Presentation on theme: "An Overview Grid Computing and Applications Subject Code: 433-498 Rajkumar Buyya Grid Computing and Distributed Systems (GRIDS) Lab. The University of."— Presentation transcript:
An Overview Grid Computing and Applications Subject Code: 433-498 Rajkumar Buyya Grid Computing and Distributed Systems (GRIDS) Lab. The University of Melbourne Melbourne, Australia www.gridbus.org WW Grid
Overview Computing platforms and how the Grid is different ? Towards global (Grid) computing. Grid resource management and scheduling. Application development challenges. Approaches to Grid computing. Grid applications Grid Projects in GRIDS Lab@ Melbourne Summary and conclusions
Major Networking and Computing Technologies Introduction 19601970197519801985199019952000 Technologies Introduced * ARPANET * Email * Ethernet * TCP/IP * IETF * Internet Era* WWW Era * Mosaic * XML * PC Clusters * Crays * MPPs * Mainframes * HTML * W3C * P2P * Grids * XEROX PARC worm COMPUTING NETWORKING * Web Services * Minicomputers* PCs * WS Clusters * PDAs * Workstations * HTC
Internet: Past, Present, Future TCP/IP HTML Mosaic XML PHASE 1. Packet Switching Networks 2. The Internet isBorn3. The World Wide Web 4.with XML 5. The Grid 1969: 4 US Universities linked to form ARPANET TCP/IP becomes core protocol HTML hypertext system created 1972: First e-mail program created Domain Name System created IETF created (1986) CERN launch World Wide Web 1976: Robert Metcalfe develops Ethernet NCSA launch Mosaic interface 0 20 40 60 80 100 120 140 1965197019751980198519901995200020052010 The 'Network Effect kicks in, and the web goes critical' Number of hosts (millions)
Internet and WWW Growth 1 10,000 100,000 1,000,000 10,000,000 19691970197519801985199019952000 10 100 1,000 4 Internet Hosts WWW Servers
Installed base and Growth rate for telephone lines, mobile phones, & Internet hosts - 1995 Installed, 1995 1994-95 Growth Rates (%) Income Group/ Phone MobileInternet Phone Mobile Internet RegionLines Phones HostsLines Phones Hosts Lower Income2.00.121.3535.7135.1246.0 Lower- Middle9.10.3373.318.7105.1167.0 Upper - Middle 14.51.34380.136.466.8111.9 High53.28.7010749.233.655.697.0 Africa1.70.0969.147.960.581.4 Americas29.05.178359.585.442.391.5 Asia5.40.62121.7014.7108.3150.0 Europe33.03.042732.243.659.5112.2 Oceans39.79.5512845.554.085.788.8 World12.11.561661.897.060.497.8 Source: ACM, Nov, 97 (phones, international telecommunication union, hosts, network Wizards
2100 DesktopSMPs or SuperComputers Local Cluster Global Cluster/Grid PERFORMANCEPERFORMANCE Inter Planet Cluster/Grid ?? Individual Group Department Campus State National Globe Inter Planet Universe Administrative Barriers Enterprise Cluster/Grid ? Scalable HPC: Breaking Administrative Barriers
Why Grids ? Large Scale Exploration needs themKiller Applications. Solving grand challenge applications using computer modeling, simulation and analysis Life Sciences CAD/CAM Aerospace Military Applications Digital Biology Military Applications Internet & Ecommerce
Cluster of Clusters - Hyperclusters Scheduler Master Daemon Execution Daemon Submit Graphical Control Clients Cluster 2 Scheduler Master Daemon Execution Daemon Submit Graphical Control Clients Cluster 3 Scheduler Master Daemon Execution Daemon Submit Graphical Control Clients Cluster 1 LAN/WAN
Grid: Towards Internet Computing for (Coordinated) Resource Sharing - Unification of geographically distributed resources http://www.sun.com/hpc/ Grid enables: c Resource Sharing c Selection c Aggreation
What is Grid ? A paradigm/infrastructure that enabling the 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. Wide area
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.
Building and Using Grids requires... 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 into requirements for computers, networks, and storage. Tools that perform resource discovery, trading, composition, scheduling and distribution of jobs and collects results.
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
Sources of Complexity in Resource Management for World Wide Computing Size (large number of nodes, providers, consumers) Heterogeneity of resources (PCs, Workstatations, clusters, and supercomputers) Heterogeneity of fabric management systems (single system image OS, queuing systems, etc.) Heterogeneity of fabric management polices Heterogeneity of applications (scientific, engineering, and commerce) Heterogeneity of application requirements (CPU, I/O, memory, and/or network intensive) Heterogeneity in demand patters Geographic distribution and different time zones Differing goals (producers and consumers have different objectives and strategies) Unsecure and Unreliable environment
Traditional approaches to resource management 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: system-wide performance matrix and common fabric management policy that is acceptable to all. So, we propose the usage of economics paradigm for managing resources proved successful in managing decentralization and heterogeneity that is present in human economies! We can easy leverage proven Economic principles and techniques Easy to regulate demand and supply User-centric, scalable, adaptable, value-driven costing, etc. Offers incentive (money?) for being part of the grid!
Grid Resource Management systems need to ensure/provide: Site autonomy. Heterogeneous resources and substrate: Each resource can be different – SMPs, Clusters, Linux, UNIX, Windows, Intel, etc. Resource owners have their own policies or scheduling mechanisms (Codine/Condor). Extend policies, through resource brokers. Resource allocation/co-allocation Online control - can apps (Graphics) tolerate non- availability of a resource and adapt themselves?
Grid RMS to support Ack: Globus.. Authentication (once). Specify (code, resources, etc.). Discover resources. Negotiate authorization, acceptable use, Cost, etc. Acquire resources. Schedule Jobs. Initiate computation. Steer computation. Access remote data-sets. Collaborate with results. Account for usage. Discover resources. Negotiate authorisation, acceptable use, Cost, etc. Acquire resources. Schedule jobs. Initiate computation. Steer computation. Domain 2 Domain 1
Local Resource Mgr Resource Brokers Application Local Resource Mgr RSL (RSL Specialization) Information Service - MDS Resource Co-allocators Resource Management Architecture
Many Grid Projects & Initiatives Australia Nimrod-G GridSim Virtual Lab Gridbus DISCWorld..new coming up Europe UNICORE MOL UK eScience Poland MC Broker EU Data Grid EuroGrid MetaMPI Dutch DAS XW, JaWS Japan Ninf DataFarm Korea... N*Grid 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
Table 4: Major European Grid Computing Efforts InitiativeFocus and Technologies Developed UNICORE The UNiform Interface to Computer Resources aims to deliver software that allows users to submit jobs to remote high performance computing resources – www.fz-juelich.de/unicorewww.fz-juelich.de/unicore MOL Metacomputer OnLine is a toolbox for the coordinated use of WAN/LAN connected systems. MOL aims at utilizing multiple WAN-connected high performance systems for solving large-scale problems that are intractable on a single supercomputer – www.uni-paderborn.de/pc2/projects/molwww.uni-paderborn.de/pc2/projects/mol METODIS Metacomputing Tools for Distributed Systems – www.hlrs.de/structure/organisation/par/projects/metodis/ www.hlrs.de/structure/organisation/par/projects/metodis/ Globe Globe is a research project aiming to study and implement a powerful unifying paradigm for the construction of large-scale wide area distributed systems: distributed shared objects – www.cs.vu.nl/~steen/globe www.cs.vu.nl/~steen/globe Pozan Poznan Centre works on development of tools and methods for metacomputing - www.man.poznan.pl/metacomputing/ www.man.poznan.pl/metacomputing/ Date Grid This project aims to develop middleware and tools necessary for the data-intensive applications of high-energy physics – grid.web.cern.ch/gridgrid.web.cern.ch/grid MetaMPI MetaMPI supports the coupling of heterogeneous MPI systems, thus allowing parallel applications developed using MPI to be run on Grids without alteration – www.lfbs.rwth- aachen.de/~martin/MetaMPICH/www.lfbs.rwth- aachen.de/~martin/MetaMPICH/ DAS This is a wide-area distributed cluster, used for research on parallel and distributed computing by five Dutch universities – www.cs.vu.nl/daswww.cs.vu.nl/das JaWs JaWS is an economy-based computing model where both resource owners and programs using these resources place bids to a central marketplace that generates leases of use – roadrunner.ics.forth.grroadrunner.ics.forth.gr
InitiativeFocus and Technologies Developed Globus This project is developing basic software infrastructure for computations that integrate geographically distributed computational and information resources – www.globus.orgwww.globus.org Legion Legion is an object-based metasystem. Legion supports transparent scheduling, data management, fault tolerance, site autonomy, and a wide range of security options – www.legion.virginia.eduwww.legion.virginia.edu Javelin Javelin: Internet-based parallel computing using Java – www.cs.ucsb.edu/research/javelin/www.cs.ucsb.edu/research/javelin AppLes This is an application-specific approach to scheduling individual parallel applications on production heterogeneous systems – www.infospheres.caltech.edu/www.infospheres.caltech.edu/ NASA IPG The Information Power Grid is a testbed that provides access to a Grid – a widely distributed network of high performance computers, stored data, instruments, and collaboration environments – www.ipg.nasa.govwww.ipg.nasa.gov Condor This project aims is to develop, deploy, and evaluate mechanisms and policies that support high throughput computing (HTC) on large collections of distributed computing resources – www.cs.wisc.edu/condor/www.cs.wisc.edu/condor/ Harness Harness builds on the concept of the virtual machine and explores dynamic capabilities beyond what PVM can supply. It focused on developing three key capabilities: Parallel plug-ins, Peer-to-peer distributed control, and multiple virtual machines – www.epm.ornl.org/harnesswww.epm.ornl.org/harness NetSolve NetSolve is a project that aims to bring together disparate computational resources connected by computer networks. It is a RPC based client/agent/server system that allows one to remotely access both hardware and software components – www.cs.utk.edu/netsolve/www.cs.utk.edu/netsolve/ Grid Port SDSCs Grid Port Toolkit generalises the HotPage infrastructure to develop a reusable portal toolkit – gridport.npaci.edu/ gridport.npaci.edu/ HotPage NPACIs HotPage is a user portal that is designed to be a single point-of-access to computer resources – hotpage.npaci.edu/ hotpage.npaci.edu/ Gateway Gateway offers a programming paradigm implemented over a virtual Web of accessible resources - www.npac.syr.edu/users/haupt/WebFlow/demo.html www.npac.syr.edu/users/haupt/WebFlow/demo.html
InitiativeFocus and Technologies Developed NinfNinf allows users to access computational resources including hardware, software and scientific data distributed across a wide area network with an easy-to-use interface – ninf.etl.go.jpninf.etl.go.jp BricksBricks is a performance evaluation system that allows analysis and comparison of various scheduling schemes on a typical high- performance global computing setting – matsu- www.is.titech.ac.jp/~takefusa/bricksmatsu- www.is.titech.ac.jp/~takefusa/bricks
InitiativeFocus and Technologies Developed DISCWorldAn infrastructure for service-based metacomputing across LAN and WAN clusters. It allows remote users to login to this environment over the Web and request access to data, and also to invoke services or operations on the available data – dhpc.adelaide.edu.au/Projects/DISCWorld/ dhpc.adelaide.edu.au/Projects/DISCWorld/ Nimrod/G & GRACE A global scheduler (resource broker) for parametric computing over clusters or computational grids – www.dgs.monash.edu.au/~rajkumar/ecogrid www.dgs.monash.edu.au/~rajkumar/ecogrid
Many Testbeds ? & who pays ? GUSTO Legion Testbed NASA IPG EcoGrid
Types of Grid Applications Sequential – dusty deck codes. Data Parallel: Synchronous – tightly coupled; Loosely synchronous. Asynchronous: Irregular in time and space; Difficult to parallelise to exploit the massive parallelism. Embarrassingly Parallel.
Grid Applications-Drivers Distributed HPC (Supercomputing): Computational science. High-throughput computing: Large scale simulation/chip design & parameter studies. Content Sharing Sharing digital contents among peers (e.g., Napster) Remote software access/renting services: Application service provides (ASPs). Data-intensive computing: Data mining, particle physics (CERN), Drug Design. On-demand computing: Medical instrumentation & network-enabled solvers. Collaborative: Collaborative design, data exploration, education.
P. Messina et al., Caltech http://www.globus.org/applications/ SF-Express distributed interactive simulation. 100K vehicles (2002 goal) using 13 computers, 1386 nodes, 9 sites. Globus mechanisms for Resource allocation; Distributed startup; I/O and configuration; Security. NCSA Origin Caltech Exemplar CEWES SP Maui SP Distributed Supercomputing (SF-Express/MPICH-G, Caltech)
SF-Express Architecture Create synthetic, representations of interactive environments. Scalability via interest management. Starting point: MPI and socket communication; Hand startup. Local Simulation Router Interest Mgmt. Local Simulation Router Interest Mgmt. Local Simulation Router Interest Mgmt.
High Throughput Computing (parameter sweep applications) A study involving exploration of possible scenarios - i.e., execution of the same program for various design alternatives (data). It consists of large number of tasks (1000s). Generally, no inter-task communication (task farming). Large size data (MBytes+) files and I/O constraints A large class of application areas: Parameter explorations and simulations (Monte Carlo); A large number of science, engineering, and commercial applications: Astrophysics, Drug Design, NeroScience, Network simulation, structural engineering, automobiles crash simulation, aerospace modeling, financial risk analysis Condor, Nimrod/G, DesignDrug@Home, SETI@Home, FOLD@Home, Distributed.net.DesignDrug@Home
Ad Hoc Mobile Network Simulation Ad Hoc Mobile Network Simulation: Network performance under different microware frequencies and different weather conditions – uses Nimrod.
Drug Design: Data Intensive Computing on Grid It involves screening millions of chemical compounds (molecules) in the Chemical DataBase (CDB) to identify those having potential to serve as drug candidates. Protein Molecules Chemical Databases (legacy, in.MOL2 format)
DesignDrug@Home Architecture 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)
MEG( MagnetoEncephaloGraphy) Data Analysis on the Grid: Brain Activity Analysis Life-electronics laboratory, AIST Data Analysis Provision of expertise in the analysis of brain function Provision of MEG analysis Data Generation Nimrod-G 64 sensors MEG Results Analysis All pairs (64x64) of MEG data by shifting the temporal region of MEG data over time: 0 to 29750: 64x64x29750 jobs World-Wide Grid [deadline, budget, optimization preference] 1 5 4 3 2 [Collaboration with Osaka University, Japan]
SETI@home: Search for Extraterrestrial Intelligence at Home
Collaborative Engineering Components of an AG Node Digital Video Digital Audio NETWORK Mixer Control Computer NTSC Video RGB Video Analog Audio Video Capture Computer Display Computer Audio Capture Computer Echo Canceller Rick Stevens & Team, ANL Group to group interactions. Human collaboration across distributed locations Remote visualizations, virtual meeting, seminars,etc. Uses grid technologies for secure communication etc. May have interaction with scientific apps. Access GRID: http://www-fp.mcs.anl.gov/fl/accessgrid/
Parallelisation of Image Rendering Image splitting (by rows, columns, and checker) Each segment can be concurrently processed on different nodes and render image as segments are processed.
Scheduling (need load balancing) Each row rendering takes different times depending on image nature – e.g, rendering rows across the sky take less time compared to those that intersect the interesting parts of the image. Rending apps can be implemented using MPI, PVM, or p-study tools like Nimrod and schedule.
Data Intensive Computing e.g., CERN Data Grid initiative
CERN Large Hadron Collider - circular particle accelerator to be placed in 27 km long tunnel in 2005.
Conclude with a comparison with the Electrical Grid……….. Where we are ????
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)
….and in the future, I imagine a worldwide Power (Electrical) Grid …... What ?!?! This is a mad man… Oh, mon Dieu !
What will be the dominant Grid approach in the next future ??
The Computational Grid is analogous to Electricity (Power) Grid and the vision is to offer a (almost) dependable, consistent, pervasive, and inexpensive access to high-end resources irrespective their location of physical existence and the location of access.
Trends It is very difficult to predict the future and this is particular true in a field such as Information Technology I think there is a world market for about five computers. Thomas J. Watson Sr., IBM Founder, 1943
Trends The time is exciting but the way ahead may be hard and long….! Grid
The Grid Impact! The global computational grid is expected to drive the economy of the 21 st century similar to the electric power grid that drove the economy of the 20 th century
Future Grid Scenarios Access to any resources, for anyone, anywhere, anytime, from any platform – portal (super) computing. Application access to resources from the wall socket! Many applications provide solutions in real- time. Choice of working: office vs home vs... Collaboratories for distributed teams. Monitoring and steering applications through wireless devices (PDAs etc.).
Final Summary There are currently a large number of projects and diverse range of emerging Grid developmental approaches being pursued. These range from metacomputing frameworks to application testbeds, and from collaborative environments to batch submission mechanisms.
Conclusions The HPC will be dominated by Peer-to-Peer Grid of clusters. Adaptive, scalable, and easy to use Systems and End-User applications will be prominent. Access electricity, internet, entertainment (music, movie,…), etc. from the wall socket! An Economics –based Service Oriented Grid Computing computing needed for eventual success of Grids! The impact of Grid on 21 st century economy will be the same as electricity on 20 th century economy.
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 GRID Forums www.gridforum.org, www.egrid.org CCGRID 2001, www.ccgrid.org GRID Meeting - www.gridcomputing.org
Further Information Cluster Computing Infoware: http://www.buyya.com/cluster/ Grid Computing Infoware: http://www.gridcomputing.com IEEE DS Online - Grid Computing area: http://computer.org/dsonline/gc Millennium Compute Power Grid/Market Project http://www.ComputePower.com