R AVpTpim(2) 0.375859R AVpTkp(1) 0.421875R AVpTkp(2) 0.564385R AVpTkm(1) 0.435554R AVpTkm(2) 0.663398R AVpTp(1) 0.651253R AVpTp(2) 0.777526R AVpTpbar(1) 0.399824R AVpTpbar(2) 0.690237I NHIGHpT(1) 205I NHIGHpT(2) 7I NHIGHpT(3) 1I NHIGHpT(4) 0I NHIGHpT(5) 0

54 Properties, as many as 108 events

SDM-Center talk

• Prevent / eliminate unwanted queries=> query estimation (fast estimation index)

• Read only events qualified for a query from a file (avoid reading irrelevant events)=> exact index over all properties

• Share files brought into cache by multiple queries=> look ahead for files needed and cache management

• Read files from same tape when possible=> coordinating file access from tape

Opportunities for optimization

SDM-Center talk

The Storage Access Coordination System (STACS)

QueryEstimator

(QE)

HirarchicalResourceManager(HRM)

QueryMonitor

(QM)

Query estimation /execution requests

file cachingrequest

CachingPolicy

Module

FileCatalog

(FC)

Bit-Slicedindex

file purging

Disk Cache

file caching

User’sApplication

open,read,close

SDM-Center talk

A typical SQL-like Query

SELECT *FROM star_datasetWHERE 500<total_tracks<1000 & energy<3

-- The index will generate a set of files {F6 : E4,E17,E44, F13 : E6,E8,E32, …, F1036 : E503,E3112} that the query needs

-- The files can be returned to the application in any order

SDM-Center talk

File Tracking (1)

SDM-Center talk

File Tracking (2)

SDM-Center talk

File Tracking

query1start

query2start

query3start

All 3queries

SDM-Center talk

Sharing of Files by Multiple Queries

SDM-Center talk

File Bundles:Multiple Event Components

e1

e2

e3

e4

e5

e6

e7

e8

e9

Files ofComponent A

Files ofComponent B

Component Aof event e1

Component Bof event e1

File 4File 3

File 1 File 2

File Bundles: (F1,F2: e1,e2,e3,e5), (F3,F2: e4,e7), (F3,F4: e6,e8,e9)

SDM-Center talk

A typical SQL-like Queryfor Multiple Components

SELECT Vertices, RawFROM star_datasetWHERE 500<total_tracks<1000 & energy<3

-- The index will generate a set of bundles {[F7, F16: E4,E17,E44], [F13, F16: E6,E8,E32], …}

that the query needs

-- The bundles can be returned to the application in any order

-- Bundle: the set of files that need to be in cache at the same time

SDM-Center talk

File Weight Policy for ManagingFile Bundles

• File weight (bundle) = 1 if it appears in a bundle, = 0 otherwise

• Initial file weight = SUM (all bundles for each query) over all queries

• Example:— query 1: file FK appears is 5 bundles

— query 2: file FK appears is 3 bundles

Then, IFW (FK) = 8

queriesinbundlesall

jbundleinisFifotherwiseij

queriesall

i j

kkk FWFIFW 1

0{)()(

SDM-Center talk

File Weight Policy for ManagingFile Bundles (cont’d)

•Dynamic file weight: the file weight for a file in a bundle that was processed is decremented by 1

• Dynamic Bundle Weight

queriesin

bundlesprocessed

jbundleinisFifotherwiseij

queriesall

i j

kkkk FWFIFWFDFW 1

0{)()()(

ibundlein

filesall

kkFDFWBiDBW )()(

SDM-Center talk

How file weights are usedfor caching and purging

• Bundle caching policy— For each query, in turn, cache the bundle

with the most files in cache— In case of a tie, select the bundle with the

highest weight— Ensures that a bundle that include files

needed by other bundles/queries have priority• File purging policy

— No file purging occurs till space is needed— Purge file not in use with smallest weight— Ensures that files needed in other bundles

stay in cache

SDM-Center talk

Other policies

• Pre-fetching policy— queries can request pre-fetching of bundles

subject to a limit— Currently, limit set to two bundles— multiple pre-fetching useful for parallel

processing• Query service policy

— queries serviced in Round Robin fashion— queries that have all their bundles cached

and are still processing are skipped

SDM-Center talk

Managing the queues

.

.

....

.

.

.

.

.

.

.

.

.

QueryQueue

BundleSet

FileSet

FilesBeing

Processed

Filesin Cache

SDM-Center talk

File Tracking of Bundles

Query 1starts here

Query 2starts here

Bundle wasfound in cache

Bundle sharedby two queries

Bundle (3 files)formed, then

passed to query

SDM-Center talk

Summary

• The key to managing bundle caching and purging policies is weight assignment— caching - based on bundle weight— purging - based on file weight

• Other file weight policies are possible— e.g. based on bundle size— e.g. based on tape sharing

• Proving which policy is best - a hard problem— can test in real system - expensive, need stand alone— simulation - too many parameters in query profile can

vary: processing time, inter-arrival time, number of drive, size of cache, etc.

— model with a system of queues - hard to model policies— we are working on last two methods

SDM-Center talk

Read files from same tape when possible

SDM-Center talk

Queuing File Transfers

• Number of PFTPs to HPSS are limited— limit set by a parameter - NoPFTP— parameter can be changed dynamically

• HRM is multi-threaded— issues and monitors multiple PFTPs in parallel

• All requests beyond PFTP limit are queued• File Catalog used to provide for each file

— HPSS path/file_name— Disk cache path/file_name— File size— tape ID

SDM-Center talk

File Queue Management

• Goal— minimize tape mounts— still respect the order of requests— do not postpone unpopular tapes forever

• File clustering parameter - FCP

— If the file at top of queue is in Tapei

and FCP > 1 (e.g. 4) then up to 4 files from Tapei will be selected to be transferred next

— then, go back to file at top of queue• Parameter can be set dynamically

F1(Ti)

F3(Ti)

F2(Ti)

F4(Ti)

1

2

3

4

5

Orderof fileservice

SDM-Center talk

File Caching Order fordifferent File Clustering Parameters

File Clustering Parameter = 1 File Clustering Parameter = 10

SDM-Center talk

Transfer Rate (Tr) Estimates

• Need Tr to estimate total time of a query• Tr is average over recent file transfers from the

time PFTP request is made to the time transfer completes. This includes:— mount time, seek time, read to HPSS Raid,

transfer to local cache over network• For dynamic network speed estimate

— check total bytes for all file being transferredover small intervals (e.g. 15 sec)

— calculate moving average over n intervals(e.g. 10 intervals)

• Using this, actual time in HPSS can be estimated

SDM-Center talk

Dynamic Display of Various Measurements

SDM-Center talk

Query Estimate

• Given: transfer rate Tr.• Given a query for which:

— X files are in cache— Y files are in the queue— Z files are not scheduled yet

• Let s(file_set) be the total byte size of all files in file_set

• If Z = 0, then— QuEst = s(Y’)/Tr

• If Z = 0, then— QuEst = (s(T)+ q.s(Z))/Tr

where q is the number of active queries

F1(Y)

F3(Y)

F2(Y)

F4(Y)

T

SDM-Center talk

Reason for q.s(Z)

20 Queries of length ~20 minuteslaunched 20 minutes apart

20 Queries of length ~20 minuteslaunched 5 minutes apart

Estimate pretty close Estimate bad - requestaccumulate in queue

SDM-Center talk

Error Handling

• 5 generic errors— file not found

• return error to caller

— limit PFTP reached• can’t login• re-queue request, try later (1-2 min)

— HPSS error (I/O, device busy)• remove part of file from cache, re-queue• try n times (e.g. 3), then return error

“transfer_failed”

— HPSS down• re-queue request, try repeatedly till successful• respond to File_status request with “HPSS_down”

SDM-Center talk

Summary

• Heirarchical Resource Manager— insulates applications from transient

HPSS and network errors— limits concurrent PFTPs to HPSS— manages queue to minimize tape mounts— provides file/query time estimates— handles errors in a generic way

• Same API can be used for any MSS, suchas Unitree, Enstore, Castor, etc.

SDM-Center talk

Future Plans

• Package the HRM (CORBA, APIs)• Dynamic file catalog Capability

— Interface HRM to HSI eliminating the need for a file catalog

• Expand use of HRM to include “write capability”• Build upon results of Probe project

— Large block size, tape striping• Provide hints to MPI-IO module• Apply to HENP experiments – initially STAR• Work with PPDG collaboration pilot (SciDAC)• Perform research on “object granularity caching”