Trojans : A Pentium/Linux PC Clusterfor Internet-Based Multimedia Applications

! Pentium/Linux PC Clustering

! SSI and Availability Middleware

! Superserver Consolidation

! Innovative Cluster Applications! Parallel 3-D Image Rendering

! Real-Time Video Scheduling

! Global Supply Chain Management

! Fast Gene/DNA Sequence Matching 1

Professor Kai HwangUSC Cluster and InternetComputing Laboratory

9/7/2000 , Kai Hwang, USC 2222

Research Team

and Collaborators

The USC Team:Dr. Hai Jin and Dr. Dan Meng, Visiting Scientists

Wonwoo Ro and Shu Xiao, Ph.D. candidates in Computer Engr.

Wei Yue, Ph.D. candidate in Computer Science

Kai Hwang, Professor of Elec. Engr. and Computer Science

Collaborators at Hong Kong Univ. :Dr. Ricky Kwok and Dr. Xiola Lin, Computer Engineering

Dr. Choli Wang, Computer Science and Information Systems

Sam Lin, Ph.D. candidate in Computer Engineering

Roy Ho, M. Phil. student at HKU, currently visiting USC

Clustering of Multiple PCs or Workstationsfor Distributed Supercomputing

Sept. 30, 1999, K. Hwang p.3

9/7/2000 , Kai Hwang, USC 4444

What is a Cluster of Computers?

2Physically, a cluster is a collection of computernodes interconnected by a system-area network(SAN) or by a local-area network (LAN), oftenhoused in the same building.

2 Logically, all computers in the cluster are gluedtogether with middleware support for collectiveusage as a single computing resource, inaddition to the traditional role as individualcomputers.

9/7/2000 , Kai Hwang, USC 5555

g Prototype has 16 Pentinum PCshoused in two 9-ft computer racks.

g All PCs run with the Redhat Linuxversion 6.0 (Kernel version 2.2.5)

g All 16 PC nodes are interconnectedby a 100 Mbps Fast Ethernet switch

g The cluster is ported with DQS,LSF, MPI, PVM, TreadMarks,Elias, and NAS benchmarks, etc.

g Scaling to a future system with 100’sor 1000’s of future PC nodes inter-connected by Gigabit networks

The USC Trojans Cluster ProjectInternet and Cluster Computing Lab., EEB Rm.104

Web site: http://andy.usc.edu/trojan/

9/7/2000 , Kai Hwang, USC 6666

Trojans: An I/O-Centric PC Cluster builtwith Pentium/Linux, Fast Ethernet, SSI,and Checkpointing Middleware at USC

Linux

Pentium

Linux

Pentium

Linux

Pentium

100 Mbps Fast Ethernet

Checkpointing and Availability Support

Single System Image Middleware

Programming Envi-ronments (Java, C,

Fortran, MPI, PVM)

Web WindowsUser Interface

Other Subsystems(Database, RAID,

OLTP, etc.)

9/7/2000 , Kai Hwang, USC 7777

Partitioning Trojan Nodes for Multimediaand I/O-Centric Web Server Applications

EntryPartition

(Internet/Intranet)

SAN(Fast Ethernet)

Console

Data/InfoPartition

ServicePartition

Service Flow Data Flow

9/7/2000 , Kai Hwang, USC 8888

Software Agents Running on A Clusterfor A Superserver Consolidation

LocalD atabase

H eterogeneousData Sources

PC /W orkstation

Laptop

NC

TV Top-SetBox/Page

JA VA -E nabledClient H osts

M ainfram e

Internet/Intranet

Internet/Intranet

Database

W eb site

U sing A PC /W orkstationC luster as A Supersever

U ser A gent(Applet A gent)

ServiceBroker A gent

ServiceAgent

Inform ationA gent

O therSuperserver(s)

C lient B roker Service D ata

9/7/2000 , Kai Hwang, USC 9999

Multi-Server Consolidation for DistributedMultimedia Processing with Guaranteed QoS

MM MultipointComm Server

MM ServersMM StorageServer

MM Transaction/Mgmt. Services

MM NetworkServices

MM EnablingNetworks

MM User Platforms

Desktops(Business)

Handhelds(Mobile)

Set-tops(Home)TV

PC

MAC

Workstation

Distributed SoftwareInfrastructure

Video Playback

Video Conf

Audio Conf

Media Acceleration Tech

9/7/2000 , Kai Hwang, USC 10101010

Scaled Workload leads to LinearSpeedup on The Trojan Cluster

9/7/2000 , Kai Hwang, USC 11111111

1. Parallel 3-D Image Rendering onCluster Nodes with Load Balancing

root

Node1

Node2

Noden

Node1

Node2

Noden

Node1

Node2

Noden

Node1

Node2

Noden

root

Display

SceneDescription

Pre-processing phase Transformationphase

Rasterization phase

Decomposedataset by

object

SimplifyModel

TransformModel

RasterizeModel

Collectpartialimages

Display

Geometricdatabase

Rendering Looping

Dataredistribution

Innovative Cluster Applications :Distributed Multimedia Processing

3-D Image Rendering Using AdaptiveSupersampling on Polygon Edges

• Data Structuretypedef struct {

Color rgb;Boolean SuperPixel;union {

int z;Subpixel * pblock;

} zOrpblock;short dxz, dyz;

} Pixel;

Polygon edge

16 blocks of Subpixel

1 Pixel

typedef struct {Color rgb;int z;

} Subpixel;Preallocated array of Subpixel

The memory usageon a supersampledpixel is fixed nomatter how manypolygons gothrough the pixel.

12

Parallel 3-D Image Rendering over 10Computer Nodes with Load Balancing

0

1

2

3

4

P1

P2

P3

P4

P5

P6 P7

P8

P9

P10 P

1

P2

P3

P4

P5

P6

P7 P8

P9

P10 P

1

P2

P3

P4 P5

P6

P7

P8 P9

P10 P

1 P2

P3

P4 P5

P6

P7

P8

P9

P10

Node Number

Ren

der

ing

Tim

e(s

ec)

Parallel Image Rendering without Load Balancing

Transformation Phase Data Redistribution Rasterization Phase

0

1

2

3

4

5

6

7

P1

P2

P3

P4 P5

P6

P7

P8

P9

P10 P

1 P2

P3 P4

P5

P6

P7

P8

P9

P10 P

1 P2

P3

P4

P5

P6

P7

P8

P9

P10 P1

P2

P3 P4

P5

P6 P7

P8

P9

P10

Node Number

Ren

der

ing

Tim

e(s

ec)

13

A c c e s sn o d e

s w itc h

s w itc h

s w itc hB a c k b o n e

n e tw o rk

S e r v ic eg a t e w a y

L o c a lA rc h iv e s

A c c e s s N e tw o r k

c lie n t

c lie n t

c l ie n t

V id e o a rc h iv e s

V id e o -o n -D e m a n d S y s te m A r c h ite c tu r e

C lu s te r e dV id e o S e r v e r

c l ie n t

c l ie n t

c l ie n t

2. Real-Time Scheduling of Video Streams with High QoSGuarantee in Video-on-Demand (VoD) Services

14

Rate-Based Scheduling with Bandwidth Divisionover Multiple Video Streams To Guarantee QoS

MovieFrames

ClusterServer

Clients

BandwidthDivision

Network

15

9/7/2000 , Kai Hwang, USC 16161616

0.02

0.06

0.1

0.14

0.18

0.22

2 3 4 5 6 7 8

number of video streams

Off

set

Qo

S

Average Offset from The Expected QoSOver Multiple Video Streams

Time-Division

Legend:Bandwidth-Division

3.Supply Chain Managementin Business Globalization.

17K.Hwang,USC, April 7, 1999

Distributed Multi-Agent Computingon the Trojans Cluster

18

Internet

Agent Name Server

OracleDatabaseWeb Browser

Agent

Agent

AgentAgent

AgentDatabase Server

1

2

3

4

56

7

Solving The Traveling SalesmanProblem on The Trojans Cluster

19

4. Bioinformatics : ComputationalMolecular Biology for Health Care

" Human genome research for health care,

disease control, and new drug development

" Parallel processing in the search, sorting,

and alignment of DNA/protein sequences

" Cluster of PCs/workstations for parallel

and distributed biological sequence analysis

Sept. 30, 1999, K. Hwang, p. 20

Internet-basedHumanBiologicalSequenceAnalysis onUSC Trojans

PC Cluster

http://www.sanger.ac.uk/HGP/

21

1. Prove the scalability and programmability of future clusters using low-cost commodity components with SSI services. These PC/WS clusters may eventually replace the high-cost servers and mainframes.

22

Concluding Remarks :

K. Hwang, USC, Sept.30, 1999

2. Support Java, Internet, multi-agent, multimedia, metacomputing, and many innovative cluster, Intranet, Internet, and Web applications in science, education, business, industry, and government with significant cut in cost:

" Distributed multimedia processingin electronic commerce, data-mining, digitalentertainment, and urban management.

" Dedicated digital Libraries for distanceeducation, bioinformatics for health-care,and economic crisis management

" Innovative applications in remote services,tele-medicine, collaborative designs, andenvironmental protection, etc. Sept. 30, 1999, K. Hwang 23

9/7/2000 , Kai Hwang, USC 24242424

Recent Publications:

[1] K. Hwang , et al, “ Designing SSI Clusters withHierarchical Checkpointing and Single I/O Space”IEEE Concurrency, March, 1999, pp.60-69.

[2] K. Hwang, et al, “ Trojans: An I/O-Centric ClusterArchitecture for Consolidated Web ServerApplications ”, submitted to IEEE Int’l Paralleland Distributed Processing Symposium,Cancun, Mexico, May 1-5, 2000.

[3] K. Hwang and Z. Xu, Scalable ParallelComputing, McGraw-Hill, N.Y., 1998.

9/7/2000 , Kai Hwang, USC 25252525

Frames

Movie

ClusterServer

ClientsTime

Time-Division Scheduling of MultipleVideo Streams To Yield Higher QoS

9/7/2000 , Kai Hwang, USC 26262626

Steps to Implement Single Entry Point inTrojans Cluster for Server Consolidation

R ound-R obin D N S

N am e resolverequest

1

2

IP add ress ofincom ing node

3 S ervicerequest

4 R equest d ispatchingto an outgoing node R eply from the

outgoing node5

U ser

In ternet/In tranet

9/7/2000 , Kai Hwang, USC 27272727

Multi-Agent System for SocioeconomicAnalysis and Crisis Management

InternetInformation

Sources

InformationAgents

SEIAgents

FI Agents

EPI Agent

User Agents

Local Storage(Database orData Files)

Backup

Historical DataManagement Agents

Users

GUI GUI

Three-TierAnalysis Model

Middleware for Web ServerConsolidation on a Cluster

Implementationlevel

Managementlevel

Programminglevel

Job Management System (JMS)(GLUnix, LSF, CODINE)

Single File Hierarchy (SFH)(NFS,AFS,xFS, Proxy)

Distributed Shared Memory (DSM)

(TreadMark, Wind Tunnel, etc)

Checkpointing /ProcessMigration (CPM)

Single Process Space(SPS)

Cluster Hardware and OS Platform

User Applications

Single I/O SpaceSIOS)

9