Optimisation of Data Access in Optimisation of Data Access in Grid Environment* Grid Environment*

Darin NikolowDarin Nikolow11 Renata Słota Renata Słota11

Łukasz DutkaŁukasz Dutka1 1 Jacek Kitowski Jacek Kitowski1212

Piotr NyczykPiotr Nyczyk1 1 Mariusz DziewierzMariusz Dziewierz11

11Institute of Computer Science - AGHInstitute of Computer Science - AGH22Academic Computer Centre CYFRONET - AGHAcademic Computer Centre CYFRONET - AGH

University of Mining and Metallurgy, Cracow, PolandUniversity of Mining and Metallurgy, Cracow, Poland

Cracow Grid Workshop, Nov.5-6, 2001 *CrossGrid Project - Task 3.4

OutlineOutline

Background Bottom-top approach Media management software

– middleware for existing HSM– dedicated VTSS

Local component-expert systems Global policy for migration/replication

FOR MORE INFO...

http://www.icsr.agh.edu.pl/

MotivationMotivation

Big and growing stuff of data Multimedia database systems (applications - medical, educational,

virtual reality, virtual laboratories, digital libraries, advanced simulations, ...)

Solution: Tertiary Storage Systems (TSS) = Media Libraries + Management Software

Examples of existing TSS:• HPSS, DataCutter, APRIL, Condor, OmniStore, UniTree, ......

Possible directions– Data access time estimation system - efficient usage– Data distribution and grid implementation - large scale experiments– Expert system for data management– Replication policies

PARMED Project(Uni. of Klagenfurt - Uni. of Mining & Metall. Cracow)

– to support physicians with telematic services for:

• long distance collaboration of medical centers,

• medical teleeducation• case archives

BackgroundBackground

ClientClientClient Site 1

Video Server

Storage Server

ClientClientClient

Site 2Video Server

ClientClientClient Site 3

Storage ServerClientClientClient Site 4

Disk Server

Meta-Database

WAN

r1

a1

d1

r2a2

d2

r3a3

d3

Media Management SoftwareMedia Management Softwareand its usage in X#and its usage in X#

Darin Nikolow

darin@uci.agh.edu.pl

MotivationMotivation

Main purpose of the developed TSS: efficient index-based retrieving of video fragments (instead of file fragments)

– specific requirements for frequent data reading• startup latency• transfer time • minimal transfer rate > video bitrate

Two prototypes proposed and benchmarked– middleware layer for existing HSM– dedicated TSS

The developed systems are of general use -> possible grid implementations

Multimedia Storage and Retrieval System (MMSRS)Multimedia Storage and Retrieval System (MMSRS)

Requirements– use existing software (UniTree HSM)– reduce latency (start-up delay), i.e. -reduce

file granularity– file fragmentation (subfiles)

Implementation– splitting files into pieces of similar size

Middleware layer on HSM Consists of:

– Automated Media Library– UniTree HSM managing system– MPEG extension for HSM (MEH)

MEH receives the name of video file and the frame range - start/end frames

output stream via HTTP

Video Tertiary Storage System (VTSS)Video Tertiary Storage System (VTSS)

Repository Daemon REPD

– keeps repository information

Tertiary File Manager Daemon TFMD

– manages:filedb - tape ident and startup position of the fragmenttapedb - information about tape usage Dedicated TSS

Client requests to VTSS can be of the following kinds:– write a new file to VTSS, read a file fragment from VTSS, delete a file from VTSS.

The fragment range is defined in the frame units Two daemons implemented in C using Unix sockets

MMSRS and VTSS performanceMMSRS and VTSS performance

Hardware (AML Quantum|ATL)– ATL 4/52 (DLT 2000)– ATL 7100 (DLT 7000)– HP D-class server (with UniTree HSM)

Data– 790 MB MPEG1 file with B=0.4 MB/s bitrate (33 min.)– subfile for MMSRS - 16 MB (8,16, 32 MB tested)

• as short as possible to keep reproducing smooth (low latency)• “optimal” subfile length depends on

– positioning time – drive transfer rate – bitrate of the video file

DLT2000 DLT7000

load time [s] 60 37maximal position time[s] 120 120transfer rate [MB/s] 1.25 5

BenchmarksBenchmarks

Startup latency - time elapsed from issuing the request to receiving the first byte

Transfer time - time from receiving the first byte till the end of transmission

Minimal rate - minimal transfer rate experienced by a client with endless buffer (should be greater than the bitrate of the video stream to have smooth reproduction)

System performance for the whole System performance for the whole video file transfer (DLT2000)video file transfer (DLT2000)

UniTree MMSRS VTSS

startup latency [s] 718 90 70

transfer time [s] 135 710 617

avarege rate [MB/s] 5.85 1.11 1.17

total transfer time [s] 853 800 747

total throughput [MB/s] 0.93 0.99 1.06

For DLT2000:– T = 10 GB– N = 64– Br = 0.4 MB/s

Minimal transfer Minimal transfer raterate

MMSRS (DLT2000)MMSRS (DLT2000)

VTSS (DLT7000)VTSS (DLT7000)

VTSS (DLT2000)VTSS (DLT2000)

Qdt = 400 sQdt = 400 s

For DLT7000:– T = 35 GB– N = 52– Br = 0.4 MB/s

Qdt = 1723 s Qdt = 1723 s

Access Time Estimation: Access Time Estimation: Motivation for X#Motivation for X#Retrieving a file from TSS could last few

seconds or few hoursUser’s satisfaction increases when the access

time of data is known (e.g. user waiting to watch selected video; administrator recovering from backup)

Efficient use of storage resources in Grid environment (data replication subsystem)

Access Time Estimation: Access Time Estimation: ApproachesApproaches

Open TSS approach• source code changes• will be used as experimental platform

Black Box TSS approach - for existing HSMs in X# sites

• retrieving TSS’s state info via its native tools and available internal files

Access Time Estimation - Access Time Estimation - Black Box TSS ApproachBlack Box TSS Approach

TSS

databasesconf. files

logs

Monitoring tools

Disk cache

TSS Monitor

TSSSimulator

Request Monitor & Proxy

Client

events collecting update [4]TSS state

[5]

ETA [6]fileid [2]

queue state [3]

feedback [12]

data [10]

fileid ETA? [1]

ETA [7]fileid [8]data [11]Needed info by Simulator:nr of drivestape labelsmedia typesposition of file in medianr of requests...

fileid [9]

ConclusionsConclusions

MMSRS and VTSS more efficient than standard UniTree HSM MMSRS efficient enough to be used as a middleware for existing

HSM of UniTree type (in X# sites) Proposed measurements could be used for:

– building more sophisticated distributed storage systems (faster access to files stored in TSS)

– building access time estimation subsystem Access time estimation subsystem

--->>> an information provider for X# replication and migration of data

http://www.icsr.agh.edu.pl/

Basics of Component-Expert Basics of Component-Expert Technology and its usage in X# Technology and its usage in X#

Łukasz Dutka

dutka@agh.edu.pl

Programusing component

technology

ComponentManagmentSubsystem

UID1

UID2

UID3

Components container

ComponentUID1

ComponentUID2

ComponentUID3

Classical Classical component strategycomponent strategy

Component-expert strategyComponent-expert strategyProgram using

component-experttechnology

ExpertComponent

ManagementSubsystem

(TID1, Env1)

(TID2, Env2)

(TID3, Env1)

Get (TID1, ENV1)

Get (TID2, ENV2)

Get (T

ID3,

ENV1)

Rule-basedexpert system

Fin

d th

e be

st c

ompo

nent

type

TID

x fo

r E

NV

x

ComponentsContain

Component(TID1, SPECa)

Component(TID2, SPECb)

Component(TID3, SPECa)

Components container

Get

info

rmat

ion

abou

t all

com

pone

nts

type

TID

xSystem

knowledgedatabase

Get additionalinformations

Com

ponen

ts differe

nt types m

ay have

the sa

me sp

ecialization

s

Co

mpon

ent typ

e T

ID3

Ko

mpo

nen

t type TID

2C

ompo

nen

t type

TID

1

(TID2, Env3)

Get

(TID

2, E

NV3

)

Component structureComponent structure

A code of the component

Dat

a st

ream

An input parametersstream

An output parametersstream

A header which describe atype and a specialization

Component header structureComponent header structure

Code of the component

Type: Type_of_Component (TIDx)

Attribute_1 = Value_1Attribute_2 = Value_2.........................

Attribute_n = Value_n

Com

ponen

t

specializa

tion

SP

EC

x

Structure of component codeStructure of component code

service run( call-environment,control-parameters)

{

service code

}

Dat

a st

ream

An input parametersstream

An output parametersstream

Call-EnvironmentCall-Environment

Describe state of the call place

Describe call place requirements

Caries information about user or programmer wishes

Expert system processes Call-Environment and finds best component for given Call-Environment

Rule-basedexpert system

Program usingcomponent-expert

technology

ExpertComponent

ManagementSubsystem

(TID1, Env1)

(TID2, Env2)

(TID3, Env1)

Get (TID1, ENV1)

Get (TID2, ENV2)

Get (TID3, ENV1)

(TID2, Env3)

Get (TID

2, ENV3)

Env1={Attribute_1=Value_1,Attribute_2=Value_2,…., Attribute_k=Value_k}

Env3={Attribute_2=Value_2,Attribute_4=Value_4,…., Attribute_z=Value_z}

Expert SubsystemExpert Subsystem

Rule-based expert systemTypical rule looks like If log-expr Then action1 Else action2

The rules describe what is meant by: The best component for given Call-Environment

Expert system logs calls and stores deduction results for further analysis

Profits from Component-Expert Profits from Component-Expert technology technology Dynamic expanding system possibility Ease of solving new problems Minimising programmer responsibility for component

choice Ease of programming in heterogeneous environment Maximal reusable of components Internal simplicity of components code Increase efficiency of programming process

Component-Expert Technology Component-Expert Technology for X# Task 3.4for X# Task 3.4

Basic analysis of Data-access Basic analysis of Data-access problems in X#problems in X# Different data set types Huge data files Distributed environment Long distance connections Mission critical applications Heterogeneous data storing systems Heterogeneous computing systems Open system Unpredictable file types

Basic connection diagram Basic connection diagram

Fires oneprocess

Storage Center

Storage Center

Storage Center

Computing Center

Computingnode

Storage Center

User

ComputingCenter

ComputingCenterComputing

Center

Component-ExpertSystem

GlobalExpertSystem

Component-ExpertSystem

Component-ExpertSystem

Component-ExpertSystem

Sequence DiagramSequence DiagramComputation Node Global Expert System

Local Component-ExpertSystem

Access Server

Get Access Strategy (ENV)

Ask for Capability

Test Capability

Use appropriate test component

Result(Access Strategy)

Get appropriate access component (ENV)

Fire choosen component

HandleHandle

Connect to data stream (Handle)

Data

Data

EOF

Example of Component-Expert Example of Component-Expert technology usage for data access in X#technology usage for data access in X#

Sample Attributes– User ID– Computing Node ID– Preferred replica

localisation– Required throughput– Application purpose– Data sharing– Critical level– Replica expiration .....

Example of local decisions– Devices choosing

(according to availability and type)

– Storing format (blocks, multimedia streams,......)

– Available delivering performance (network, storage devices,....)

– ... And much more ...

System Management for System Management for Migration/Replication Strategies (2/2)Migration/Replication Strategies (2/2) In cooperation with other projects High-level control system (e.g. cooperating with LDAP) Two possible realizations

– heuristic reinforcement learning based on heuristic strategies for migration/replication and system state

– classical rule-based expert system

ConclusionsConclusions

Some elements have been defined and implemented

Working on higher level structure and cooperation with other X# modules and services