1. Economics Paradigm for “Resource Management and Scheduling” for Service Oriented P2P/Grid Computing
WW Grid
Rajkumar Buyya
Melbourne, Australia

3. Need Honest Answers!
WW Grid
I want to have access to your Grid resources & want to know how many of you are willing to give me access ? (following cases)
I am unable to give you access our Australian machines, but I want to have access to yours! [social]
Want to solve academic problems
Want to solve business problems
I am willing to gift you Kangaroos! [bartering]
I am willing to give you access to my machines, if you want. (sharing, but no measure & no QoS) [bartering]
I am willing to pay you dollars on usage basis. [economic incentive, market-based, and QoS]

4. Overview A quick glance at today’s Grid computing
Resource Management challenges for next generation Grid computing
A Glance at Approaches to Grid computing.
Grid Architecture for Computational Economy
Economy Grid = Globus + GRACE
Nimrod-G -- Grid Resource Broker
Scheduling Experiments
Case Study: Drug Design Application on Grid
Conclusions
Scheduling
Economics
Grid
Economy Grid

5. Scalable HPC: Breaking Administrative Barriers & new challenges
2100 ?
PERFORMANCE
2100
Administrative Barriers
Individual
Group
Department
Campus
State
National
Globe
Inter Planet
Universe
Desktop
SMPs or SuperComputers
Local
Cluster
Enterprise
Cluster/Grid
Global
Cluster/Grid
Inter Planetary
Grid!

6. Why Grids? Large Scale Explorations need them—Killer Applications.
Solving grand challenge applications using modeling, simulation and analysis
Aerospace
Internet &
Ecommerce
Life Sciences
CAD/CAM
Digital Biology
Military Applications
Military Applications
Military Applications

8. What is Grid ?
An infrastructure that logically couples distributed resources:
Computers – PCs, workstations, clusters, supercomputers, laptops, notebooks,
mobile devices, PDA, etc;
Software – e.g., ASPs renting expensive special purpose applications on demand;
Catalogued data and databases – e.g. transparent access to human genome database;
Special devices – e.g., radio telescope – searching for life in galaxy.
People/collaborators.
and presents them as an integrated global resource.
It enables the creation of virtual enterprises (VEs) for resource sharing.
Wide area
data archives

9. P2P/Grid Applications-Drivers
Distributed HPC (Supercomputing):
Computational science.
High-Capacity/Throughput Computing:
Large scale simulation/chip design & parameter studies.
Content Sharing (free or paid)
Sharing digital contents among peers (e.g., Napster)
Remote software access/renting services:
Application service provides (ASPs) & Web services.
Data-intensive computing:
Virtual Drug Design, Particle Physics, Stock Prediction...
On-demand, realtime computing:
Medical instrumentation & Mission Critical.
Collaborative Computing:
Collaborative design, Data exploration, education.
Service Oriented Computing (SOC):
Computing as Utility: New paradigm and new industries.

10. Building and Using Grids require
Services that make our systems Grid Ready!
Security mechanisms that permit resources to be accessed only by authorized users.
(New) programming tools that make our applications Grid Ready!.
Tools that can translate the requirements of an application/user into the requirements of computers, networks, and storage.
Tools that perform resource discovery, trading, selection/allocation, scheduling and distribution of jobs and collects results.
Globus ?

11. Players in Grid Computing

12. What users want ? Users in Grid Economy & Strategy
Grid Consumers
Execute jobs for solving varying problem size and complexity
Benefit by selecting and aggregating resources wisely
Tradeoff timeframe and cost
Strategy: minimise expenses
Grid Providers
Contribute “idle” resource for executing consumer jobs
Benefit by maximizing resource utilisation
Tradeoff local requirements & market opportunity
Strategy: maximise return on investment

13. Challenges for Next Generation Grid Technology Development

14. Challenges for Grid Computing
Uniform Access
Security
System Management
Computational Economy
Resource Discovery
Resource Allocation
& Scheduling
Data locality
Network Management
Application Development Tools

15. Sources of Complexity in Resource Management for World Wide Grid Computing
Size (large number of nodes, providers, consumers)
Heterogeneity of resources (PCs, Workstations, clusters, and supercomputers, instruments, databases, software)
Heterogeneity of fabric management systems (single system image OS, queuing systems, etc.)
Heterogeneity of fabric management polices
Heterogeneity of application requirements (CPU, I/O, memory, and/or network intensive)
Heterogeneity in resource demand patterns (peak, off-peak, ...)
Applications need different QoS at different times (time critical results). The utility of experimental results varies from time to time.
Geographical distribution of users & located different time zones
Differing goals (producers and consumers have different objectives and strategies)
Unsecure and Unreliable environment

16. Traditional approaches to resource management & scheduling are NOT useful for Grid ?
They use centralised policy that need
complete state-information and
common fabric management policy or decentralised consensus-based policy.
Due to too many heterogenous parameters in the Grid it is impossible to define/get:
system-wide performance matrix and
common fabric management policy that is acceptable to all.
“Economics” paradigm proved to effective institution in managing decentralization and heterogeneity that is present in human economies!
Fall of USSR & Emergence of US as world superpower! (monopoly?)
So, we propose/advocate the use of computational economics principles in management of resources and scheduling computations on world wide Grid.
Think locally and act globally approach to grid computing!

17. Benefits of Computational Economies
It provides a nice paradigm for managing self interested and self-regulating entities (resource owners and consumers)
Helps in regulating supply-and-demand for resources.
Services can be priced in such a way that equilibrium is maintained.
User-centric / Utility driven: Value for money!
Scalable:
No need of central coordinator (during negotiation)
Resources(sellers) and also Users(buyers) can make their own decisions and try to maximize utility and profit.
Adaptable
It helps in offering different QoS (quality of services) to different applications depending the value users place on them.
It improves the utilisation of resources
It offers incentive for resource owners for being part of the grid!
It offers incentive for resource consumers for being good citizens
There is large body of proven Economic principles and techniques available, we can easily leverage it.

18. New challenges of Computational Economy
Resource Owners
How do I decide prices ? (economic models?)
How do I specify them ?
How do I enforce them ?
How do I advertise & attract consumers ?
How do I do accounting and handle payments?
…..
Resource Consumers
How do I decide expenses ?
How do I express QoS requirements ?
How I trade between timeframe & cost ? ….
Any tools, traders & brokers available to automate the process ?

19. Grid Computing Approaches
mix-and-match
Object-oriented
Internet/partial-P2P
Grid Computing
Approaches
Network enabled Solvers
NetSolve
Market/Computational Economy
Nimrod-G

20. Many Grid Projects & Initiatives
Australia
Economy Grid
Nimrod-G
Virtual Lab
Active Sheets
DISCWorld
..new coming up
Europe
UNICORE
MOL
Lecce GRB
Poland MC Broker
EU Data Grid
EuroGrid
MetaMPI
Dutch DAS
XW, JaWS
and many more...
Japan
Ninf
DataFarm
USA
Globus
Legion
Javelin
AppLeS
NASA IPG
Condor
Harness
NetSolve
AccessGrid
GrADS
and many more...
Cycle Stealing & .com Initiatives
Distributed.net ….
Entropia, UD, Parabon,….
Public Forums
Global Grid Forum
P2P Working Group
IEEE TFCC
Grid & CCGrid conferences

21. Many Testbeds ? & who pays ?, who regulates supply and demand ?
WW Grid
GUSTO (decommissioned)
World Wide Grid
Legion Testbed
NASA IPG

22. Testbeds so far -- observations
Who contributed resources & why ?
Volunteers: for fun, challenge, fame, charismatic apps, public good like distributed.net & projects.
Collaborators: sharing resources while developing new technologies of common interest – Globus, Legion, Ninf, Ninf, MC Broker, Lecce GRB,... Unless you know lab. leaders, it is impossible to get access!
How long ?
Short term: excitement is lost, too much of admin. Overhead (Globus inst+), no incentive, policy change,…
What we need ? Grid Marketplace!
Regulates supply-and-demand, offers incentive for being players, simple, scalable solution, quasi-deterministic – proven model in real-world.

23. Building an Economy Grid (Next Generation Grid Computing!)
To enable the creation and promotion of:
Grid Marketplace (competitive)
ASP
Service Oriented Computing
. . .
And let users focus on their own work (science, engineering, or commerce)!

24. GRACE: A Reference Grid Architecture for Computational Economy
Grid Bank
Grid Market Services
Information Server(s)
Sign-on
Health
Monitor
Info ?
Grid Explorer
Grid Node N …
Application …
Secure
Job Control
Agent
Schedule Advisor
Grid Node1
QoS
Pricing Algorithms
Trade Server
Trading
Trade Manager
Accounting
Resource
Reservation
Misc. services …
Deployment Agent
JobExec
Resource Allocation
Grid User
Grid Resource Broker
Storage R1 R2 … Rm
Grid Middleware
Services
Grid Service Providers
See PDPTA 2000 paper!

25. See SPIE ITCom 2001 paper!: with Heinz Stockinger, CERN!
Economic Models
Price-based: Supply,demand,value, wealth of economic system
Commodity Market Model
Posted Price Model
Bargaining Model
Tendering (Contract Net) Model
Auction Model
English, first-price sealed-bid, second-price sealed-bid (Vickrey), and Dutch (consumer:low,high,rate; producer:high, low, rate)
Proportional Resource Sharing Model
Monopoly (one provider) and Oligopoly (few players)
consumers may not have any influence on prices.
Bartering
Shareholder Model
Partnership Model
See SPIE ITCom 2001 paper!: with Heinz Stockinger, CERN!

26. Grid Open Trading Protocols
Trade Manager
Trade Server
Get Connected
Pricing Rules
Call for Bid(DT)
Reply to Bid (DT)
Negotiate Deal(DT) ….
API
Confirm Deal(DT, Y/N)
DT - Deal Template
- resource requirements (BM)
- resource profile (BS)
- price (any one can set)
- status
- change the above values
- negotiation can continue
- accept/decline
- validity period
Cancel Deal(DT)
Change Deal(DT)
Get Disconnected

27. Grid Components … … … … Grid Apps. Scientific Engineering
Applications and Portals
Grid
Apps. …
Scientific
Engineering
Collaboration
Prob. Solving Env.
Web enabled Apps
Development Environments and Tools
Grid
Tools …
Languages
Libraries
Debuggers
Monitoring
Resource Brokers
Web tools
Distributed Resources Coupling Services
Grid
Middleware …
Security
Information
Process
QoS
Resource Trading
Market Info
Local Resource Managers
Operating Systems
Queuing Systems
Libraries & App Kernels …
TCP/IP & UDP
Grid
Fabric
Networked Resources across Organisations …
Computers
Clusters
Storage Systems
Data Sources
Scientific Instruments

28. Economy Grid = Globus + GRACE
Applications …
Grid
Apps.
Science
Engineering
Commerce
Portals
ActiveSheet
High-level Services and Tools
Grid Status …
Grid
Tools
DUROC
MPI-G
CC++
Nimrod/G
globusrun
Core Services
Heartbeat Monitor
Nexus
GRAM
GRACE-TS
Grid
Middleware
Globus Security Interface
MDS
GASS
DUROC
GARA
GMD
GBank
Grid
Fabric
Local Services
Condor
GRD
QBank
JVM
TCP
UDP
LSF
PBS
eCash
Linux
Irix
Solaris
See IPDPS HWC 2001 paper!

29. GRACE components A resource broker (e.g., Nimrod/G)
Various resource trading protocols for different economic models
A mediator for negotiating between users and grid service providers (Grid Market Directory)
A deal template for specifying resource requirements and services offers
Grid Trading Server
Pricing policy specification
Accounting (e.g., QBank) and payment management (GridBank, not yet implemented)

30. (digital transactions)
Pricing, Accounting, Allocations and Job Scheduling each site/Grid Level
Pricing Policy
GRID Bank
(digital transactions) 2
Trade Server
QBank
Site 1 3 5 8
0. Make Deposits,
Transfers, Refunds,
Queries/Reports
1. Clients negotiates for
access cost.
2. Negotiation is performed
per owner defined policies.
3. If client is happy, TS informs
QB about access deal.
4. Job is Submitted
5. Check with QB for “go ahead”
6. Job Starts
7. Job Completes
8. Inform QB about resource
resource utilization.
Resource Manager 4
IBM-LL/PBS/…. 6 7
Compute Resources
clusters/SGI/SP/...

31. Service Items to be Charged
CPU - User and System time
Memory:
maximum resident set size - page size
amount of memory used
page faults: with/without physical I/O
Storage: size, r/w/block IO operations
Network: msgs sent/received
Signals received, context switches
Software and Libraries accessed
Data Sources (e.g. Protein Data Bank)

32. How to decide Price ? Fixed price model (like today’s Internet)
Dynamic/Demand and Supply (like tomorrow’s Internet)
Usage Period
Loyalty of Customers (like Airlines favoring frequent flyers!)
Historical data
Advance Agreement (high discount for corporations)
Usage Timing (peak, off-peak, lunch time)
Calendar based (holiday/vacation period)
Bulk Purchase (register 100 .com domains at once!)
Voting -- trade unions decide pricing structure
Resource capability as benchmarked in the market!
Academic R&D/public-good application users can be offered at cheaper rate compared to commercial use.
Customer Type – Quality or price sensitive buyers.
Can be Prescribed by Regulating (Govt.) authorities

33. Payments- Options & Automation
Buy credits in advance / GSPs bill the user later--”pay as you go”
Pay by Electronic Currency via Grid Bank
NetCash (anonymity), NetCheque, and Paypal
NetCheque: -
Users register with NC accounting servers, can write electronic cheques and send (e.g ). When deposited, balance is transferred from sender to receiver account.
NetCash -
It supports anonymity and it uses the NetCheque system to clear payments between currency servers.
Paypal.com– account+ is linked to credit card.
Enter the recipient’s address and the amount you wish to request.
The recipient gets an notification and pays you at

34. Nimrod-G: The Grid Resource Broker
Soft Deadline and Budget-based Economy Grid Resource Broker for Parameter Processing on P2P Grids

35. Parametric Computing (What Users think of Nimrod Power)
Parameters
Magic Engine
Multiple Runs
Same Program
Multiple Data
Killer Application for the Grid!
See IPDPS 2000 paper!
Courtesy: Anand Natrajan, University of Virginia

36. P-study Applications -- Characteristics
Code (Single Program: sequential or threaded)
High Resource Requirements
Long-running Instances
Numerous Instances (Multiple Data)
High Computation-to-Communication Ratio
Embarrassingly/Pleasantly Parallel

37. Sample P-Sweep Applications
Bioinformatics: Drug Design / Protein Modelling
Combinatorial Optimization:
Meta-heuristic parameter estimation
Ecological Modelling: Control Strategies for Cattle Tick
Sensitivity experiments on smog formation
Data Mining
High Energy Physics: Searching for Rare Events
Electronic CAD: Field Programmable Gate Arrays
Computer Graphics: Ray Tracing
Finance: Investment Risk Analysis
VLSI Design: SPICE Simulations
Civil Engineering:
Building Design
Automobile:
Crash Simulation
Network Simulation
Aerospace: Wing Design
astrophysics

38. Thesis
Perform parameter sweep (bag of tasks) (utilising distributed resources) within “T” hours or early and cost not exceeding $M.
Three Options/Solutions:
Using pure Globus commands
Build your own Distributed App & Scheduler
Use Nimrod-G (Resource Broker)

39. Remote Execution Steps
Choose Resource
Transfer Input Files
Set Environment
Start Process
Pass Arguments
Monitor Progress
Summary View
Job View
Event View
Read/Write Intermediate Files
Transfer Output Files
+Resource Discovery, Trading, Scheduling, Predictions, Rescheduling, ...

40. Using Pure Globus commands
Do all yourself! (manually)
Total Cost:$???

41. Build Distributed Application & Scheduler
Build App case by case basis
Complicated Construction
E.g., AppLeS/MPI based
Total Cost:$???

42. Use Nimrod-G
Aggregate Job Submission
Aggregate View
Submit & Play!

43. Nimrod & Associated Family of Tools
Remote Execution Server
(on demand Nimrod Agent)
P-sweep App. Composition: Nimrod/ Enfusion
Resource Management and Scheduling:
Nimrod-G Broker
Design Optimisations:
Nimrod-O
App. Composition and Online Visualization:
Active Sheets
Grid Simulation in Java:
GridSim
Drug Design on Grid:
Virtual Lab
File Transfer Server
Upcoming?: HEPGrid (+U. Melbourne), GAVE(+Rutherford Appleton Lab)
Grid (Un)Aware Virtual Engineering

44. Nimrod/G : A Grid Resource Broker
A resource broker for managing, steering, and executing task farming (parametric sweep/SPMD model) applications on Grid based on deadline and computational economy.
Based on users’ QoS requirements, our Broker dynamically leases services at runtime depending on their quality, cost, and availability.
Key Features
A single window to manage & control experiment
Persistent and Programmable Task Farming Engine
Resource Discovery
Resource Trading
Scheduling & Predications
Generic Dispatcher & Grid Agents
Transportation of data & results
Steering & data management
Accounting

45. A Glance at Nimrod-G Broker
Nimrod/G Client
Nimrod/G Client
Nimrod/G Client
Nimrod/G Engine
Schedule Advisor
Grid
Store
Trading Manager
Grid Dispatcher
Grid Explorer
Grid Middleware
Globus, Legion, Condor, etc.
TM TS
GE GIS
Grid Information Server(s)
RM & TS
RM & TS
RM & TS G C L G
Legion enabled node.
Globus enabled node. L G C L
See HPCAsia 2000 paper!
RM: Local Resource Manager, TS: Trade Server
Condor enabled node.

46. Nimrod/G Grid Broker Architecture
Legacy Applications
Customised Apps
(Active Sheet)
Monitoring and Steering Portals
Nimrod Clients
P-Tools (GUI/Scripting)
(parameter_modeling)
XML?
Farming Engine
Meta-Scheduler
XML
Programmable Entities Management
Algorithm1
Schedule Advisor
. . .
Resources
Jobs
Tasks
Channels
AlgorithmN
Nimrod Broker
IP hourglass ?
AgentScheduler
Agents
JobServer
Database
(Postgres)
Grid Explorer
Trading Manager
Dispatcher & Actuators
. . .
Globus-A
Legion-A
P2P-A
Globus
Legion
Condor
P2P
. . .
GTS
GMD
G-Bank
Middleware
Computers
Local Schedulers
Storage
Networks
. . .
Instruments
Fabric
PC/WS/Clusters
Condor/LL/Mosix/
Database
. . .
Radio Telescope

47. A Nimrod/G Monitor Cost Deadline Legion hosts Globus Hosts
Bezek is in both
Globus and Legion Domains

48. User Requirements: Deadline/Budget

49. Active Sheet: Spreadsheet Processing on Grid
Nimrod
Proxy
Nimrod/G
See HPC 2001 paper!

51. Nimrod/G Interactions
Resource
Discovery
Grid Info
servers
Scheduler
Grid Trade
Server
Resource allocation
(local)
Farming
Engine
Dispatcher
Queuing
System
Nimrod
Agent
User
process
Process
server
I/O
server
File access
“Do this in 30min. for $10?”
Root node
Gatekeeper node
Computational node

52. Adaptive Scheduling Algorithms
See HPDC AMS 2001 paper!
Discover More Resources
Discover Resources
Establish Rates
Compose & Schedule
Evaluate & Reschedule
Meet requirements ? Remaining Jobs, Deadline, & Budget ?
Distribute Jobs

53. Cost Model Without cost ANY shared system becomes un-managable
Charge users more for remote facilities than their own
Choose cheaper resources before more expensive ones
Cost units (G$) may be
Dollars
Shares in global facility
Stored in bank

54. Cost Matrix @ Grid site X1 3 2
User 5
Machine 1
User 1
Machine 5
Non-uniform costing
Encourages use of local resources first
Real accounting system can control machine usage
Resource Cost = Function (cpu, memory, disk, network, software, QoS, current demand, etc.)
Simple: price based on peaktime, offpeak, discount when less demand, ..

55. Deadline and Budget-based Cost Minimization Scheduling
Sort resources by increasing cost.
For each resource in order, assign as many jobs as possible to the resource, without exceeding the deadline.
Repeat all steps until all jobs are processed.

56. Deadline-based Cost-minimization Scheduling
M - Resources, N - Jobs, D - deadline
Note: Cost of any Ri is less than any of Ri+1 …. Or Rm
RL: Resource List need to be maintained in increasing order of cost
Ct - Time when accessed (Time now)
Ti - Job runtime (average) on Resource i (Ri) [updated periodically]
Ti is acts as a load profiling parameter.
Ai - number of jobs assigned to Ri , where:
Ai = Min (No.Unassigned Jobs, No. Jobs Ri can complete by remaining deadline)
No.UnAssignedJobsi = Diff( N, (A1+…+Ai-1))
JobsRi consume = RemainingTime (D- Ct) DIV Ti
ALG: Invoke Job Assignment() periodically until all jobs done.
Job Assignment()/Reassignment():
Establish ( RL, Ct , Ti , Ai ) dynamically – Resource Discovery.
For all resources (I = 1 to M) { Assign Ai Jobs to Ri , if required}

57. Deadline and Budget Constraint (DBC) Time Minimization Scheduling
For each resource, calculate the next completion time for an assigned job, taking into account previously assigned jobs.
Sort resources by next completion time.
Assign one job to the first resource for which the cost per job is less than the remaining budget per job.
Repeat all steps until all jobs are processed. (This is performed periodically or at each scheduling-event.)

58. Deadline and Budget Constraint (DBC) Time+Cost Min. Scheduling
Split resources by whether cost per job is less than budget per job.
For the cheaper resources, assign jobs in inverse proportion to the job completion time (e.g. a resource with completion time = 5 gets twice as many jobs as a resource with completion time = 10).
For the dearer resources, repeat all steps (with a recalculated budget per job) until all jobs are assigned.
[Schedule/Reschedule] Repeat all steps until all jobs are processed.

59. Evaluation of Scheduling Heuristics
A Hypothetical Application on
World Wide Grid
WW Grid

60. World Wide Grid (WWG) Internet Australia North America Monash Uni.:
WW Grid
World Wide Grid (WWG)
Australia
North America
Monash Uni.:
ANL: SGI/Sun/SP2
USC-ISI: SGI
UVa: Linux Cluster
UD: Linux cluster
UTK: Linux cluster
Nimrod/G
Linux cluster
Globus+Legion
GRACE_TS
Solaris WS
Globus/Legion
GRACE_TS
Internet
WW Grid
Asia/Japan
Europe
Tokyo I-Tech.:
ETL, Tuskuba
ZIB/FUB: T3E/Mosix
Cardiff: Sun E6500
Paderborn: HPCLine
Lecce: Compaq SC
CNR: Cluster
Calabria: Cluster
CERN: Cluster
Pozman: SGI/SP2
Linux cluster
Globus +
GRACE_TS
Chile: Cluster
Globus +
GRACE_TS
Globus +
GRACE_TS
South America

61. Experiment-1 Setup Workload:
165 jobs, each need 5 minute of cpu time
Deadline: 1 hrs. and budget: 800,000 units
Strategy: minimise cost and meet deadline
Execution Cost with cost optimisation
AU Peaktime: (G$)
AU Offpeak time: (G$)

62. Resources Selected & Price/CPU-sec.
Resource Type & Size
Owner and Location
Grid services
Peaktime Cost (G$)
Offpeak cost
Linux cluster (60 nodes)
Monash, Australia
Globus/Condor 20 5
IBM SP2 (80 nodes)
ANL, Chicago, US
Globus/LL 10
Sun (8 nodes)
Globus/Fork
SGI (96 nodes)
Globus/Condor-G 15
SGI (10 nodes)
ISI, LA, US

63. Execution @ AU Peak Time

64. Execution @ AU Offpeak Time

65. AU peak: Resources/Cost in Use
After the calibration phase, note
the difference in pattern of two graphs.
This is when scheduler stopped using expensive resources.

66. AU offpeak: Resources/Cost in Use

67. Experiment-2 Setup Workload: Deadline: 2 hrs. and budget: 396000 units
165 jobs, each need 5 minute of CPU time
Deadline: 2 hrs. and budget: units
Strategy: minimise time / cost
Execution Cost with cost optimisation
Optimise Cost: (G$) (finished in 2hrs.)
Optimise Time: (G$) (finished in 1.25 hr.)
In this experiment: Time-optimised scheduling run costs double that of Cost-optimised.
Users can now trade-off between Time Vs. Cost.

68. Resources Selected & Price/CPU-sec.
Resource & Location
Grid services & Fabric
Cost/CPU sec.or unit
No. of Jobs Executed
Time_Opt
Cost_Opt.
Linux Cluster-Monash, Melbourne, Australia
Globus, GTS, Condor 2 64
153
Linux-Prosecco-CNR, Pisa, Italy
Globus, GTS, Fork 3 7 1
Linux-Barbera-CNR, Pisa, Italy 4 6
Solaris/Ultas2
TITech, Tokyo, Japan 9
SGI-ISI, LA, US 8 37 5
Sun-ANL, Chicago,US 42
Total Experiment Cost (G$)
237000
115200
Time to Complete Exp. (Min.) 70
119

69. DBC Scheduling for Time Optimization

70. DBC Scheduling for Cost Optimization

71. Application Case Study
The Virtual Laboratory Project: "Molecular Modelling for Drug Design" on Peer-to-Peer Grid

72. Virtual Drug Design: Data Intensive Computing on Grid
A Virtual Laboratory for “Molecular Modelling for Drug Design” on Peer-to-Peer Grid.
It provides tools for examining millions of chemical compounds (molecules) in the Protein Data Bank (PDB) to identify those having potential use in drug design.
In collaboration with:
Kim Branson, Structural Biology, Walter and Eliza Hall Institute (WEHI)

73. Virtual Drug Design A Virtual Lab for “Molecular Modeling for Drug Design” on P2P Grid
Data Replica Catalogue
Grid Market
Directory
Grid Info.
Service
“Give me list PDBs sources
Of type aldrich_300?”
“service cost?”
“service providers?”
GTS
Resource
Broker
“Screen 2K molecules in 30min. for $10”
“mol.5 please?”
GTS
(RB maps suitable Grid nodes and Protein DataBank)
“get mol.10 from pdb1 & screen it.”
PDB2
GTS
“mol.10 please?”
GTS
GTS
(GTS - Grid
Trade Server)
PDB1

74. DataGrid Brokering Nimrod/G Computational PDB Broker Grid Broker
“Screen 2K molecules in 30min. for $10”
Nimrod/G
Computational
Grid Broker
Algorithm1
Data Replica Catalogue
PDB Broker
. . .
AlgorithmN 3
“PDB replicas please?”
“advise PDB source? 1 5 2 4
“process & send results”
Grid Info.
Service
“selection & advise: use GSP4!”
“Screen mol.5 please?”
“Is GSP4 healthy?” 7 6
“mol.5 please?”
PDB2
PDB Service
PDB Service
GSP1
GSP2
GSP3 (Grid Service Provider)
GSP4
GSPm
GSPn

75. Software Tools Molecular Modelling Tools (DOCK)
Parameter Modelling Tools (Nimrod/enFusion)
Grid Resource Broker (Nimrod-G)
Data Grid Broker
Protein Data Bank (PDB) Management and Intelligent Access Tools
PDB databse Lookup/Index Table Generation.
PDB and associated index-table Replication.
PDB Replica Catalogue (that helps in Resource Discovery).
PDB Servers (that serve PDB clients requests).
PDB Brokering (Replica Selection).
PDB Clients for fetching Molecule Record (Data Movement).
Grid Middleware (Globus and GrACE)
Grid Fabric Management (Fork/LSF/Condor/Codine/…)

76. DOCK code* (Enhanced by WEHI, U of Melbourne)
A program to evaluate the chemical and geometric complementarities between a small molecule and a macromolecular binding site.
It explores ways in which two molecules, such as a drug and an enzyme or protein receptor, might fit together.
Compounds which dock to each other well, like pieces of a three-dimensional jigsaw puzzle, have the potential to bind.
So, why is it important to able to identify small molecules which may bind to a target macromolecule?
A compound which binds to a biological macromolecule may inhibit its function, and thus act as a drug.
Thus disabling the ability of (HIV) virus attaching itself to molecule/protein!
With system specific code changed, we have been able to compile it for Sun-Solaris, PC Linux, SGI IRIX, Compaq Alpha/OSF1
* Original Code: University of California, San Francisco:

77. Molecule to be screened
Dock input file
score_ligand yes
minimize_ligand yes
multiple_ligands no
random_seed
anchor_search no
torsion_drive yes
clash_overlap
conformation_cutoff_factor 3
torsion_minimize yes
match_receptor_sites no
random_search yes
maximum_cycles
ligand_atom_file S_1.mol2
receptor_site_file ece.sph
score_grid_prefix ece
vdw_definition_file parameter/vdw.defn
chemical_definition_file parameter/chem.defn
chemical_score_file parameter/chem_score.tbl
flex_definition_file parameter/flex.defn
flex_drive_file parameter/flex_drive.tbl
ligand_contact_file dock_cnt.mol2
ligand_chemical_file dock_chm.mol2
ligand_energy_file dock_nrg.mol2
Molecule to be screened

78. Parameterized Dock input file
score_ligand $score_ligand
minimize_ligand $minimize_ligand
multiple_ligands $multiple_ligands
random_seed $random_seed
anchor_search $anchor_search
torsion_drive $torsion_drive
clash_overlap $clash_overlap
conformation_cutoff_factor $conformation_cutoff_factor
torsion_minimize $torsion_minimize
match_receptor_sites $match_receptor_sites
random_search $random_search
maximum_cycles $maximum_cycles
ligand_atom_file ${ligand_number}.mol2
receptor_site_file $HOME/dock_inputs/${receptor_site_file}
score_grid_prefix $HOME/dock_inputs/${score_grid_prefix}
vdw_definition_file vdw.defn
chemical_definition_file chem.defn
chemical_score_file chem_score.tbl
flex_definition_file flex.defn
flex_drive_file flex_drive.tbl
ligand_contact_file dock_cnt.mol2
ligand_chemical_file dock_chm.mol2
ligand_energy_file dock_nrg.mol2
Molecule to be screened

79. Molecules to be screened
Dock PlanFile (contd.)
parameter database_name label "database_name" text select oneof "aldrich" "maybridge" "maybridge_300" "asinex_egc" "asinex_epc" "asinex_pre" "available_chemicals_directory" "inter_bioscreen_s" "inter_bioscreen_n" "inter_bioscreen_n_300" "inter_bioscreen_n_500" "biomolecular_research_institute" "molecular_science" "molecular_diversity_preservation" "national_cancer_institute" "IGF_HITS" "aldrich_300" "molecular_science_500" "APP" "ECE" default "aldrich_300";
parameter score_ligand text default "yes";
parameter minimize_ligand text default "yes";
parameter multiple_ligands text default "no";
parameter random_seed integer default 7;
parameter anchor_search text default "no";
parameter torsion_drive text default "yes";
parameter clash_overlap float default 0.5;
parameter conformation_cutoff_factor integer default 5;
parameter torsion_minimize text default "yes";
parameter match_receptor_sites text default "no";
parameter random_search text default "yes";
parameter maximum_cycles integer default 1;
parameter receptor_site_file text default "ece.sph";
parameter score_grid_prefix text default "ece";
parameter ligand_number integer range from 1 to 200 step 1;
Molecules to be screened

80. Dock PlanFile task nodestart copy ./parameter/vdw.defn node:.
copy ./parameter/chem.defn node:.
copy ./parameter/chem_score.tbl node:.
copy ./parameter/flex.defn node:.
copy ./parameter/flex_drive.tbl node:.
copy ./dock_inputs/get_molecule node:.
copy ./dock_inputs/dock_base node:.
endtask
task main
node:substitute dock_base dock_run
node:substitute get_molecule get_molecule_fetch
node:execute sh ./get_molecule_fetch
node:execute $HOME/bin/dock.$OS -i dock_run -o dock_out
copy node:dock_out ./results/dock_out.$jobname
copy node:dock_cnt.mol2 ./results/dock_cnt.mol2.$jobname
copy node:dock_chm.mol2 ./results/dock_chm.mol2.$jobname
copy node:dock_nrg.mol2 ./results/dock_nrg.mol2.$jobname

81. Nimrod/TurboLinux enFuzion GUI tools for Parameter Modeling

82. Docking Experiment Preparation
Setup PDB DataGrid
Index PDB databases
Pre-stage (all) Protein Data Bank (PDB) on replica sites
Start PDB Server
Create Docking GridScore (receptor surface details) for a given receptor on home node.
Pre-Staging Large Files required for Docking:
Pre-stage Dock executables and PDB access client on Grid nodes, if required (e.g., dock.Linux, dock.SunOS, dock.IRIX64, and dock.OSF1 on Linux, Sun, SGI, and Compaq machines respectively). Use globus-rcp.
Pre-stage/Cache all data files (~3-13MB each) representing receptor details on Grid nodes.
This can can be done demand by Nimrod/G for each job, but few input files are too large and they are required for all jobs). So, pre-staging/caching at http-cache or broker level is necessary to avoid the overhead of copying the same input files again and again!

83. Protein Data Bank
Databases consist of small molecules from commercially available organic synthesis libraries, and natural product databases.
There is also the ability to screen virtual combinatorial databases, in their entirety.
This methodology allows only the required compounds to be subjected to physical screening and/or synthesis reducing both time and expense.

84. Target Testcase
The target for the test case: electrocardiogram (ECE) endothelin converting enzyme. This is involved in “heart stroke” and other transient ischemia.
Is·che·mi·a : A decrease in the blood supply to a bodily organ, tissue, or part caused by constriction or obstruction of the blood vessels.

85. Nimrod/G in Action: Screening on World-Wide Grid

86. Any Scientific Discovery
Any Scientific Discovery ? Did your collaborator invent new drug for xxxx?
Anyway, checkout the announcement of Nobel-prize winners for next year
Not Yet ?

87. Conclude with a comparison with the Electrical Grid………..
Where we are ????
Courtesy: Domenico Laforenza

88. Fresco by N. Cianfanelli (1841)
Alessandro Volta in Paris in 1801 inside French National Institute shows the battery while in the presence of Napoleon I
Fresco by N. Cianfanelli (1841)
(Zoological Section "La Specula" of National History Museum of Florence University)

89. What ?!?! Oh, mon Dieu ! This is a mad man… ….and in the future,
I imagine a worldwide
Power (Electrical) Grid …...
Oh, mon
Dieu !
What ?!?!
This is a mad man…

90. = 200 Years
2001
1801

91. Grid Computing: A New Wave ?
Can we Predict its Future ?
” I think there is a world market for about five computers.”
Thomas J. Watson Sr., IBM Founder, 1943

92. Summary and Conclusions
P2P and Grid Computing is emerging as a next generation computing platform for solving large scale problems through sharing of geographically distributed resources.
Resource management is a complex undertaking as systems need to be adaptive, scalable, competitive,…, and driven by QoS.
We proposed a framework based on “computational economies” and discussed several economic models for resource allocation and for regulating supply-and-demand for resources.
Scheduling experiments on World Wide Grid demonstrate our Nimrod-G broker ability to dynamically lease or rent services at runtime based on their quality, cost, and availability depending on consumers QoS requirements.
Economics paradigm for QoS driven resource management is essential to push P2P/Grids into mainstream computing!

93. Download Software & Information
Nimrod & Parameteric Computing:
Economy Grid & Nimrod/G:
Virtual Laboratory/Virtual Drug Design:
Grid Simulation (GridSim) Toolkit (Java based):
World Wide Grid (WWG) testbed:
Looking for new volunteers to grow 
Please contact me to barter your & our machines!
Want to build on our work/collaborate:
Talk to me now or

94. Thank You… Any ??

95. Further Information Books: IEEE Task Force on Cluster Computing
High Performance Cluster Computing, V1, V2, R.Buyya (Ed), Prentice Hall, 1999.
The GRID, I. Foster and C. Kesselman (Eds), Morgan-Kaufmann, 1999.
IEEE Task Force on Cluster Computing
Global Grid Forum
IEEE/ACM CCGrid’xy:
CCGrid 2002, Berlin: ccgrid2002.zib.de
Grid workshop -

96. Further Information Cluster Computing Info Centre:
Grid Computing Info Centre:
IEEE DS Online - Grid Computing area:
Compute Power Market Project