1. Weaving the World-Wide Grid [Marketplace]: Rajkumar Buyya Melbourne, Australia www.buyya.com/ecogrid WW Grid “ Economic Paradigm for Distributed Resource Management and Scheduling for Service-Oriented Computing ”

2. 2

3. 3 Vision: Grid for Service Oriented Computing? WW Grid World Wide Grid! Nimrod-G

4. 4 Overview A quick glance at Grid computing Resource Management challenges for next generation Grid computing A Glance at Approaches to Grid computing. Grid Architecture for Computational Economy Nimrod-G -- Grid Resource Broker Scheduling Experiments on the World Wide Grid: both Real and Simulation Conclusions SchedulingEconomics Grid Economy Grid

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

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

7. 7 What is Grid ? A paradigm/infrastructure that allows sharing, selection, & aggregation of geographically 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/instruments – e.g., radio telescope – SETI@Home searching for life in galaxy. People/collaborators. [depending on their availability, capability, cost, and user QoS requirements] for solving large-scale problems/applications.  Thus enabling the creation of “ virtual enterprises ” (VEs) Wide area

8. 8 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: 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 Competitive Utility: New paradigm, new industries, and new business.

9. 9 Building and Using Grids require Services that enable the execution of a job on a resource in different admistrative domain. Security mechanisms that permit resources to be accessed only by authorized users. App/Data Security (?) – A must for commercial users (protecting from GSPs/other 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 Nimrod-G

10. Resource Management Challenges in Grid Computing Environments

11. 11 A Typical Grid Computing Environment Grid Resource Broker Resource Broker Application Grid Information Service Grid Resource Broker database R2R2 R3R3 RNRN R1R1 R4R4 R5R5 R6R6 Grid Information Service

12. 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. 13 Sources of Complexity in Grid for Resource Management and Scheduling 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

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

15. 15 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. “ Economic ” paradigm proved as an effective institution in managing decentralization and heterogeneity that is present in human economies! Hence, we propose/advocate the use of “ computational economy ” principles in the management of resources and scheduling computations on the Grid.

16. 16 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.

17. 17 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 ?

18. 18 mix-and-match Object-oriented Internet/partial-P2P Network enabled Solvers Market/Computational Economy

19. 19 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)!

20. 20 Grid Node N GRACE: A Reference Grid Architecture for Computational Economy Grid Consumer Programming Environments Grid Resource Broker Grid Service Providers Grid Explorer Schedule Advisor Trade Manager Job Control Agent Deployment Agent Trade Server Resource Allocation Resource Reservation R1R1 Misc. services Information Service R2R2 RmRm … Pricing Algorithms Accounting Grid Node1 … Grid Middleware Services … … Health Monitor Grid Market Services JobExec Info ? Secure Trading QoS Storage Sign-on Grid Bank Applications

21. 21 Grid Node N GRACE: A Reference Grid Architecture for Computational Economy Grid User Application Grid Resource Broker Grid Service Providers Grid Explorer Schedule Advisor Trade Manager Job Control Agent Deployment Agent Trade Server Resource Allocation Resource Reservation R1R1 Misc. services Information Server(s) R2R2 RmRm … Pricing Algorithms Accounting Grid Node1 … Grid Middleware Services … … Health Monitor Grid Market Services JobExec Info ? Secure Trading QoS Storage Sign-on Grid Bank See PDPTA 2000 paper!

22. 22 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!

23. 23 Cost Model Without cost model 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

24. 24 Cost Matrix @ Grid site X Non-uniform costing Encourages use of local resources first Real accounting system can control machine usage 13 21 User 5 Machine 1 User 1 Machine 5 Resource Cost = Function (cpu, memory, disk, network, software, QoS, current demand, etc.) Simple: price based on peaktime, offpeak, discount when less demand,..

25. Nimrod-G: The Grid Resource Broker Soft Deadline and Budget-based Economy Grid Resource Broker for Parameter (Task Farming Applications) Processing on Grids

26. 26 A resource broker for managing, steering, and executing task farming (parameter 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 Nimrod/G : A Grid Resource Broker

27. 27 Parametric Computing (What Users think of Nimrod Power) Multiple Runs Same Program Multiple Data Killer Application for the Grid! Parameters Courtesy: Anand Natrajan, University of Virginia Magic Engine

28. 28 Sample P-Sweep/Task Farming Applications Bioinformatics: Drug Design / Protein Modelling Bioinformatics: Drug Design / Protein Modelling Sensitivity experiments on smog formation Combinatorial Optimization: Meta-heuristic parameter estimation Ecological Modelling: Control Strategies for Cattle Tick Electronic CAD: Field Programmable Gate Arrays Computer Graphics: Ray Tracing High Energy Physics: Searching for Rare Events Finance: Investment Risk Analysis VLSI Design: SPICE Simulations Aerospace: Wing Design Network Simulation Automobile: Crash Simulation Data Mining Civil Engineering: Building Design astrophysics

29. 29 Distributed Drug Design: Data Intensive Computing on the Grid A Virtual Laboratory environment for “ Molecular Docking for Drug Design ” on the Grid. It provides tools for screening millions of chemical compounds (molecules) in the Chemical DataBase (CDB) to identify those having potential use in drug design (acts as inhibitor). In collaboration with: Kim Branson, Structural Biology, Walter and Eliza Hall Institute (WEHI) http://www.buyya.com/vlab

30. 30 Docking Application Input data configuration file score_ligand yes minimize_ligand yes multiple_ligands no random_seed 7 anchor_search no torsion_drive yes clash_overlap 0.5 conformation_cutoff_factor 3 torsion_minimize yes match_receptor_sites no random_search yes...... maximum_cycles 1 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

31. 31 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 Parameterize Dock input file (use Nimrod Tools: GUI/language) Molecule to be screened

32. 32 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 CDB_SERVER text default "bezek.dstc.monash.edu.au"; parameter CDB_PORT_NO text default "5001"; 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 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 2000 step 1; Create Dock PlanFile 1. Define parameters and their value Molecules to be screened

33. 33 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 endtask Create Dock PlanFile 2. Define the task that each job needs to do

34. 34 Nimrod-G Broker Automating Distributed Processing Compose, Submit, & Play!

35. 35 Nimrod & Associated Family of Tools 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 Remote Execution Server (on demand Nimrod Agent) File Transfer Server

36. 36 A Glance at Nimrod-G Broker Grid Middleware Nimrod/G Client Grid Information Server(s) Schedule Advisor Trading Manager Nimrod/G Engine Grid Store Grid Explorer GE GIS TM TS RM & TS Grid Dispatcher RM: Local Resource Manager, TS: Trade Server Globus, Legion, Condor, etc. G G C L Globus enabled node. Legion enabled node. G L Condor enabled node. RM & TS CL See HPCAsia 2000 paper!

37. 37 GlobusLegion Fabric Nimrod-G Broker Nimrod-G Clients P-Tools (GUI/Scripting) (parameter_modeling) Legacy Applications P2PGTS Farming Engine Dispatcher & Actuators Schedule Advisor Trading Manager Grid Explorer Customised Apps (Active Sheet) Monitoring and Steering Portals Algorithm1 AlgorithmN Middleware... ComputersStorageNetworksInstrumentsLocal Schedulers G-Bank... Agents Resources Programmable Entities Management JobsTasks... AgentSchedulerJobServer PC/WS/ClustersRadio TelescopeCondor/LL/NQS... Database Meta-Scheduler Nimrod/G Grid Broker Architecture Channels... Database CondorGMD IP hourglass! Condor-AGlobus-ALegion-AP2P-A

38. 38 A Nimrod/G Monitor CostDeadline Legion hosts Globus Hosts Bezek is in both Globus and Legion Domains

39. 39 User Requirements: Deadline/Budget

40. 40 Another User Interface: Active Sheet for Spreadsheet Processing on Grid NimrodProxy Nimrod/G

41. 41

42. 42 Nimrod/G Interactions Grid Info Server Process Server User Process File access File Server Grid Node Nimrod Agent Compute Node User Node Grid Dispatcher Grid Trade Server Grid Scheduler Local Resource Manager Nimrod-G Grid Broker Task Farming Engine Grid Tools And Applications Do this in 30 min. for $10?

43. 43 Discover Resources Distribute Jobs Establish Rates Meet requirements ? Remaining Jobs, Deadline, & Budget ? Evaluate & Reschedule Discover More Resources Adaptive Scheduling Steps Compose & Schedule

44. 44 Deadline and Budget Constrained Scheduling Algorithms Algorithm/ Strategy Execution Time (Deadline, D) Execution Cost (Budget, B) Cost OptLimited by DMinimize Cost-Time OptMinimize when possible Minimize Time OptMinimizeLimited by B Conservative-Time Opt MinimizeLimited by B, but all unprocessed jobs have guaranteed minimum budget

45. Application Scheduling Experiments on the World- Wide Grid Task Farming Applications on World Wide Grid WW Grid

46. 46 The World Wide Grid Sites WW Grid EUROPE: ZIB/Germany PC 2 /Germany AEI/Germany Lecce/Italy CNR/Italy Calabria/Italy Pozman/Poland Lund/Sweden CERN/Swiss CUNI/Czech R. Vrije: Netherlands EUROPE: ZIB/Germany PC 2 /Germany AEI/Germany Lecce/Italy CNR/Italy Calabria/Italy Pozman/Poland Lund/Sweden CERN/Swiss CUNI/Czech R. Vrije: Netherlands ANL/Chicago USC-ISC/LA UTK/Tennessee UVa/Virginia Dartmouth/NH BU/Boston UCSD/San Diego ANL/Chicago USC-ISC/LA UTK/Tennessee UVa/Virginia Dartmouth/NH BU/Boston UCSD/San Diego Monash/Melbourne VPAC/Melbourne Monash/Melbourne VPAC/Melbourne Santiago/Chile TI-Tech/Tokyo ETL/Tsukuba AIST/Tsukuba TI-Tech/Tokyo ETL/Tsukuba AIST/Tsukuba Cardiff/UK Portsmoth/UK Manchester, UK Cardiff/UK Portsmoth/UK Manchester, UK Kasetsart/Bangkok Singapore

47. 47 World Wide Grid (WWG) WW Grid Globus+Legion GRACE_TS Australia Monash U. : Cluster VPAC: Alpha Solaris WS Nimrod/G Globus + GRACE_TS Europe ZIB: T3E/Onyx AEI: Onyx Paderborn: HPCLine Lecce: Compaq SC CNR: Cluster Calabria: Cluster CERN: Cluster CUNI/CZ: Onyx Pozman: SGI/SP2 Vrije U: Cluster Cardiff: Sun E6500 Portsmouth: Linux PC Manchester: O3K Globus + GRACE_TS Asia Tokyo I-Tech.: Ultra WS AIST, Japan: Solaris Cluster Kasetsart, Thai: Cluster NUS, Singapore: O2K Globus/Legion GRACE_TS North America ANL: SGI/Sun/SP2 USC-ISI: SGI UVa: Linux Cluster UD: Linux cluster UTK: Linux cluster UCSD: Linux PCs BU: SGI IRIX Internet Globus + GRACE_TS South America Chile: Cluster WW Grid

48. 48 Experiment-1: Peak and Off-peak Workload: 165 jobs, each need 5 minute of cpu time Deadline: 1 hrs. and budget: 800,000 units Strategy: Minimize Cost and meet the deadline Execution Cost with cost optimisation AU Peaktime:471205 (G$) AU Offpeak time: 427155 (G$)

49. 49 Resources Selected & Price/CPU-sec. Resource Type & Size Owner and Location Grid servicesPeaktime Cost (G$) Offpeak cost Linux cluster (60 nodes) Monash, Australia Globus/Condor205 IBM SP2 (80 nodes) ANL, Chicago, US Globus/LL510 Sun (8 nodes)ANL, Chicago, US Globus/Fork510 SGI (96 nodes)ANL, Chicago, US Globus/Condor-G15 SGI (10 nodes)ISI, LA, USGlobus/Fork1020

50. 50 Execution @ AU Peak Time

51. 51 Execution @ AU Offpeak Time

52. 52 Experiment-2 Setup Workload: 165 jobs, each need 5 minute of CPU time Deadline: 2 hrs. and budget: 396000 G$ Strategies: 1. Minimise cost 2. Minimise time Execution: Optimise Cost: 115200 (G$) (finished in 2hrs.) Optimise Time: 237000 (G$) (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.

53. 53 Resources Selected & Price/CPU-sec. Resource & Location Grid services & Fabric Cost/CPU sec.or unit No. of Jobs Executed Time_OptCost_Opt. Linux Cluster-Monash, Melbourne, Australia Globus, GTS, Condor 264153 Linux-Prosecco-CNR, Pisa, Italy Globus, GTS, Fork 371 Linux-Barbera-CNR, Pisa, Italy Globus, GTS, Fork 461 Solaris/Ultas2 TITech, Tokyo, Japan Globus, GTS, Fork 391 SGI-ISI, LA, US Globus, GTS, Fork 8375 Sun-ANL, Chicago,US Globus, GTS, Fork 7424 Total Experiment Cost (G$)237000115200 Time to Complete Exp. (Min.)70119

54. 54 Resource Scheduling for DBC Time Optimization

55. 55 Resource Scheduling for DBC Cost Optimization

56. 56 Experiment-3 Setup: Using GridSim Workload Synthesis: 200 jobs, each job processing requirement = 10K MI or SPEC with random variation from 0-10%. Exploration of many scenarios: Deadline: 100 to 3600 simulation time, step = 500 Budget: 500 to 22000 G$, step = 1000 DBC Strategies: Cost Optimisation Time Optimisation Resources: Simulated WWG resources

57. 57 Simulated WWG Resources

58. 58 DBC Cost Optimisation

59. 59 DBC Time Optimisation

60. 60 Comparison: D = 3100, B varied Time Opt Execution Time vs. Budget Execution Cost vs. Budget Cost Opt

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

62. 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)

63. 63 ….and in the future, I imagine a Worldwide Power (Electrical) Grid …... What ?!?! This is a mad man… Oh, mon Dieu !

64. 64 2002 - 1801 = 201 Years

65. 65 Electric Grid Management and Delivery methodology is highly advanced Central Grid Regional Grid Local Grid Production Utility Consumption Whereas, our Computational Grid is in primitive/infancy state?

66. 66 ” I think there is a world market for about five computers.” Thomas J. Watson Sr., IBM Founder, 1943 Can we Predict its Future ?

67. 67 Summary and Conclusion 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 ” for resource allocation and for regulating supply-and-demand for resources. Scheduling experiments on the World Wide Grid demonstrate our Nimrod-G broker ability to dynamically lease services at runtime based on their quality, cost, and availability depending on consumers QoS requirements. Easy to use tools for creating Grid applications are essential to attracting and getting application community on board. The use of economic paradigm for resource management and scheduling is essential for pushing Grids into mainstream computing and weaving the World-Wide Grid Marketplace!

68. 68 Download Software & Information Nimrod & Parameteric Computing: http://www.csse.monash.edu.au/~davida/nimrod/ Economy Grid & Nimrod/G: http://www.buyya.com/ecogrid/ Virtual Laboratory Toolset for Drug Design: http://www.buyya.com/vlab/ Grid Simulation (GridSim) Toolkit (Java based): http://www.buyya.com/gridsim/ World Wide Grid (WWG) testbed: http://www.buyya.com/ecogrid/wwg/ Cluster and Grid Info Centres: www.buyya.com/cluster/ || www.gridcomputing.com

69. 69 Selected GridSim Users!

70. 70 Final Word?

71. Backup Slides

72. 72 Further Information Books: 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 http://www.ieeetfcc.org Global Grid Forum www.gridforum.org IEEE/ACM CCGrid ’ xy: www.ccgrid.org CCGrid 2002, Berlin: ccgrid2002.zib.de Grid workshop - www.gridcomputing.org

73. 73 Further Information Cluster Computing Info Centre: http://www.buyya.com/cluster/ Grid Computing Info Centre: http://www.gridcomputing.com IEEE DS Online - Grid Computing area: http://computer.org/dsonline/gc Compute Power Market Project http://www.ComputePower.com

74. 74

75. 75 Deadline and Budget-based Cost Minimization Scheduling 1. Sort resources by increasing cost. 2. For each resource in order, assign as many jobs as possible to the resource, without exceeding the deadline. 3. Repeat all steps until all jobs are processed.

76. 76 Deadline and Budget Constraint (DBC) Time Minimization Scheduling 1. For each resource, calculate the next completion time for an assigned job, taking into account previously assigned jobs. 2. Sort resources by next completion time. 3. Assign one job to the first resource for which the cost per job is less than the remaining budget per job. 4. Repeat all steps until all jobs are processed. (This is performed periodically or at each scheduling-event.)

77. 77 DBC Conservative Time Min. Scheduling 1. Split resources by whether cost per job is less than budget per job. 2. 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). 3. For the dearer resources, repeat all steps (with a recalculated budget per job) until all jobs are assigned. 4. [Schedule/Reschedule] Repeat all steps until all jobs are processed.

78. 78 M - Resources, N - Jobs, D - deadline Note: Cost of any R i is less than any of R i+1 …. Or Rm RL: Resource List need to be maintained in increasing order of cost C t - Time when accessed (Time now) T i - Job runtime (average) on Resource i (R i ) [updated periodically] T i is acts as a load profiling parameter. A i - number of jobs assigned to R i, where: A i = Min (No.Unassigned Jobs, No. Jobs R i can complete by remaining deadline) No.UnAssignedJobs i = Diff( N, (A 1 + … +A i-1 )) JobsR i consume = RemainingTime (D- C t ) DIV T i ALG: Invoke Job Assignment() periodically until all jobs done. Job Assignment()/Reassignment(): Establish ( RL, C t, T i, A i ) dynamically – Resource Discovery. For all resources (I = 1 to M) { Assign A i Jobs to R i, if required} Deadline-based Cost- minimization Scheduling

79. 79 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 – SETI@Home searching for life in galaxy. People/collaborators.  and presents them as an integrated global resource for solving large-scale problems.  It enables the creation of virtual enterprise (VE) for resource sharing and aggregation. Wide area data archives

80. 80 Virtual Enterprise  A temporary alliance of enterprises or organizations that come together to share resources and skills, or competencies in order to better respond to business opportunities or challenges, and who cooperation is supported by computer networks.

81. 81 Many Testbeds ? & who pays ?, who regulates supply and demand ? GUSTO (decommissioned) Legion Testbed NASA IPG World Wide Grid WW Grid

82. 82 Testbeds so far -- observations Who contributed resources & why ? Volunteers: for fun, challenge, fame, charismatic apps, public good like distributed.net & SETI@Home projects.SETI@Home 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.

83. 83 Grid Open Trading Protocols Get Connected Call for Bid(DT) Reply to Bid (DT) Negotiate Deal(DT) Confirm Deal(DT, Y/N) …. Cancel Deal(DT) Change Deal(DT) Get Disconnected Trade Manager Trade Server Pricing Rules DT - Deal Template: - resource requirements (TM) - resource profile (TS) - price (any one can set) - status - change the above values - negotiation can continue - accept/decline - validity period API

84. 84 Layered Grid Architecture Networked Resources across Organizations Computers NetworksData SourcesScientific InstrumentsStorage Systems Local Resource Managers Operating Systems Queuing Systems Internet Protocols Libraries & App Kernels Distributed Resources Coupling Services InformationQoSProcess Development Environments and Tools Languages/CompilersLibrariesDebuggersWeb tools Resource Management, Selection, and Aggregation (BROKERS) Applications and Portals Prob. Solving Env. Scientific … Collaboration Engineering Web enabled Apps Trading … … … … FABRIC APPLICATIONS SECURITY LAYER Security Data CORE MIDDLEWARE USER LEVEL MIDDLEWARE Monitors

85. 85 Grid Fabric Grid Apps. Grid Middleware Grid Tools Networked Resources across Organisations Computers Clusters Data Sources Scientific Instruments Storage Systems Local Resource Managers Operating Systems Queuing Systems TCP/IP & UDP … Libraries & App Kernels … Distributed Resources Coupling Services SecurityInformation … QoS Process Development Environments and Tools Languages Libraries Debuggers … Web tools Resource BrokersMonitoring Applications and Portals Prob. Solving Env. Scientific … Collaboration Engineering Web enabled Apps Resource Trading Grid Components Market Info

86. 86 Economy Grid = Globus + GRACE Applications GRAM Globus Security Interface (GSI) Local Services LSF Condor GRDQBank PBS TCP SolarisIrixLinux UDP High-level Services and Tools CactusMPI-G Nimrod-G Broker CC++ GASSGTSGARA Grid Fabric Grid Apps. Grid Middleware Grid Tools GBank GMD eCash JVM DUROC Core Services ScienceEngineeringCommercePortalsActiveSheet … … … … MDS Higher Level Resource Aggregators Nimrod Parametric Language

87. 87 Virtual Drug Design A Virtual Lab for “ Molecular Modeling for Drug Design ” on P2P Grid “Screen 2K molecules in 30min. for $10” Grid Market Directory Resource Broker Grid Info. Service GTS “Give me list PDBs sources Of type aldrich_300?” “service cost?” (GTS - Grid Trade Server) PDB2 “get mol.10 from pdb1 & screen it.” Data Replica Catalogue “service providers?” GTS PDB1 “mol.10 please?” “mol.5 please?” (RB maps suitable Grid nodes and Protein DataBank)

88. 88 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

89. 89 Many Grid Projects & Initiatives Australia Nimrod-G GridSim Virtual Lab Active Sheets DISCWorld..new coming up Europe UNICORE MOL UK eScience Poland MC Broker EU Data Grid EuroGrid MetaMPI Dutch DAS XW, JaWS and many more... Japan Ninf DataFarm and many more... USA Globus Legion OGSA Javelin AppLeS NASA IPG Condor-G Jxta NetSolve AccessGrid and many more... Cycle Stealing &.com Initiatives Distributed.net SETI@Home, …. SETI@Home Entropia, UD, Parabon, …. Public Forums Global Grid Forum P2P Working Group IEEE TFCC Grid & CCGrid conferences http://www.gridcomputing.com

90. 90 Using Pure Globus/Legion commands Do all yourself! (manually) Total Cost:$???

91. 91 Build Distributed Application & Scheduler Build App case by case basis Complicated Construction E.g., AppLeS/MPI basedTotal Cost:$???

92. 92 Experiment-3 Setup Workload: 200 jobs, each need 10 minute of CPU time Deadline: 4 hrs. and budget: 250,000 G$ Strategies: 1. Minimise cost 2. Minimise time Execution: Optimise Cost: 141,869 (G$) (finished in 150min./2.5hrs) Optimise Time: 199,968 (G$) (finished in 250min.) In this experiment: Time-optimised scheduling run costs double that of Cost-optimised. Users can now trade-off between Time Vs. Cost.

93. 93

94. 94 Jobs Completed for DBC Time Optimization

95. 95 Jobs Completed for DBC Cost Optimization

96. 96 Active Sheet: Microsoft Excel Spreadsheet Processing on Grid NimrodProxy Nimrod-G World-Wide Grid