Introduction to Grid computing

Transcription

1 Introduction to Grid computing The INFNGrid Project Team

2 Introduction This tutorial has been implemented considering as starting point the DataGrid (EDG) tutorial Many thanks to the EDG tutorials team! Various slides and exercises come from the EDG tutorial Many changes to the above material have been applied E.g. information on Globus has been added by the INFNGrid Tutorials team

3 Overview What is a Grid? What is Grid computing? Why Grids? Grid projects world wide The European DataGrid project

4 What is a Grid? Dependable, consistent, pervasive access to resources Enable communities ( virtual organizations ) to share geographically distributed resources as they pursue common goals in the absence of central control, omniscience, trust relationships Make it easy to use diverse, geographically distributed, locally managed and controlled DB computing facilities as if they formed a coherent local cluster DB DB DB

5 What is Grid computing Coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations [ I.Foster] A VO is a collection of users sharing similar needs and requirements in their access to processing, data and distributed resources and pursuing similar goals. Big challenge for computing with high sociological impact and a new way of collaborating

6 Researchers perform their activities regardless geographical location, interact with colleagues, share and access data The Grid Vision The Grid: networked data processing centres and middleware software as the glue of resources. Scientific instruments and experiments provide huge amount of data

7 The Grid distributed computing idea 1/2 Once upon a time.. mainframe Microcomputer Mini Computer Cluster (by Christophe Jacquet)

8 The Grid distributed computing idea 2/2 and today (by Christophe Jacquet) January, 2004 EGrid Project - Grid Tutorial, Trieste

9 Grid vs distributed computing Distributed computing Distributed applications tend to be specialised systems, intended for a single purpose or user group Resources are usually centrally managed and controlled, or at least they are managed using the same mechanisms, policies, tools Resources are usually homogeneous Static nature Grid Computing Resources locally managed and controlled The owner of the resource has the full control, and therefore different Grid resources can be managed using different policies and mechanisms, e.g. Computing resources can be managed by different local resource management systems Different storage systems (disk pools, mass storage systems, ) used in different Grid sites Different policies and priorities given to the same user in different Grid systems Different kind of resources Not always the same hardware and applications Dynamic nature Resources and users added/removed/changed frequently

10 Main entities of a Grid architecture Service providers Publish the availability of their services via information systems Such services may come-and-go or change dynamically E.g. a Grid site that offers x CPUs and y GB of storage Service brokers Register and categorize published services and provide search capabilities E.g. 1) Resource Broker selects the best resources for a job 2) Catalogues of data held at each Grid site Service requesters Single sign-on: log into the grid once Use brokering services to find a needed service and employ it E.g. scientist submits a simulation job that needs 12 CPUs for 6 hours and 15 GB which gets scheduled, via the Resource Broker, on a certain Grid site

11 Why Grids Distributed (world-wide) user collaborations Distributed computational and storage resources Scale of the problems Grids provide access to large data processing power and huge data storage possibilities As the Grid grows its usefulness increases (more resources available) Large communities of possible Grid users : High Energy Physics Environmental studies: Earthquakes forecast, geologic and climate changes, ozone monitoring Biology, Genetics, Earth Observation Astrophysics, New composite materials research Astronautics, etc.

12 High Energy Physics ATLAS The LHC Detectors CMS ~6-8 PetaBytes / year ~10 8 events/year ~10 3 batch and interactive users Federico.carminati, EU review presentation LHCb

13 Earth Observation ESA missions: about 100 Gbytes of data per day (ERS 1/2) 500 Gbytes, for the next ENVISAT mission (2002). Grid contribute to EO: enhance the ability to access high level products allow reprocessing of large historical archives improve Earth science complex applications (data fusion, data mining, modelling ) Federico.Carminati, EU review presentation, 1 March 2002 Source: L. Fusco, June 2001

14 Biology BioInformatics Bio-informatics Phylogenetics Search for primers Statistical genetics Bio-informatics web portal Parasitology Data-mining on DNA chips Geometrical protein comparison Medical imaging MR image simulation Medical data and metadata management Mammographies analysis Simulation platform for PET/SPECT 1. Query the medical image database and retrieve a patient image 3. Retrieve most similar cases Exam image patient key ACL... Metadata Medical images 2. Compute similarity measures over the database images Submit 1 job per image Similar images Low score images

15 Status (some years ago) People have been discussing about Grid for several years but till some years ago more or less only Globus toolkit available Globus toolkit: core services for Grid tools and applications (Security, Information service, Resource management, etc ) Good basis to build on but: No high level services Many problems (e.g. handling of high volumes of data) not addressed No production quality implementations Not possible to do real work with Grids yet Necessary to address the shortcomings and implement the missing (high level) services

16 Major existing Grid projects Europe-based projects: European DataGrid (EDG) : LHC Computing GRID (LCG): cern.ch/lcg CrossGrid : DataTAG : GridLab : EGEE : European National Projects: INFNGRID, UK-GridPP, NorduGrid(Nordic test bed for wide area computing )

17 Major existing Grid projects US projects: GriPhyN HEP PPDG HEP ivdgl ( joint GriPhyN, PPDG) TERAGRID (NSF) IBM, Intel Qwest,Myricom, Sun Microsystems, Oracle. National Middleware Initiative (NSF NMI) ESG NEESgrid virtual lab earthquake engineering BIRN biomedical informatics research network birn.ncrr.nih.gov/birn/ Asia-based projects: ApGRID TWGRID Many Grid projects in : Korea, Japan, China, Australia

18 Global Grid Forum (GGF) Mission To focus on the promotion and development of Grid technologies and applications via the development and documentation of "best practices," implementation guidelines, and standards with an emphasis on "rough consensus and running code" An Open Process for Development of Standards A Forum for Information Exchange A Regular Gathering to Encourage Shared Effort See

19 The European Data Grid (EDG) Project To build on the emerging Grid technology to develop a sustainable computing model for effective share of computing resources and data Applications/End Users Communities: HEP, Earth Observation, Biology Start : Jan 1, 2001 End : Dec 31, 2003 Specific project objectives: Middleware for fabric & Grid management Large scale testbed To collaborate with and complement other European and US projects To contribute to Open Standards and international bodies: DataGrid co-founder of Global Grid Forum (GGF)

20 DataGrid in Numbers Testbeds People >15 regular sites >350 registered users 12 Virtual Organisations >10 000s jobs submitted 16 Certificate Authorities >1000 CPUs >5 TeraBytes disk >500 people trained 3 Mass Storage Systems 278 man-years of effort 100 years funded Software 50 use cases 18 software releases Scientific applications 5 Earth Obs institutes 9 bio-informatics apps 6 HEP experiments >300K lines of code January, 2004 EGrid Project - Grid Tutorial, Trieste

21 EDG software architecture EDG architectural functional blocks: Basic Services (authentication, authorization, Replica Catalog, secure file transfer, Info Providers) rely on VDT (basically Globus + Condor) Higher level EDG middleware (developed within EDG) Applications (HEP,BIO,EO) Specific application layer VOs common application layer Grid middleware VDT ALICE ATLAS CMS LHCb Other apps LHC Other apps High level Grid middleware Basic Services OS & Net services

22 EDG middleware Grid architecture Local Computing Local Application Local Database APPLICATIONS Grid Grid Application Layer Job Management Data Management Metadata Management Collective Services Grid Scheduler Replica Manager Information & Monitoring Underlying Grid Services Grid SQL Database Services Computing Element Services Storage Element Services Replica Catalog Authorization Authentication and Accounting Service Index M / W Fabric Fabric services Resource Management Configuration Management Monitoring and Fault Tolerance Node Installation & Management Fabric Storage Management GLOBUS

23 Current EDG Status EDG currently provides a set of middleware services Job & Data Management Grid Information Services Grid & Network monitoring Security, Authentication & Authorization tools Fabric Management EDG release 2.1 currently deployed to the EDG-Testbeds GNU/Linux RedHat 7.3 on Intel PCs ~15 sites in application testbed actively used by application groups EDG software used also in other Grid infrastructures: Deployed in the LCG Grid infrastructure (for LHC experiments) EDG sw also deployed at total of ~40 sites via CrossGrid, DataTAG and national grid projects Deployed in the GRID.IT grid (used by different scientific applications) Many applications ported to EDG testbeds and actively being used Activities will continue in the framework of the EGEE project

24 Conclusions Grids enable distributed communities ( virtual organizations ) to share geographically distributed resources Many achievements in the last years Achievements in Grid middleware functionality and stability More and more applications using the Grid Also industry is joining the Grid but still a lot to do Going towards standardizations (OGSA, etc.) Going from Grid prototypes to Grid infrastructures to be used for real production activities

25 28/9/2003: Computational grids finally reached the same level of stability of electrical power grids!