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1. GRID COMPUTING
 

1. GRID COMPUTING

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OUTLINE

OUTLINE
Introduction to Grid Computing
Methods of Grid computing
Grid Middleware
Grid Architecture

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    1. GRID COMPUTING 1. GRID COMPUTING Presentation Transcript

    • GRID COMPUTING Sandeep Kumar Poonia Head Of Dept. CS/IT B.E., M.Tech., UGC-NET LM-IAENG, LM-IACSIT,LM-CSTA, LM-AIRCC, LM-SCIEI, AM-UACEE
    • WHY GRID COMPUTING?  40% Mainframes are idle  90% Unix servers are idle  95% PC servers are idle  0-15% Mainframes are idle in peak-hour  70% PC servers are idle in peak-hour Source: “Grid Computing” Dr Daron G Green SandeepKumarPoonia
    • OUTLINE  Introduction to Grid Computing  Methods of Grid computing  Grid Middleware  Grid Architecture SandeepKumarPoonia
    • SandeepKumarPoonia ELECTRICAL POWER GRID ANALOGY Electrical power grid  users (or electrical appliances) get access to electricity through wall sockets with no care or consideration for where or how the electricity is actually generated.  “The power grid” links together power plants of many different kinds The Grid  users (or client applications) gain access to computing resources (processors, storage, data, applications, and so on) as needed with little or no knowledge of where those resources are located or what the underlying technologies, hardware, operating system, and so on are  "the Grid" links together computing resources (PCs, workstations, servers, storage elements) and provides the mechanism needed to access them.
    • Sandeep Kumar Poonia WHY NEED GRID COMPUTING?  Core networking technology now accelerates at a much faster rate than advances in microprocessor speeds  Exploiting under utilized resources  Parallel CPU capacity  Virtual resources and virtual organizations for collaboration  Access to additional resources
    • Sandeep Kumar Poonia WHO NEEDS GRID COMPUTING?  Not just computer scientists…  scientists “hit the wall” when faced with situations:  The amount of data they need is huge and the data is stored in different institutions.  The amount of similar calculations the scientist has to do is huge.  Other areas:  Government  Business  Education  Industrial design  ……
    • LIVING IN AN EXPONENTIAL WORLD (1) COMPUTING & SENSORS Moore‘s Law: transistor count doubles each 18 months Magnetohydro- dynamics star formation SandeepKumarPoonia
    • LIVING IN AN EXPONENTIAL WORLD: (2) STORAGE  Storage density doubles every 12 months  Dramatic growth in online data (1 petabyte = 1000 terabyte = 1,000,000 gigabyte)  2000 ~0.5 petabyte  2005 ~10 petabytes  2010 ~100 petabytes  2015 ~1000 petabytes?  Transforming entire disciplines in physical and, increasingly, biological sciences; humanities next? SandeepKumarPoonia
    • DATA INTENSIVE PHYSICAL SCIENCES  High energy & nuclear physics  Including new experiments at CERN  Gravity wave searches  LIGO, GEO, VIRGO  Time-dependent 3-D systems (simulation, data)  Earth Observation, climate modeling  Geophysics, earthquake modeling  Fluids, aerodynamic design  Pollutant dispersal scenarios  Astronomy: Digital sky surveys SandeepKumarPoonia
    • ONGOING ASTRONOMICAL MEGA-SURVEYS  Large number of new surveys  Multi-TB in size, 100M objects or larger  In databases  Individual archives planned and under way  Multi-wavelength view of the sky  > 13 wavelength coverage within 5 years  Impressive early discoveries  Finding exotic objects by unusual colors  L,T dwarfs, high redshift quasars  Finding objects by time variability  Gravitational micro-lensing MACHO 2MASS SDSS DPOSS GSC-II COBE MAP NVSS FIRST GALEX ROSAT OGLE ... SandeepKumarPoonia
    • COMING FLOODS OF ASTRONOMY DATA  The planned Large Synoptic Survey Telescope will produce over 10 petabytes per year by 2008!  All-sky survey every few days, so will have fine-grain time series for the first time SandeepKumarPoonia
    • DATA INTENSIVE BIOLOGY AND MEDICINE  Medical data  X-Ray, mammography data, etc. (many petabytes)  Digitizing patient records  X-ray crystallography  Molecular genomics and related disciplines  Human Genome, other genome databases  Proteomics (protein structure, activities, …)  Protein interactions, drug delivery  Virtual Population Laboratory (proposed)  Simulate likely spread of disease outbreaks  Brain scans (3-D, time dependent) SandeepKumarPoonia
    • And comparisons must be made among many We need to get to one micron to know location of every cell. We’re just now starting to get to 10 microns – Grids will help get us there and further A BRAIN IS A LOT OF DATA! (MARK ELLISMAN, UCSD) SandeepKumarPoonia
    • Fastest virtual supercomputers SandeepKumarPoonia As of April 2013, Folding@home – 11.4 x86-equivalent (5.8 "native") PFLOPS. As of March 2013, BOINC – processing on average 9.2 PFLOPS. As of April 2010, MilkyWay@Home computes at over 1.6 PFLOPS, with a large amount of this work coming from GPUs. As of April 2010, SETI@Home computes data averages more than 730 TFLOPS. As of April 2010, Einstein@Home is crunching more than 210 TFLOPS. As of June 2011, GIMPS is sustaining 61 TFLOPS.
    • HOW GRID COMPUTING WORKS Super computer, Big mainframe… Idol time Idol CPU Idol CPU Idol time Source: “The Evolving Computing Model: Grid Computing” Michael Teyssedre SandeepKumarPoonia
    • HOW GRID COMPUTING WORKS Virtual machine Virtual CPU… Idol time Idol CPU Idol CPU Idol time Source: “The Evolving Computing Model: Grid Computing” Michael Teyssedre SandeepKumarPoonia
    • HOW GRID COMPUTING WORKS Grid Computing 0% idol 0% idol 0% idol 0% idol Source: “The Evolving Computing Model: Grid Computing” Michael Teyssedre SandeepKumarPoonia
    • GRID ARCHITECTURE Autonomous, globally distributed computers/clusters SandeepKumarPoonia
    • WHAT IS A GRID?  Many definitions exist in the literature  Early defs: Foster and Kesselman, 1998 ―A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational facilities‖  Kleinrock 1969: ―We will probably see the spread of ‗computer utilities‘, which, like present electric and telephone utilities, will service individual homes and offices across the country.‖ SandeepKumarPoonia
    • 3-POINT CHECKLIST (FOSTER 2002) 1. Coordinates resources not subject to centralized control 2. Uses standard, open, general purpose protocols and interfaces 3. Deliver nontrivial qualities of service • e.g., response time, throughput, availability, security SandeepKumarPoonia
    • DEFINITION Grid computing is…  A distributed computing system  Where a group of computers are connected  To create and work as one large virtual computing power, storage, database, application, and service SandeepKumarPoonia
    • DEFINITION Grid computing…  Allows a group of computers to share the system securely and  Optimizes their collective resources to meet required workloads  By using open standards SandeepKumarPoonia
    • GRID COMPUTING Grid computing is a form of distributed computing whereby a "super and virtual computer" is composed of a cluster of networked, loosely coupled computers, acting in concert to perform very large tasks. Grid computing (Foster and Kesselman, 1999) is a growing technology that facilitates the executions of large-scale resource intensive applications on geographically distributed computing resources. Facilitates flexible, secure, coordinated large scale resource sharing among dynamic collections of individuals, institutions, and resource Enable communities (―virtual organizations‖) to share geographically distributed resources as they pursue common goals Ian Foster and Carl Kesselman SandeepKumarPoonia
    • A COMPARISON SERIAL  Fetch/Store  Compute PARALLEL  Fetch/Store  Compute/ communicate  Cooperative game GRID  Fetch/Store  Discovery of Resources  Interaction with remote application  Authentication / Authorization  Security  Compute/Communicate  Etc SandeepKumarPoonia
    • DISTRIBUTED COMPUTING VS. GRID  Grid is an evolution of distributed computing  Dynamic  Geographically independent  Built around standards  Internet backbone  Distributed computing is an ―older term‖  Typically built around proprietary software and network  Tightly couples systems/organization SandeepKumarPoonia
    • WEB VS. GRID  Web  Uniform naming access to documents  Grid - Uniform, high performance access to computational resources Colleges/R&D Labs Software Catalogs Sensor nets http:// http:// SandeepKumarPoonia
    • IS THE WORLD WIDE WEB A GRID ?  Seamless naming? Yes  Uniform security and Authentication? No  Information Service? Yes or No  Co-Scheduling? No  Accounting & Authorization ? No  User Services? No  Event Services? No  Is the Browser a Global Shell ? No SandeepKumarPoonia
    • WHAT DOES THE WORLD WIDE WEB BRING TO THE GRID ?  Uniform Naming  A seamless, scalable information service  A powerful new meta-data language: XML  XML will be standard language for describing information in the grid  SOAP – simple object access protocol  Uses XML for encoding. HTML for protocol  SOAP may become a standard RPC mechanism for Grid services  Uses XML for encoding. HTML for protocol  Portal Ideas SandeepKumarPoonia
    • THE ULTIMATE GOAL  In future I will not know or care where my application will be executed as I will acquire and pay to use these resources as I need them SandeepKumarPoonia
    • WHY GRIDS?  Large-scale science and engineering are done through the interaction of people, heterogeneous computing resources, information systems, and instruments, all of which are geographically and organizationally dispersed.  The overall motivation for ―Grids‖ is to facilitate the routine interactions of these resources in order to support large-scale science and Engineering. SandeepKumarPoonia
    • AN EXAMPLE VIRTUAL ORGANIZATION: CERN‘S LARGE HADRON COLLIDER 1800 Physicists, 150 Institutes, 32 Countries 100 PB of data by 2010; 50,000 CPUs? SandeepKumarPoonia
    • GRID COMMUNITIES & APPLICATIONS: DATA GRIDS FOR HIGH ENERGY PHYSICS Tier2 Centre ~1 TIPS Online System Offline Processor Farm ~20 TIPS CERN Computer Centre FermiLab ~4 TIPSFrance Regional Centre Italy Regional Centre Germany Regional Centre InstituteInstituteInstitute Institute ~0.25TIPS Physicist workstations ~100 MBytes/sec ~100 MBytes/sec ~622 Mbits/sec ~1 MBytes/sec There is a “bunch crossing” every 25 nsecs. There are 100 “triggers” per second Each triggered event is ~1 MByte in size Physicists work on analysis “channels”. Each institute will have ~10 physicists working on one or more channels; data for these channels should be cached by the institute server Physics data cache ~PBytes/sec ~622 Mbits/sec or Air Freight (deprecated) Tier2 Centre ~1 TIPS Tier2 Centre ~1 TIPS Tier2 Centre ~1 TIPS Caltech ~1 TIPS ~622 Mbits/sec Tier 0 Tier 1 Tier 2 Tier 4 1 TIPS is approximately 25,000 SpecInt95 equivalents www.griphyn.org www.ppdg.net www.eu-datagrid.org SandeepKumarPoonia
    • INTELLIGENT INFRASTRUCTURE: DISTRIBUTED SERVERS AND SERVICES SandeepKumarPoonia
    •  Early 90s  Gigabit testbeds, metacomputing  Mid to late 90s  Early experiments (e.g., I-WAY), academic software projects (e.g., Globus, Legion), application experiments  2002  Dozens of application communities & projects  Major infrastructure deployments  Significant technology base (esp. Globus ToolkitTM)  Growing industrial interest  Global Grid Forum: ~500 people, 20+ countries THE GRID: A BRIEF HISTORY SandeepKumarPoonia
    • HOW IT EVOLVES Utility computing Service grid Data grid Processing grid Virtualization Service-oriented Open standard SandeepKumarPoonia
    • EARLY ADOPTERS  Academic  Big science  Life science  Nuclear engineering  Simulation… SandeepKumarPoonia
    • MARKET POTENTIAL  Financial services: risk management and compliance  Automotive: acceleration of product development  Petroleum: discovery of oils Source: “Perspectives on grid: Grid computing - next-generation distributed computing" Matt Haynos, 01/27/04 SandeepKumarPoonia
    • Criteria for a Grid: Coordinates resources that are not subject to centralized control. Uses standard, open, general-purpose protocols and interfaces. Delivers nontrivial qualities of service. e.g., response time, throughput, availability, security Benefits Exploit Underutilized resources Resource load Balancing Virtualize resources across an enterprise Data Grids, Compute Grids Enable collaboration for virtual organizations SandeepKumarPoonia
    • WHY DO WE NEED GRIDS?  Many large-scale problems cannot be solved by a single computer  Globally distributed data and resources SandeepKumarPoonia
    • GRID APPLICATIONS Data and computationally intensive applications: This technology has been applied to computationally- intensive scientific, mathematical, and academic problems like drug discovery, economic forecasting, seismic analysis back office data processing in support of e-commerce  A chemist may utilize hundreds of processors to screen thousands of compounds per hour.  Teams of engineers worldwide pool resources to analyze terabytes of structural data.  Meteorologists seek to visualize and analyze petabytes of climate data with enormous computational demands. Resource sharing  Computers, storage, sensors, networks, …  Sharing always conditional: issues of trust, policy, negotiation, payment, … Coordinated problem solving  distributed data analysis, computation, collaboration, … SandeepKumarPoonia
    • GRID TOPOLOGIES • Intragrid – Local grid within an organisation – Trust based on personal contracts • Extragrid – Resources of a consortium of organisations connected through a (Virtual) Private Network – Trust based on Business to Business contracts • Intergrid – Global sharing of resources through the internet – Trust based on certification SandeepKumarPoonia
    • COMPUTATIONAL GRID ―A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities.‖ ‖The Grid: Blueprint for a New Computing Infrastructure‖, Kesselman & Foster Example : Science Grid (US Department of Energy) SandeepKumarPoonia
    • DATA GRID  A data grid is a grid computing system that deals with data — the controlled sharing and management of large amounts of distributed data.  Data Grid is the storage component of a grid environment. Scientific and engineering applications require access to large amounts of data, and often this data is widely distributed. A data grid provides seamless access to the local or remote data required to complete compute intensive calculations. Example : Biomedical informatics Research Network (BIRN), the Southern California earthquake Center (SCEC). SandeepKumarPoonia
    • BACKGROUND: RELATED TECHNOLOGIES  Cluster computing  Peer-to-peer computing  Internet computing SandeepKumarPoonia
    • CLUSTER COMPUTING  Idea: put some PCs together and get them to communicate  Cheaper to build than a mainframe supercomputer  Different sizes of clusters  Scalable – can grow a cluster by adding more PCs SandeepKumarPoonia
    • CLUSTER ARCHITECTURE SandeepKumarPoonia
    • PEER-TO-PEER COMPUTING  Connect to other computers  Can access files from any computer on the network  Allows data sharing without going through central server  Decentralized approach also useful for Grid SandeepKumarPoonia
    • PEER TO PEER ARCHITECTURE SandeepKumarPoonia
    • METHODS OF GRID COMPUTING  Distributed Supercomputing  High-Throughput Computing  On-Demand Computing  Data-Intensive Computing  Collaborative Computing  Logistical Networking SandeepKumarPoonia
    • DISTRIBUTED SUPERCOMPUTING  Combining multiple high-capacity resources on a computational grid into a single, virtual distributed supercomputer.  Tackle problems that cannot be solved on a single system.  Examples: climate modeling, computational chemistry  Challenges include:  Scheduling scarce and expensive resources  Scalability of protocols and algorithms  Maintaining high levels of performance across heterogeneous systems SandeepKumarPoonia
    • HIGH-THROUGHPUT COMPUTING  Uses the grid to schedule large numbers of loosely coupled or independent tasks, with the goal of putting unused processor cycles to work.  Schedule large numbers of independent tasks  Goal: exploit unused CPU cycles (e.g., from idle workstations)  Unlike distributed computing, tasks loosely coupled  Examples: parameter studies, cryptographic problems SandeepKumarPoonia
    • On-Demand Computing  Uses grid capabilities to meet short-term requirements for resources that are not locally accessible.  Models real-time computing demands.  Use Grid capabilities to meet short-term requirements for resources that cannot conveniently be located locally  Unlike distributed computing, driven by cost- performance concerns rather than absolute performance  Dispatch expensive or specialized computations to remote servers SandeepKumarPoonia
    • COLLABORATIVE COMPUTING  Concerned primarily with enabling and enhancing human-to-human interactions.  Enable shared use of data archives and simulations  Applications are often structured in terms of a virtual shared space.  Examples:  Collaborative exploration of large geophysical data sets  Challenges:  Real-time demands of interactive applications  Rich variety of interactions SandeepKumarPoonia
    • Data-Intensive Computing  The focus is on synthesizing new information from data that is maintained in geographically distributed repositories, digital libraries, and databases.  Particularly useful for distributed data mining.  Examples: •High energy physics generate terabytes of distributed data, need complex queries to detect “interesting” events •Distributed analysis of Sloan Digital Sky Survey data SandeepKumarPoonia
    • LOGISTICAL NETWORKING  Logistical networks focus on exposing storage resources inside networks by optimizing the global scheduling of data transport, and data storage.  Contrasts with traditional networking, which does not explicitly model storage resources in the network.  high-level services for Grid applications  Called "logistical" because of the analogy it bears with the systems of warehouses, depots, and distribution channels. SandeepKumarPoonia
    • P2P COMPUTING VS GRID COMPUTING  Differ in Target Communities  Grid system deals with more complex, more powerful, more diverse and highly interconnected set of resources than P2P. SandeepKumarPoonia
    • A TYPICAL VIEW OF GRID ENVIRONMENT User Resource Broker Grid Resources Grid Information Service A User sends computation or data intensive application to Global Grids in order to speed up the execution of the application. A Resource Broker distribute the jobs in an application to the Grid resources based on user’s QoS requirements and details of available Grid resources for further executions. Grid Resources (Cluster, PC, Supercomputer, database, instruments, etc.) in the Global Grid execute the user jobs. Grid Information Service system collects the details of the available Grid resources and passes the information to the resource broker. Computation result Grid application Computational jobs Details of Grid resources Processed jobs 1 2 3 4 SandeepKumarPoonia
    • GRID MIDDLEWARE  Grids are typically managed by grid ware - a special type of middleware that enable sharing and manage grid components based on user requirements and resource attributes (e.g., capacity, performance)  Software that connects other software components or applications to provide the following functions: Run applications on suitable available resources – Brokering, Scheduling Provide uniform, high-level access to resources – Semantic interfaces – Web Services, Service Oriented Architectures Address inter-domain issues of security, policy, etc. – Federated Identities Provide application-level status monitoring and control SandeepKumarPoonia
    • MIDDLEWARES  Globus –chicago Univ  Condor – Wisconsin Univ – High throughput computing  Legion – Virginia Univ – virtual workspaces- collaborative computing  IBP – Internet back pane – Tennesse Univ – logistical networking  NetSolve – solving scientific problems in heterogeneous env – high throughput & data intensive SandeepKumarPoonia
    • TWO KEY GRID COMPUTING GROUPS The Globus Alliance (www.globus.org)  Composed of people from: Argonne National Labs, University of Chicago, University of Southern California Information Sciences Institute, University of Edinburgh and others.  OGSA/I standards initially proposed by the Globus Group The Global Grid Forum (www.ggf.org)  Heavy involvement of Academic Groups and Industry  (e.g. IBM Grid Computing, HP, United Devices, Oracle, UK e-Science Programme, US DOE, US NSF, Indiana University, and many others)  Process  Meets three times annually  Solicits involvement from industry, research groups, and academics SandeepKumarPoonia
    • GRID USERS  Many levels of users  Grid developers  Tool developers  Application developers  End users  System administrators SandeepKumarPoonia
    • SOME GRID CHALLENGES  Data movement  Data replication  Resource management  Job submission SandeepKumarPoonia
    • SOME OF THE MAJOR GRID PROJECTS Name URL/Sponsor Focus EuroGrid, Grid Interoperability (GRIP) eurogrid.org European Union Create tech for remote access to super comp resources & simulation codes; in GRIP, integrate with Globus Toolkit™ Fusion Collaboratory fusiongrid.org DOE Off. Science Create a national computational collaboratory for fusion research Globus Project™ globus.org DARPA, DOE, NSF, NASA, Msoft Research on Grid technologies; development and support of Globus Toolkit™; application and deployment GridLab gridlab.org European Union Grid technologies and applications GridPP gridpp.ac.uk U.K. eScience Create & apply an operational grid within the U.K. for particle physics research Grid Research Integration Dev. & Support Center grids-center.org NSF Integration, deployment, support of the NSF Middleware Infrastructure for research & education SandeepKumarPoonia
    • SandeepKumarPoonia Grid in India-GARUDA •GARUDA is India's Grid Computing initiative connecting 17 cities across the country. •The 45 participating institutes in this nationwide project include all the IITs and C-DAC centers and other major institutes in India.
    • GLOBUS GRID TOOLKIT  Open source toolkit for building Grid systems and applications  Enabling technology for the Grid  Share computing power, databases, and other tools securely online  Facilities for:  Resource monitoring  Resource discovery  Resource management  Security  File management SandeepKumarPoonia
    • DATA MANAGEMENT IN GLOBUS TOOLKIT  Data movement  GridFTP  Reliable File Transfer (RFT)  Data replication  Replica Location Service (RLS)  Data Replication Service (DRS) SandeepKumarPoonia
    • GRIDFTP  High performance, secure, reliable data transfer protocol  Optimized for wide area networks  Superset of Internet FTP protocol  Features:  Multiple data channels for parallel transfers  Partial file transfers  Third party transfers  Reusable data channels  Command pipelining SandeepKumarPoonia
    • MORE GRIDFTP FEATURES  Auto tuning of parameters  Striping  Transfer data in parallel among multiple senders and receivers instead of just one  Extended block mode  Send data in blocks  Know block size and offset  Data can arrive out of order  Allows multiple streams SandeepKumarPoonia
    • STRIPING ARCHITECTURE  Use ―Striped‖ servers SandeepKumarPoonia
    • LIMITATIONS OF GRIDFTP  Not a web service protocol (does not employ SOAP, WSDL, etc.)  Requires client to maintain open socket connection throughout transfer  Inconvenient for long transfers  Cannot recover from client failures SandeepKumarPoonia
    • GRIDFTP SandeepKumarPoonia
    • RELIABLE FILE TRANSFER (RFT)  Web service with ―job-scheduler‖ functionality for data movement  User provides source and destination URLs  Service writes job description to a database and moves files  Service methods for querying transfer status SandeepKumarPoonia
    • RFT SandeepKumarPoonia
    • REPLICA LOCATION SERVICE (RLS)  Registry to keep track of where replicas exist on physical storage system  Users or services register files in RLS when files created  Distributed registry  May consist of multiple servers at different sites  Increase scale  Fault tolerance SandeepKumarPoonia
    • REPLICA LOCATION SERVICE (RLS)  Logical file name – unique identifier for contents of file  Physical file name – location of copy of file on storage system  User can provide logical name and ask for replicas  Or query to find logical name associated with physical file location SandeepKumarPoonia
    • DATA REPLICATION SERVICE (DRS)  Pull-based replication capability  Implemented as a web service  Higher-level data management service built on top of RFT and RLS  Goal: ensure that a specified set of files exists on a storage site  First, query RLS to locate desired files  Next, creates transfer request using RFT  Finally, new replicas are registered with RLS SandeepKumarPoonia
    • CONDOR  Original goal: high-throughput computing  Harvest wasted CPU power from other machines  Can also be used on a dedicated cluster  Condor-G – Condor interface to Globus resources SandeepKumarPoonia
    • CONDOR  Provides many features of batch systems:  job queueing  scheduling policy  priority scheme  resource monitoring  resource management  Users submit their serial or parallel jobs  Condor places them into a queue  Scheduling and monitoring  Informs the user upon completion SandeepKumarPoonia
    • NIMROD-G  Tool to manage execution of parametric studies across distributed computers  Manages experiment  Distributing files to remote systems  Performing the remote computation  Gathering results  User submits declarative plan file  Parameters, default values, and commands necessary for performing the work  Nimrod-G takes advantage of Globus toolkit features SandeepKumarPoonia
    • NIMROD-G ARCHITECTURE SandeepKumarPoonia
    • GRID CASE STUDIES  Earth System Grid  LIGO  TeraGrid SandeepKumarPoonia
    • EARTH SYSTEM GRID  Provide climate studies scientists with access to large datasets  Data generated by computational models – requires massive computational power  Most scientists work with subsets of the data  Requires access to local copies of data SandeepKumarPoonia
    • ESG INFRASTRUCTURE  Archival storage systems and disk storage systems at several sites  Storage resource managers and GridFTP servers to provide access to storage systems  Metadata catalog services  Replica location services  Web portal user interface SandeepKumarPoonia
    • EARTH SYSTEM GRID SandeepKumarPoonia
    • EARTH SYSTEM GRID INTERFACE SandeepKumarPoonia
    • LASER INTERFEROMETER GRAVITATIONAL WAVE OBSERVATORY (LIGO)  Instruments at two sites to detect gravitational waves  Each experiment run produces millions of files  Scientists at other sites want these datasets on local storage  LIGO deploys RLS servers at each site to register local mappings and collect info about mappings at other sites SandeepKumarPoonia
    • LARGE SCALE DATA REPLICATION FOR LIGO  Goal: detection of gravitational waves  Three interferometers at two sites  Generate 1 TB of data daily  Need to replicate this data across 9 sites to make it available to scientists  Scientists need to learn where data items are, and how to access them SandeepKumarPoonia
    • LIGO SandeepKumarPoonia
    • LIGO SOLUTION  Lightweight data replicator (LDR)  Uses parallel data streams, tunable TCP windows, and tunable write/read buffers  Tracks where copies of specific files can be found  Stores descriptive information (metadata) in a database  Can select files based on description rather than filename SandeepKumarPoonia
    • TERAGRID  NSF high-performance computing facility  Nine distributed sites, each with different capability , e.g., computation power, archiving facilities, visualization software  Applications may require more than one site  Data sizes on the order of gigabytes or terabytes SandeepKumarPoonia
    • TERAGRID SandeepKumarPoonia
    • TERAGRID  Solution: Use GridFTP and RFT with front end command line tool (tgcp)  Benefits of system:  Simple user interface  High performance data transfer capability  Ability to recover from both client and server software failures  Extensible configuration SandeepKumarPoonia
    • TGCP DETAILS  Idea: hide low level GridFTP commands from users  Copy file smallfile.dat in a working directory to another system: tgcp smallfile.dat tg-login.sdsc.teragrid.org:/users/ux454332  GridFTP command: globus-url-copy -p 8 -tcp-bs 1198372 gsiftp://tg-gridftprr.uc.teragrid.org:2811/home/navarro/smallfile.dat gsiftp://tg-login.sdsc.teragrid.org:2811/users/ux454332/smallfile.dat SandeepKumarPoonia
    • GRID ARCHITECTURE SandeepKumarPoonia
    • THE HOURGLASS MODEL  Focus on architecture issues  Propose set of core services as basic infrastructure  Used to construct high-level, domain-specific solutions (diverse)  Design principles  Keep participation cost low  Enable local control  Support for adaptation  ―IP hourglass‖ model Diverse global services Core services Local OS A p p l i c a t i o n s SandeepKumarPoonia
    • LAYERED GRID ARCHITECTURE (BY ANALOGY TO INTERNET ARCHITECTURE) Application Fabric “Controlling things locally”: Access to, & control of, resources Connectivity “Talking to things”: communication (Internet protocols) & security Resource “Sharing single resources”: negotiating access, controlling use Collective “Coordinating multiple resources”: ubiquitous infrastructure services, app-specific distributed services Internet Transport Application Link InternetProtocolArchitecture SandeepKumarPoonia
    • EXAMPLE: DATA GRID ARCHITECTURE Discipline-Specific Data Grid Application Coherency control, replica selection, task management, virtual data catalog, virtual data code catalog, … Replica catalog, replica management, co-allocation, certificate authorities, metadata catalogs, Access to data, access to computers, access to network performance data, … Communication, service discovery (DNS), authentication, authorization, delegation Storage systems, clusters, networks, network caches, … Collective (App) App Collective (Generic) Resource Connect Fabric SandeepKumarPoonia
    • SIMULATION TOOLS  GridSim – job scheduling  SimGrid – single client multiserver scheduling  Bricks – scheduling  GangSim- Ganglia VO  OptoSim – Data Grid Simulations  G3S – Grid Security services Simulator – security services SandeepKumarPoonia
    • SIMULATION TOOL  GridSim is a Java-based toolkit for modeling, and simulation of distributed resource management and scheduling for conventional Grid environment.  GridSim is based on SimJava, a general purpose discrete-event simulation package implemented in Java.  All components in GridSim communicate with each other through message passing operations defined by SimJava. SandeepKumarPoonia
    • SALIENT FEATURES OF THE GRIDSIM  It allows modeling of heterogeneous types of resources.  Resources can be modeled operating under space- or time-shared mode.  Resource capability can be defined (in the form of MIPS (Million Instructions Per Second) benchmark.  Resources can be located in any time zone.  Weekends and holidays can be mapped depending on resource‘s local time to model non- Grid (local) workload.  Resources can be booked for advance reservation.  Applications with different parallel application models can be simulated. SandeepKumarPoonia
    • SALIENT FEATURES OF THE GRIDSIM  Application tasks can be heterogeneous and they can be CPU or I/O intensive.  There is no limit on the number of application jobs that can be submitted to a resource.  Multiple user entities can submit tasks for execution simultaneously in the same resource, which may be time-shared or space-shared. This feature helps in building schedulers that can use different market- driven economic models for selecting services competitively.  Network speed between resources can be specified.  It supports simulation of both static and dynamic schedulers.  Statistics of all or selected operations can be recorded and they can be analyzed using GridSim statistics analysis methods. SandeepKumarPoonia
    • A MODULAR ARCHITECTURE FOR GRIDSIM PLATFORM AND COMPONENTS. Appn Conf Res Conf User Req Grid Sc Output Application, User, Grid Scenario’s input and Results Grid Resource Brokers or Schedulers … Appn modeling Res entity Info serv Job mgmt Res alloc Statis GridSim Toolkit Single CPU SMPs Clusters Load Netw Reservation Resource Modeling and Simulation SimJava Distributed SimJava Basic Discrete Event Simulation Infrastructure PCs Workstation ClustersSMPs Distributed Resources Virtual Machine SandeepKumarPoonia
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    • Sandeep Kumar Poonia