1. e-Science e-Business e-Government and their Technologies Introduction
Bryan Carpenter, Geoffrey Fox, Marlon Pierce
Pervasive Technology Laboratories
Indiana University Bloomington IN 47404
January

2. Class Structure
Grading based on mixture of homework and a single final project
Up to 2 students can collaborate together on final project
Homework 70% Final Project 30% grade
NO midterm or final
Homework will mainly be programming based but there may be reports either in final or one or two homework assignments
Reports will use Internet, book and “Gap Analysis”

3. What are we doing
This is a semester-long course on Grids (viewed as technologies and infrastructure) and the application – mainly to science but also to business and government
We will assume a basic knowledge of the Java language and then interweave 6 topic areas – first four cover technologies that will be used by students
1) Advanced Java: including networking, Java Server Pages and perhaps servlets
2) XML: Specification, Tools, Linkage to Java
3) Web Services: Basic Ideas, WSDL, Axis and Tomcat
4)Grid Systems: GT3/Cogkit, Gateway, XSOAP, Portlet
5) Advanced Technology Discussions: CORBA as istory, OGSA-DAI, security, Semantic Grid, Workflow
6) Applications: Bioinformatics, Particle Physics, Engineering, Crises, Computing-on-demand Grid, Earth Science

4. Course Topics 1 and 2 : Background/Core
Advanced Java Programming
We will assume basic Java programming proficiency
We will cover Java client/server, three-tiered and network programming.
Ancillary but interesting Java topics to be covered include Apache Ant, XML-Beans, and Java Message Service
XML and XML Schema
We will provide introductory material.
Necessary to understand Web Service standards
Examples include RDF (semantic web) and SOAP (Web services)
XML Tools
XML Databases (Xindice, Sleepycat)
Search: XPath, XQuery

5. Course Topics 3 and 4: Web and Grid Services
Overview Material
Grid and Web Service Architectures
Basic Web Service Standards
WSDL, SOAP: structure and definitions
Building services in Java: Apache Axis
Advanced Web Services: Emerging capabilities
WS-ReliableMessaging, WS-Security, WS-Transaction
Computational Grids
Globus Toolkit 2
Java COG Kit for Globus programming
Grids Meet Web Services
Open Grid Service Architecture/Infrastructure
Implementations: GSX from Indiana University
The Semantic Grid: Information Models for Describing Resources
RDF, DAML-OIL, and OWL

6. Grid Computing: Making The Global Infrastructure a Reality
Based on work done in preparing book edited with Fran Berman and Anthony J.G. Hey,
ISBN:
Hardcover 1080 Pages
Published March 2003

7. Other
See the webcast in an Oracle technology series
See also the “Gap Analysis”
We can send you nicely printed versions of this
End of this is a good collection of references and it gives both a general survey of current Grids and specific examples from UK
Appendix with more details is:
See also GlobusWorld
and the Grid Forum

8. e-moreorlessanything and the Grid
e-Business captures an emerging view of corporations as dynamic virtual organizations linking employees, customers and stakeholders across the world.
The growing use of outsourcing is one example
e-Science is the similar vision for scientific research with international participation in large accelerators, satellites or distributed gene analyses.
The Grid integrates the best of the Web, traditional enterprise software, high performance computing and Peer-to-peer systems to provide the information technology e-infrastructure for e-moreorlessanything.
A deluge of data of unprecedented and inevitable size must be managed and understood.
People, computers, data and instruments must be linked.
On demand assignment of experts, computers, networks and storage resources must be supported

9. So what is a Grid?
Supporting human decision making with a network of at least four large computers, perhaps six or eight small computers, and a great assortment of disc files and magnetic tape units - not to mention remote consoles and teletype stations - all churning away. (Licklider 1960)
Coordinated resource sharing and problem solving in dynamic multi-institutional virtual organizations
Infrastructure that will provide us with the ability to dynamically link together resources as an ensemble to support the execution of large-scale, resource-intensive, and distributed applications.
Realizing thirty year dream of science fiction writers that have spun yarns featuring worldwide networks of interconnected computers that behave as a single entity.

10. What is a High Performance Computer?
We might wish to consider three classes of multi-node computers
1) Classic MPP with microsecond latency and scalable internode bandwidth (tcomm/tcalc ~ 10 or so)
2) Classic Cluster which can vary from configurations like 1) to 3) but typically have millisecond latency and modest bandwidth
3) Classic Grid or distributed systems of computers around the network
Latencies of inter-node communication – 100’s of milliseconds but can have good bandwidth
All have same peak CPU performance but synchronization costs increase as one goes from 1) to 3)
Cost of system (dollars per gigaflop) decreases by factors of 2 at each step from 1) to 2) to 3)
One should NOT use classic MPP if class 2) or 3) suffices unless some security or data issues dominates over cost-performance
One should not use a Grid as a true parallel computer – it can link parallel computers together for convenient access etc.

11. e-Science
e-Science is about global collaboration in key areas of science, and the next generation of infrastructure that will enable it. This is a major UK Program
e-Science reflects growing importance of international laboratories, satellites and sensors and their integrated analysis by distributed teams
CyberInfrastructure is the analogous US initiative
Grid Technology supports e-Science and CyberInfrastructure
It is software (middeleware) built on top of networks

12. Global Terabit Research Network
The Grid software and resources run on top of high performance global networks

13. USA Network

14. Terabit Networks
Network performance will increase faster than Moore’s law – partly because optical fiber has almost unlimited bandwidth and partly because there are many old networks to be replaced
Home dial-ups (56kbit)  DSL/Cable Modem (2 megabits/sec)  FTTP (Fiber to the Premise at gigabit performance)
2006 Goal of Global Terabit Research Network International: National Backbone: Organization;: Optical Desktop: Copper Desktop is 1000:1000:100:10:1 Gigabit/sec

15. e-Business and (Virtual) Organizations
Enterprise Grid supports information system for an organization; includes “university computer center”, “(digital) library”, sales, marketing, manufacturing …
Outsourcing Grid links different parts of an enterprise together (Gridsourcing)
Manufacturing plants with designers
Animators with electronic game or film designers and producers
Coaches with aspiring players (e-NCAA or e-NFL etc.)
Customer Grid links businesses and their customers as in many web sites such as amazon.com
e-Multimedia can use secure peer-to-peer Grids to link creators, distributors and consumers of digital music, games and films respecting rights
Distance education Grid links teacher at one place, students all over the place, mentors and graders; shared curriculum, homework, live classes …

16. e-Defense and e-Crisis
Grids support Command and Control and provide Global Situational Awareness
Link commanders and frontline troops to themselves and to archival and real-time data; link to what-if simulations
Dynamic heterogeneous wired and wireless networks
Security and fault tolerance essential
System of Systems; Grid of Grids
The command and information infrastructure of each ship is a Grid; each fleet is linked together by a Grid; the President is informed by and informs the national defense Grid
Grids must be heterogeneous and federated
Crisis Management and Response enabled by a Grid linking sensors, disaster managers, and first responders with decision support

17. Classes of Computing Grid Applications
Running “Pleasing Parallel Jobs” as in United Devices, Entropia (Desktop Grid) “cycle stealing systems”
Can be managed (“inside” the enterprise as in Condor) or more informal (as in
Computing-on-demand in Industry where jobs spawned are perhaps very large (SAP, Oracle …)
Support distributed file systems as in Legion (Avaki), Globus with (web-enhanced) UNIX programming paradigm
Particle Physics will run some 30,000 simultaneous jobs this way
Pipelined applications linking data/instruments, compute, visualization
Seamless Access where Grid portals allow one to choose one of multiple resources with a common interfaces

18. Utility Computing
An important business application of Grids is utility computing
Namely support a pool of computers to be assigned as needed to take-up extra demand
Pool shared between multiple applications
One can say this application is common in academia where different simulations share resources while in industry we have
Web Servers
Financial Modeling
Data-mining
Simulation response to crisis like forest fire or earthquake
Architecture is “Farm of Grid Services” connected to Internet not cluster of computers connected to each other

19. Static Assignment with redundancy Dynamic on-demand Assignment
Resources-on-demand
Computing-on-demand uses dynamically assigned (shared) pool of resources to support excess demand in flexible cost-effective fashion
Program A Computer 1
Program Z Computer 26
Program A Computer 27
Program Z Computer 52
Spares
Static Assignment with redundancy
Pool Computer 1
Pool Computer N <52
Program A
Program Z
Dynamic on-demand Assignment

20. Some Important Styles of Grids
Computational Grids were origin of concepts and link computers across the globe – high latency stops this from being used as parallel machine
Knowledge and Information Grids link sensors and information repositories as in Virtual Observatories or BioInformatics
More detail on next slide
Education Grids link teachers, learners, parents as a VO with learning tools, distant lectures etc.
e-Science Grids link multidisciplinary researchers across laboratories and universities
Community Grids focus on Grids involving large numbers of peers rather than focusing on linking major resources – links Grid and Peer-to-peer network concepts
Semantic Grid links Grid, and AI community with Semantic web (ontology/meta-data enriched resources) and Agent concepts

21. Information/Knowledge Grids
Distributed (10’s to 1000’s) of data sources (instruments, file systems, curated databases …)
Data Deluge: 1 (now) to 100’s petabytes/year (2012)
Moore’s law for Sensors
Possible filters assigned dynamically (on-demand)
Run image processing algorithm on telescope image
Run Gene sequencing algorithm on compiled data
Needs decision support front end with “what-if” simulations
Metadata (provenance) critical to annotate data
Integrate across experiments as in multi-wavelength astronomy
Data Deluge comes from pixels/year available

22. 2.4 Petabytes Today

23. Repositories Federated Databases SERVOGrid for e-Geoscience
Closely Coupled Compute Nodes
Analysis and Visualization
Repositories Federated Databases
Sensor Nets
Streaming Data
Loosely Coupled Filters
SERVOGrid for e-Geoscience ?
Discovery
Services
SERVOGrid – Solid Earth Research Virtual Observatory will link Australia, Japan, USA ……

24. SERVOGrid Requirements
Seamless Access to Data repositories and large scale computers
Integration of multiple data sources including sensors, databases, file systems with analysis system
Including filtered OGSA-DAI (Grid database access)
Rich meta-data generation and access with SERVOGrid specific Schema extending openGIS (Geography as a Web service) standards and using Semantic Grid
Portals with component model for user interfaces and web control of all capabilities
Collaboration to support world-wide work
Basic Grid tools: workflow and notification

25. ~ Gigabyte per aircraft per Engine per transatlantic flight
DAME
In flight data
~5000 engines
~ Gigabyte per aircraft per
Engine per transatlantic flight
Global Network
Such as SITA
Ground Station
Airline
Engine Health (Data) Center
Maintenance Centre
Internet, , pager
Rolls Royce and UK e-Science Program Distributed Aircraft Maintenance Environment

26. NASA Aerospace Engineering Grid
It takes a distributed virtual organization to design, simulate and build a complex system like an aircraft

27. Virtual Observatory Astronomy Grid Integrate Experiments
Radio
Far-Infrared
Visible
Dust Map
Visible + X-ray
Galaxy Density Map

28. e-Chemistry Laboratory Experiments-on-demand
Grid-enabled Output Streams
Grid Resources

29. CERN LHC Data Analysis Grid

30. Typical Grid Architecture Each Blob is a Computer Program!
Raw (HPC) Resources
Middleware
Database
Portal
Services
System Services
Application Service
Each Blob is a Computer Program!
User Services
“Core” Grid

31. Sources of Grid Technology
Grids support distributed collaboratories or virtual organizations integrating concepts from
The Web
Agents
Distributed Objects (CORBA Java/Jini COM)
Globus, Legion, Condor, NetSolve, Ninf and other High Performance Computing activities
Peer-to-peer Networks
With perhaps the Web and P2P networks being the most important for “Information Grids” and Globus for “Compute Grids”

32. The Essence of Grid Technology?
We will start from the Web view and assert that basic paradigm is
Meta-data rich Web Services communicating via messages
These have some basic support from some runtime such as .NET, Jini (pure Java), Apache Tomcat+Axis (Web Service toolkit), Enterprise JavaBeans, WebSphere (IBM) or GT3 (Globus Toolkit 3)
These are the distributed equivalent of operating system functions as in UNIX Shell
Called Hosting Environment or platform
W3C standard WSDL defines IDL (Interface standard) for Web Services

33. Meta-data Meta-data is usually thought of as “data about data”
The Semantic Web is at its simplest considered as adding meta-data to web pages
For example, the hospital web-page has meta-data telling you its location, phone-number, specialties which can be used to automate Google-style searches to allow planning of disease/accident treatment from web
Modern trend (Semantic Grid) is meta-data about web-services e.g. specify details of interface and useage
Such as that a bioinformatics service is free or bandwidth input is of limited amount
Provenance – history and ownership – of data very important

34. A typical Web Service
In principle, services can be in any language (Fortran .. Java .. Perl .. Python) and the interfaces can be method calls, Java RMI Messages, CGI Web invocations, totally compiled away (inlining)
The simplest implementations involve XML messages (SOAP) and programs written in net friendly languages like Java and Python
Payment Credit Card
Web Services
WSDL interfaces
Security
Catalog
Portal Service
Warehouse
Shipping
control
WSDL interfaces
Web Services

35. Services and Distributed Objects
A web service is a computer program running on either the local or remote machine with a set of well defined interfaces (ports) specified in XML (WSDL)
Web Services (WS) have many similarities with Distributed Object (DO) technology but there are some (important) technical and religious points (not easy to distinguish)
CORBA Java COM are typical DO technologies
Agents are typically SOA (Service Oriented Architecture)
Both involve distributed entities but Web Services are more loosely coupled
WS interact with messages; DO with RPC (Remote Procedure Call)
DO have “factories”; WS manage instances internally and interaction-specific state not exposed and hence need not be managed
DO have explicit state (statefull services); WS use context in the messages to link interactions (statefull interactions)
Claim: DO’s do NOT scale; WS build on experience (with CORBA) and do scale

36. Details of Web Service Protocol Stack
UDDI finds where programs are
remote (distributed) programs are just Web Services
(not clearly a great success)
WSFL links programs together (under revision as BPEL)
WSDL defines interface (methods, parameters, data formats)
SOAP defines structure of message including serialization of information
HTTP is negotiation/transport protocol
TCP/IP is layers 3-4 of OSI
Physical Network is layer 1 of OSI
UDDI or WSIL
WSFL
WSDL
SOAP or RMI
HTTP or SMTP or IIOP or RMTP
TCP/IP
Physical Network

37. Classic Grid Architecture
Resources
Database
Database
Content Access
Composition
Middle Tier Brokers Service Providers
Netsolve
Security
Collaboration
Computing
Middle Tier becomes Web Services
Clients
Users and Devices

38. Grid Services for the Education Process
“Learning Object” XML standards already exist
Registration
Performance (grading)
Authoring of Curriculum
Online laboratories for real and virtual instruments
Homework submission
Quizzes of various types (multiple choice, random parameters)
Assessment data access and analysis
Synchronous Delivery of Curricula including Audio/Video Conferencing and other synchronous collaborative tools as Web Services
Scheduling of courses and mentoring sessions
Asynchronous access, data-mining and knowledge discovery
Learning Plan agents to guide students and teachers

39. Grid Learning Model
Education and Research Grids share some services both for content and “process”
For example collaboration services are largely identical
Research will use much larger simulation engines to get high resolution results
Maybe a researcher uses a CAVE to visualize; education a Macintosh
But both can share data services but run through different filters to select for precision (research) or pedagogical value (education)
Education has “digital textbook” frontend to resources of the research Grid
Both use same workflow technologies to link services together

40. Repositories Federated Databases SERVOGrid for e-Education
Coarse grain simulations
Analysis and Visualization
Repositories Federated Databases
Field Trip Data
Streaming Data
Loosely Coupled Filters
Sensors ?
Discovery
Services
SERVOGrid for e-Education

41. Some Observations
“Traditional “ Grids manage and share asynchronous resources in a rather centralized fashion
Peer-to-peer networks are “just like” Grids with different implementations of message-based services like registration and look-up
Collaboration systems like WebEx/Placeware (Application sharing) or Polycom (audio/video conferencing) can be viewed as Grids
Computers are fast and getting faster. One can afford many strategies that used to be unrealistic including rich usually XML based messaging
Web Services interact with messages
Everything (including applications like PowerPoint) will be a Web Service?
Grids, P2P Networks, Collaborative Environments are (will be) managed message-linked Web Services

42. Peer to Peer Grid Peers Peers A democratic organization
Database
Database
Peers
Service Facing Web Service Interfaces
Event/ Message Brokers
Peer to Peer Grid
Peers
User Facing Web Service Interfaces
A democratic organization
Peer to Peer Grid

43. System and Application Services?
There are generic Grid system services: security, collaboration, persistent storage, universal access
OGSA (Open Grid Service Architecture) is implementing these as extended Web Services
An Application Web Service is a capability used either by another service or by a user
It has input and output ports – data is from sensors or other services
Consider Satellite-based Sensor Operations as a Web Service
Satellite management (with a web front end)
Each tracking station is a service
Image Processing is a pipeline of filters – which can be grouped into different services
Data storage is an important system service
Big services built hierarchically from “basic” services
Portals are the user (web browser) interfaces to Web services

44. Satellite Science Grid Environment

45. What is Happening?
Grid ideas are being developed in (at least) three communities
Web Service – W3C, OASIS
Grid Forum (High Performance Computing, e-Science)
Service Standards are being debated
Grid Operational Infrastructure is being deployed
Grid Architecture and core software being developed
Particular System Services are being developed “centrally” – OGSA framework for this in
Lots of fields are setting domain specific standards and building domain specific services
There is a lot of hype
Grids are viewed differently in different areas
Largely “computing-on-demand” in industry (IBM, Oracle, HP, Sun)
Largely distributed collaboratories in academia

46. OGSA OGSI & Hosting Environments
Start with Web Services in a hosting environment
Add OGSI to get a Grid service and a component model
Add OGSA to get Interoperable Grid “correcting” differences in base platform and adding key functionalities
OGSI on Web Services
Broadly applicable services: registry,
authorization, monitoring, data
access, etc., etc.
Hosting Environment for WS
More specialized services: data
replication, workflow, etc., etc.
Domain -
specific services
Network
OGSA
Environment
Possibly OGSA
Not OGSA
Given to us from on high

47. Technical Activities of Note
Look at different styles of Grids such as Autonomic (Robust Reliable Resilient)
New Grid architectures hard due to investment required
Critical Services Such as
Security – build message based not connection based
Notification – event services
Metadata – Use Semantic Web, provenance
Databases and repositories – instruments, sensors
Computing – Submit job, scheduling, distributed file systems
Visualization, Computational Steering
Fabric and Service Management
Network performance
Program the Grid – Workflow
Access the Grid – Portals, Grid Computing Environments

48. Issues and Types of Grid Services
Lightweight
P2P
Federation and Interoperability
2) Core Infrastructure and Hosting Environment
Service Management
Component Model
Service wrapper/Invocation
Messaging
3) Security Services
Certificate Authority
Authentication
Authorization
Policy
4) Workflow Services and Programming Model
Enactment Engines (Runtime)
Languages and Programming
Compiler
Composition/Development
5) Notification Services
6) Metadata and Information Services
Basic including Registry
Semantically rich Services and meta-data
Information Aggregation (events)
Provenance
7) Information Grid Services
OGSA-DAI/DAIT
Integration with compute resources
P2P and database models
8) Compute/File Grid Services
Job Submission
Job Planning Scheduling Management
Access to Remote Files, Storage and Computers
Replica (cache) Management
Virtual Data
Parallel Computing
9) Other services including
Grid Shell
Accounting
Fabric Management
Visualization Data-mining and Computational Steering
Collaboration
10) Portals and Problem Solving Environments
11) Network Services
Performance
Reservation
Operations

49. Data Technology Components of (Services in) a Computing Grid
Remote Grid Service
10: Job
Status
1: Job Management Service
(Grid Service Interface to user or program client)
1: Plan Execution
4: Job Submittal
2: Schedule and control Execution
8: Virtual Data
3: Access to Remote Computers
7: Cache Data Replicas
6: File and
Storage
Access
Data
5: Data Transfer
Data
Technology Components of (Services in) a Computing Grid
9: Grid MPI

50. Approach Build on e-Science methodology and Grid technology
Application WS
Approach
Typical codes
WS linking
to user and
Other WS
(data sources)
Build on e-Science methodology and Grid technology
Science applications with multi-scale models, scalable parallelism, data assimilation as key issues
Data-driven models for earthquakes, climate, environment …..
Use existing code/database technology (SQL/Fortran/C++) linked to “Application Web/OGSA services”
XML specification of models, computational steering, scale supported at “Web Service” level as don’t need “high performance” here
Allows use of Semantic Grid technology

51. Grid Computing Environments
Raw (HPC) Resources
Middleware
Database
Portal
Services
System Services
Application Service
User Services
Grid Computing Environments
Application Metadata
Actual Application
“Core” Grid

52. Why we can dream of using HTTP and that slow stuff
We have at least three tiers in computing environment
Client (user portal)
“Middle Tier” (Web Servers/brokers)
Back end (databases, files, computers etc.)
In Grid programming, we use HTTP (and used to use CORBA and Java RMI) in middle tier ONLY to manipulate a proxy for real job
Proxy holds metadata
Control communication in middle tier only uses metadata
“Real” (data transfer) high performance communication in back end

53. Virtualization
The Grid could and sometimes does virtualize various concepts – should do more
Location: URI (Universal Resource Identifier) virtualizes URL (WSAddressing goes further)
Replica management (caching) virtualizes file location generalized by GriPhyn virtual data concept
Protocol: message transport and WSDL bindings virtualize transport protocol as a QoS request
P2P or Publish-subscribe messaging virtualizes matching of source and destination services
Semantic Grid virtualizes Knowledge as a meta-data query
Brokering virtualizes resource allocation
Virtualization implies all references can be indirect and needs powerful mapping (look-up) services -- metadata

54. Integration of Data and Filters
One has the OGSA-DAI Data repository interface combined with WSDL of the (Perl, Fortran, Python …) filter
User only sees WSDL not data syntax
Some non-trivial issues as to where the filtering compute power is
Microsoft says filter next to data DB
Filter
WSDL
Of Filter
OGSA-DAI
Interface

55. Complexity Simulation Service Visualization Service
SERVOGrid Complexity Computing Environment
Database
Parallel
Simulation Service
Database Service
Compute Service
Sensor
Service
Middle Tier with XML Interfaces
Application Service-1
XML Meta-data Service
Application Service-2
CCE Control
Portal Aggregation
Complexity Simulation Service
Application Service-3
Users
Visualization Service

56. SERVOGrid (Complexity) Computing Model
Data
Filter
Data
Filter
OGSA-DAI Grid Services
Analysis Control
Visualize
Grid
Data
Filter
This Type of Grid
integrates with
Parallel computing
Multiple HPC facilities but only use one at a time
Many simultaneous data sources and sinks
Grid Data
Assimilation
HPC
Simulation
Data
Filter
Other Grid and Web Services
Distributed Filters
massage data
For simulation
Data
Filter
SERVOGrid (Complexity) Computing Model

57. Two-level Programming I
The paradigm implicitly assumes a two-level Programming Model
We make a Service (same as a “distributed object” or “computer program” running on a remote computer) using conventional technologies
C++ Java or Fortran Monte Carlo module
Data streaming from a sensor or Satellite
Specialized (JDBC) database access
Such services accept and produce data from users files and databases
The Grid is built by coordinating such services assuming we have solved problem of programming the service
Service
Data

58. Two-level Programming II
The Grid is discussing the composition of distributed services with the runtime interfaces to Grid as opposed to UNIX pipes/data streams
Familiar from use of UNIX Shell, PERL or Python scripts to produce real applications from core programs
Such interpretative environments are the single processor analog of Grid Programming
Some projects like GrADS from Rice University are looking at integration between service and composition levels but dominant effort looks at each level separately
Service1
Service2
Service3
Service4

59. Conclusions Grids are inevitable and pervasive
Can expect Web Services and Grids to merge with a common set of general principles but different implementations with different scaling and functionality trade-offs
e-Science will grow in importance as Science grows as an international “team sport”; affects scientists and organizations
Enough is known that one can start today
We will be flooded with data, information and purported knowledge
One should be learning about Grids; understanding relevant Web and Grid standards and developing new domain specific standards
Note many existing (standards) efforts assume client-server and not a brokered service model; these will need to change!