Transcript Web 2.0, Grids and Parallel Computing Tsinghua University October 31 2007 Geoffrey Fox Community Grids Laboratory, School of informatics Indiana University http://www.infomall.org/multicore [email protected], http://www.infomall.org.

Web 2.0, Grids
and Parallel Computing
Tsinghua University
October 31 2007
Geoffrey Fox
Community Grids Laboratory, School of informatics
Indiana University
http://www.infomall.org/multicore
[email protected], http://www.infomall.org
1
Abstract of Web 2.0,
Grids and Parallel Computing
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We discuss the application of Web 2.0 to support scientific research
(e-Science) and related e-moreorlessanything applications.
Web 2.0 offers interesting technical approaches to build the core einfrastructure (Cyberinfrastructure) as well as a host of interesting
services exemplified by Facebook, YouTube, Amazon S3/EC2 and
Google maps.
We discuss why some of the original Grid goals of linking the
world's computer systems may not be so relevant today and that
interoperability is needed at the data and not always at the
infrastructure level.
Web 2.0 may also support Parallel Programming 2.0 -- a better
parallel computing software environment motivated by the need to
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run commodity applications on multicore chips.
Applications, Infrastructure,
Technologies
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This field is confused by inconsistent use of terminology; I define
Web Services, Grids and (aspects of) Web 2.0 (Enterprise 2.0) are
technologies
Grids could be everything (Broad Grids implementing some sort
of managed web) or reserved for specific architectures like OGSA
or Web Services (Narrow Grids)
These technologies combine and compete to build electronic
infrastructures termed e-infrastructure or Cyberinfrastructure
e-moreorlessanything is an emerging application area of broad
importance that is hosted on the infrastructures e-infrastructure
or Cyberinfrastructure
e-Science or perhaps better e-Research is a special case of emoreorlessanything
e-moreorlessanything
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‘e-Science is about global collaboration in key areas of science,
and the next generation of infrastructure that will enable it.’ from
its inventor John Taylor Director General of Research Councils
UK, Office of Science and Technology
e-Science is about developing tools and technologies that allow
scientists to do ‘faster, better or different’ research
Similarly e-Business captures an emerging view of corporations as
dynamic virtual organizations linking employees, customers and
stakeholders across the world.
This generalizes to e-moreorlessanything including presumably eTsinghuaResearch and e-olympics, e-chinesefood ….
A deluge of data of unprecedented and inevitable size must be
managed and understood.
People (see Web 2.0), computers, data (including sensors and
instruments) must be linked.
On demand assignment of experts, computers, networks and
storage resources must be supported
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What is Cyberinfrastructure
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Cyberinfrastructure is (from NSF) infrastructure that
supports distributed science (e-Science)– data, people,
computers
• Clearly core concept more general than Science
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Exploits Internet technology (Web2.0) adding (via Grid
technology) management, security, supercomputers etc.
It has two aspects: parallel – low latency (microseconds)
between nodes and distributed – highish latency (milliseconds)
between nodes
Parallel needed to get high performance on individual large
simulations, data analysis etc.; must decompose problem
Distributed aspect integrates already distinct components –
especially natural for data
Cyberinfrastructure is in general a distributed collection of
parallel systems
Cyberinfrastructure is made of services (originally Web
services) that are “just” programs or data sources packaged
for distributed access
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Service or Web Service Approach
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One uses GML, CML etc. to define the data structure in a system
and one uses services to capture “methods” or “programs”
In eScience, important services fall in three classes
• Simulations
• Data access, storage, federation, discovery
• Filters for data mining and manipulation
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Services could use something like WSDL (Web Service Definition
Language) to define interoperable interfaces but Web 2.0 follows
old library practice: one just specifies interface
Service Interface (WSDL) establishes a “contract” independent
of implementation between two services or a service and a client
Services should be loosely coupled which normally means they
are coarse grain
Services will be composed (linked together) by mashups
(typically scripts) or workflow (often XML – BPEL)
Software Engineering and Interoperability/Standards are closely
related
Relevance of Web 2.0
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They say that Web 1.0 was a read-only Web while Web
2.0 is the wildly read-write collaborative Web
Web 2.0 can help e-Science in many ways
Its tools can enhance scientific collaboration, i.e.
effectively support virtual organizations, in different
ways from grids
The popularity of Web 2.0 can provide high quality
technologies and software that (due to large
commercial investment) can be very useful in e-Science
and preferable to Grid or Web Service solutions
The usability and participatory nature of Web 2.0 can
bring science and its informatics to a broader audience
Web 2.0 can even help the emerging challenge of using
multicore chips i.e. in improving parallel computing
programming and runtime environments
“Best Web 2.0 Sites” -- 2006
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Extracted from http://web2.wsj2.com/
All important capabilities for e-Science
Social Networking
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Start Pages
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Social Bookmarking
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Peer Production News
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Social Media Sharing
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Online Storage
(Computing)
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Web 2.0, Grids and Web Services I
Web Services have clearly defined protocols (SOAP) and a well
defined mechanism (WSDL) to define service interfaces
• There is good .NET and Java support
• The so-called WS-* specifications provide a rich sophisticated but
complicated standard set of capabilities for security, fault tolerance, metadata, discovery, notification etc.
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“Narrow Grids” build on Web Services and provide a robust
managed environment with growing but still small adoption in
Enterprise systems and distributed science (so called e-Science)
Web 2.0 supports a similar architecture to Web services but has
developed in a more chaotic but remarkably successful fashion
with a service architecture with a variety of protocols including
those of Web and Grid services
• Over 500 Interfaces defined at http://www.programmableweb.com/apis
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Web 2.0 also has many well known capabilities with Google
Maps and Amazon Compute/Storage services of clear general
relevance
There are also Web 2.0 services supporting novel collaboration
modes and user interaction with the web as seen in social
networking sites, portals, MySpace, YouTube
Web 2.0 Systems like Grids have Portals, Services, Resources
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Captures the incredible development of interactive Web
sites enabling people to create and collaborate
Web 2.0, Grids and Web Services II
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I once thought Web Services were inevitable but this is no longer
clear to me
Web services are complicated, slow and non functional
• WS-Security is unnecessarily slow and pedantic
(canonicalization of XML)
• WS-RM (Reliable Messaging) seems to have poor adoption
and doesn’t work well in collaboration
• WSDM (distributed management) specifies a lot
There are de facto Web 2.0 standards like Google Maps and
powerful suppliers like Google/Microsoft which “define the
architectures/interfaces”
One can easily combine SOAP (Web Service) based
services/systems with HTTP messages but dominance of “lowest
common denominator” suggests additional structure/complexity
of SOAP will not easily survive
Distribution of APIs and Mashups per
Protocol
google
maps
Number of
APIs
Number of
Mashups
del.icio.us
411sync
yahoo! search
yahoo! geocoding
SOAP is quite a small fraction
virtual
earth
technorati
netvibes
yahoo! images
trynt
yahoo! local
amazon
ECS
google
search
flickr
SOAP
ebay
youtube
amazon S3
REST
live.com
XML-RPC
REST,
XML-RPC
REST,
XML-RPC,
SOAP
REST,
SOAP
JS
Other
Where did Narrow Grids and Web Services go wrong?
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Too much Computing: historically one (including narrow grids) has tried to
increase computing capabilities by
• Optimizing performance of codes at cost of re-usability
• Exploiting all possible CPU’s such as Graphics co-processors and “idle
cycles” (across administrative domains)
• Linking central computers together such as NSF/DoE/DoD
supercomputer networks without clear user requirements
Next Crisis in technology area will be the opposite problem – commodity
chips will be 32-128way parallel in 5 years time and we currently have no
idea how to use them – especially on clients
• Only 2 releases of standard software (e.g. Office) in this time span
Interoperability Interfaces will be for data not for infrastructure
• Google, Amazon, TeraGrid, European Grids will not interoperate at the
resource or compute (processing) level but rather at the data streams
flowing in and out of independent Grid islands
• Data focus is consistent with Semantic Grid/Web but not clear if latter
has learnt the usability message of Web 2.0
One needs to share computing, data, people in e-moreorlessanything, Grids
initially focused on computing but data and people are more important
eScience is healthy as is e-moreorlessanything
Most Grids are solving wrong problem at wrong point in stack with a
complexity that makes friendly usability difficult
Information System Architecture
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The Party Line approach to Information Infrastructure is clear –
one creates a Cyberinfrastructure consisting of distributed
services accessed by portals/gadgets/gateways/RSS feeds
Services include:
• “Original data”
• Transformations or filters implementing DIKW (Data Information
Knowledge Wisdom) lattice
• Some filters could correspond to large simulations
• Final “Decision Support” step converting wisdom into action
• Generic services such as security, profiles etc.
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Infrastructure will be set up as a System of Systems (Grids of
Grids)
Services and/or Grids just accept some form of DIKW and
produce another form of DIKW
• “Original data” has no explicit input; just output
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e-moreorlessanything Interoperability at DIKW interface not at
details of computing and repository resources
Raw Data 
Dataand

Information

Knowledge
 Wisdom
Information
Cyberinfrastructure
Another
S
S
FS
FS
SS
FS
FS
MD
O
S
FS
O
S
FS
MD
MD
O
S
FS
F
S
O
S
MD
Filter Service
FS
O
S
FS
Other
Service
MD
O
S
FS
MetaData
SS
S
S
Database
FS
FS
F
S
FS
MD
MD
FS
O
S
FS
FS
O
S
SS
Another
Grid
O
S
FS
SS
SS
S
S
Another
Service
O
S
O
S
SS
FS
MD
MD
FS
SS
FS
S
S
O
S
S
S
Another
Grid
Decisions
Grid
S
S
S
S
Another
Service
S
S
S
S
S
S
S
S
S
S
S
S
Sensor Service
Some Web 2.0 Activities at IU
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Use of Blogs, RSS feeds, Wikis etc.
Use of Mashups for Cheminformatics Grid workflows
Moving from Portlets to Gadgets in portals (or at least
supporting both)
Use of Connotea to produce tagged document collections
such as http://www.connotea.org/user/crmc for parallel
computing
Semantic Research Grid integrates multiple tagging and
search systems and copes with overlapping inconsistent
annotations
MSI-CIEC portal augments Connotea to tag a mix of
URL and URI’s e.g. NSF TeraGrid use, PI’s and
Proposals
• Hopes to support collaboration (for Minority Serving
Institution faculty)
Multicore SALSA project using for Parallel Programming 2.0
Use blog to
create posts.
Display blog RSS
feed in MediaWiki.
Semantic Research Grid (SRG)
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Integrates tagging and search system that allows users to use
multiple sites and consistently integrate them with traditional
citation databases
We built a mashup linking to del.icio.us, CiteULike, Connotea
allowing exchange of tags between sites and between local
repositories
Repositories also link to local sources (PubsOnline) and Google
Scholar (GS) and Windows Academic Live (WLA)
• GS has number of cited publications.
• WLA has Digital Object Identifier (DOI)
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We implement a rather more powerful access control mechanism
We build heuristic tools to mine “web lists” for citations
We have an “event” based architecture (consistency model)
allowing change actions to be preserved and selectively changed
• Supports integrating different inconsistent views of a given document and
its updates on different tagging systems
11/7/2015
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Semantic Scholars Grid
Export:
RSS, Bibtex
Endnote etc.
Windows Live
Academic Search
Traditional Grid
Cyberinfrastructure
Google Scholar
Citeseer
Bibliographic
Database
MyResearch
Database
Web 2.0
MySpace
Del.icio.us
CiteULike
Connotea
Science.gov
Bibsonomy
PubChem
Generic Document Tools
Community Tools
Integration/
Enhancement
User Interface
New Document-enhanced
Research Tools
M
A
S
H
U
P
Biolicious
PubMed
CMT
Conference
Management
Manuscript
Central
etc.
Existing
User Interface
Web service
Wrappers
Existing Document
based Tools
Example
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Parallel
Computing
Collection
selected on
Cell Tag
So far no clear
“winner” in
tagging space
Maybe
CiteUlike with
different
metadata
better
How do I
preserve
investment?
del.icio.us Tags
Download to
Local System
MSI-CIEC Portal
MSI-CIEC
Minority Serving Institution CyberInfrastructure Empowerment Coalition
NSF Grants Tag System
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NSF has the ability to get information (in XML) on all of
the grants a particular person worked on
We downloaded, parsed, and bookmarked this info using a
little scavenger robot.
• Each grant is represented by a bookmark and tagged with
relevant information in MSI-CIEC Portal
• Grant tags point to URLs of the NSF award page.
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The investigators are imported as users
Each has a bookmark for each project they worked on
• They are also represented in the tags of these projects.
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Can now form research collaborations by linking
researchers with common tags
Hopefully will enable broader collaborations and not
Superior (from broad usage)
technologies of Web 2.0
Mash-ups can replace Worflow
Gadgets can replace Portlets
UDDI replaced by user generated
registries
Mashups v Workflow?
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Mashup Tools are reviewed at
http://blogs.zdnet.com/Hinchcliffe/?p=63
Workflow Tools are reviewed by Gannon and Fox
http://grids.ucs.indiana.edu/ptliupages/publications/Workflow-overview.pdf
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Both include scripting
in PHP, Python, sh etc.
as both implement
distributed
programming at level
of services
Mashups use all types
of service interfaces
and perhaps do not
have the potential
robustness (security) of
Grid service approach
Mashups typically
“pure” HTTP (REST)
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Grid Workflow Datamining in Earth Science
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NASA GPS
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Work with Scripps Institute
Grid services controlled by scripting workflow process
real time data from ~70 GPS Sensors in Southern
California
Earthquake
Streaming Data
Support
Archival
Transformations
Data Checking
Hidden Markov
Datamining (JPL)
Real Time
Display (GIS)
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Grid Workflow Data Assimilation in Earth Science
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Grid services triggered by abnormal events and controlled by workflow process real
time data from radar and high resolution simulations for tornado forecasts
Typical
graphical
interface to
service
composition
Taverna another well known Grid/Web Service workflow tool
Recent Web 2.0 visual Mashup tools include Yahoo Pipes and
Microsoft Popfly
Web 2.0 Mashups
and APIs
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http://www.programmable
web.com/apis has (Sept 12
2007) 2312 Mashups and
511 Web 2.0 APIs and with
GoogleMaps the most often
used in Mashups
This is the Web 2.0 UDDI
(service registry)
The List of Web
2.0 API’s
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Each site has API and
its features
Divided into broad
categories
Only a few used a lot
(49 API’s used in 10
or more mashups)
RSS feed of new APIs
Google maps
dominates but
Amazon S3 growing
in popularity
Grid-style portal as used in Earthquake Grid
The Portal is built from portlets
– providing user interface
fragments for each service
that are composed into the
full interface – uses OGCE
technology as does planetary
QuakeSim has a typical Grid technology portal
science VLAB portal with
Such Server side Portlet-based approaches to portals are University
being challenged
by client
of Minnesota
side gadgets from Web 2.0
Now to Portals
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Note the many competitions powering Web 2.0
Mashup and Gadget Development
Portlets v. Google Gadgets
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Portals for Grid Systems are built using portlets with
software like GridSphere integrating these on the
server-side into a single web-page
Google (at least) offers the Google sidebar and Google
home page which support Web 2.0 services and do not
use a server side aggregator
Google is more user friendly!
The many Web 2.0 competitions is an interesting model
for promoting development in the world-wide
distributed collection of Web 2.0 developers
I guess Web 2.0 model will win!
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Typical Google Gadget Structure
Google Gadgets are an example of
Start Page Web 2.0 term for portals)
technology
See http://blogs.zdnet.com/Hinchcliffe/?p=8
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… Lots of HTML and JavaScript </Content> </Module>
Portlets build User Interfaces by combining fragments in a standalone Java Server
Google Gadgets build User Interfaces by combining fragments with JavaScript on the client
Web 2.0 can also help address
long standing difficulties with
parallel programming
environments
Too much computing addresses too much data and
implies need for multicore datamining algorithms
Clustering
Principal Component Analysis (SVD)
Expectation-Maximization EM (mixture models)
Hidden Markov Models HMM
Multicore SALSA at CGL
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Service Aggregated Linked Sequential Activities
• http://www.infomall.org/multicore
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Aims to link parallel and distributed (Grid) computing
by developing parallel applications as services and not
as programs or libraries
• Improve traditionally poor parallel programming
development environments
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Can use messaging to link parallel and Grid services
but performance – functionality tradeoffs different
• Parallelism needs few µs latency for message latency and
thread spawning
• Network overheads in Grid 10-100’s µs
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Developing set of services (library) of multicore parallel
data mining algorithms
Parallel Programming Model
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If multicore technology is to succeed, mere mortals must be able to build
effective parallel programs
There are interesting new developments – especially the Darpa HPCS
Languages X10, Chapel and Fortress
However if mortals are to program the 64-256 core chips expected in 5-7
years, then we must use today’s technology and we must make it easy
• This rules out radical new approaches such as new languages
The important applications are not scientific computing but most of the
algorithms needed are similar to those explored in scientific parallel
computing
• Intel RMS analysis
We can divide problem into two parts:
• High Performance scalable (in number of cores) parallel kernels or
libraries
• Composition of kernels into complete applications
We currently assume that the kernels of the scalable parallel
algorithms/applications/libraries will be built by experts with a
Broader group of programmers (mere mortals) composing library
members into complete applications.
Scalable Parallel Components
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There are no agreed high-level programming environments for
building library members that are broadly applicable.
However lower level approaches where experts define
parallelism explicitly are available and have clear performance
models.
These include MPI for messaging or just locks within a single
shared memory.
There are several patterns to support here including the
collective synchronization of MPI, dynamic irregular thread
parallelism needed in search algorithms, and more specialized
cases like discrete event simulation.
We use Microsoft CCR
http://msdn.microsoft.com/robotics/ as it supports both MPI
and dynamic threading style of parallelism
• It already supports a Web 2.0 compatible service model DSS
Composition of Parallel Components
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The composition step has many excellent solutions as this does not
have the same drastic synchronization and correctness constraints as
for scalable kernels
• Unlike kernel step which has no very good solutions
Task parallelism in languages such as C++, C#, Java and Fortran90;
General scripting languages like PHP Perl Python
Domain specific environments like Matlab and Mathematica
Functional Languages like MapReduce, F#
HeNCE, AVS and Khoros from the past and CCA from DoE
Web Service/Grid Workflow like Taverna, Kepler, InforSense KDE,
Pipeline Pilot (from SciTegic) and the LEAD environment built at
Indiana University.
Web solutions like Mash-ups and DSS
Many scientific applications use MPI for the coarse grain composition
as well as fine grain parallelism but this doesn’t seem elegant
The new languages from Darpa’s HPCS program support task
parallelism (composition of parallel components) decoupling
composition and scalable parallelism will remain popular and must be
supported.
Parallel Programming 2.0
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Web 2.0 Mashups will (by definition the largest
market) drive composition tools for Grid, web and
parallel programming
Parallel Programming 2.0 will build on Mashup tools
like Yahoo Pipes and Microsoft Popfly
Yahoo Pipes
MPI Exchange Latency in µs (20-30 µs computation between messaging)
Machine
OS
Runtime
Grains
Parallelism
MPI Exchange
Latency
Intel8c:gf12
(8 core 2.33 Ghz)
(in 2 chips)
Redhat
MPJE (Java)
Process
8
181
MPICH2 (C)
Process
8
40.0
MPICH2: Fast
Process
8
39.3
Process
SALSANemesis
Performance
8
4.21
Intel8c:gf20
(8 core 2.33 Ghz)
Fedora
MPJE
Process
8
157
mpiJava
Process
8
111
The macroscopic inter-service
DSS Overhead
is about
35µs
MPICH2
Process
8
Process
8
64.2
Intel8b
Vista
DSS
from
(8 core is
2.66composed
Ghz)
Fedora
MPJE
170
AMD4
(4 core 2.19 Ghz)
XP
MPJE
Process
4
185
Redhat
MPJE
Process
4
152
mpiJava
Process
4
99.4
MPICH2
Process
4
39.3
XP
CCR
Thread
4
16.3
XP
CCR
Thread
4
25.8
CCRMPJE
threads that
Processhave
8
142
4µs overhead for
spawningmpiJava
threads inProcess
dynamic search
applications
Fedora
8
100
20µs overhead for
MPI Exchange
Vista
CCR (C#)
Thread
8
20.2
Intel4 (4 core 2.8 Ghz)
Total
Clustering is typical of data
mining methods that are needed for
Total
tomorrow’s clients or servers bathed in a data rich environment
Asian
Clustering Census data in
Indiana on dual quadcore processors
Asian
Implemented with CCR and DSS
Hispanic
Hispanic
Use deterministic annealing that uses multiscale method to avoid
local minima
Purdue
Renters
Renters
Efficiency is 90% limitedRenters
by peculiar Windows thread scheduling
effects
IUB
30 Clusters
10 Clusters
Parallel Multicore GIS
Deterministic Annealing Clustering
Parallel Overhead
on 8 Threads Intel 8b
0.45
10 Clusters
0.4
Overhead = Constant1 + Constant2/n
Speedup = 8/(1+Overhead)
0.35
Constant1 = 0.02 to 0.1 (Windows) due to thread
runtime fluctuations
0.3
0.25
20 Clusters
0.2
0.15
0.1
0.05
10000/(Grain Size n = points per core)
0
0
0.5
1
1.5
2
2.5
3
3.5
4
Web 2.0 v Narrow Grid I
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Web 2.0 and Grids are addressing a similar application class
although Web 2.0 has focused on user interactions
• So technology has similar requirements
Web 2.0 chooses simplicity (REST rather than SOAP) to lower
barrier to everyone participating
Web 2.0 and Parallel Computing tend to use traditional (possibly
visual) (scripting) languages for equivalent of workflow whereas
Grids use visual interface backend recorded in BPEL
Web 2.0 and Grids both use SOA Service Oriented Architectures
Services will be used everywhere: Grids, Web 2.0 and Parallel
Computing
“System of Systems”: Grids and Web 2.0 are likely to build
systems hierarchically out of smaller systems
• We need to support Grids of Grids, Webs of Grids, Grids of
Services etc. i.e. systems of systems of all sorts
• Web 2.0 suggest data not infrastructure system linkage
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Web 2.0 v Narrow Grid II
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Web 2.0 has a set of major services like GoogleMaps or Flickr
but the world is composing Mashups that make new composite
services
• End-point standards are set by end-point owners
• Many different protocols covering a variety of de-facto standards
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Narrow Grids have a set of major software systems like Condor
and Globus and a different world is extending with custom
services and linking with workflow
Popular Web 2.0 technologies are PHP, JavaScript, JSON,
AJAX and REST with “Start Page” e.g. (Google Gadgets)
interfaces
Popular Narrow Grid technologies are Apache Axis, BPEL
WSDL and SOAP with portlet interfaces
Robustness of Grids demanded by the Enterprise?
Not so clear that Web 2.0 won’t eventually dominate other
application areas and with Enterprise 2.0 it’s invading Grids
The world does itself in large numbers!
Web 2.0 v Narrow Grid III
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Narrow Grids have a strong emphasis on standards and structure
Web 2.0 lets a 1000 flowers (protocols) and a million developers
bloom and focuses on functionality, broad usability and simplicity
• Interoperability at user (data) level not at service level
• Puts semantics into application (user) level (like KML for maps)
and minimizes general system level semantics
Semantic Web/Grid has structure to allow reasoning
• Annotation in sites like del.icio.us and uploading to
MySpace/YouTube is unstructured and free text search replaces
structured ontologies?
• Flickr has geocoded (structured) and unstructured tags
Portals are likely to feature both Web and “desktop client”
technology although it is possible that Web approach will be
adopted more or less uniformly
Web 2.0 has a very active portal activity which has similar
architecture to Grids
• A page has multiple user interface fragments
Web 2.0 user interface integration is typically Client side using
Gadgets AJAX and JavaScript while
• Grids are in a special JSR168 portal server side using Portlets
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WSRP and Java
The Ten areas covered by the 60 core WS-*
Specifications
WS-* Specification Area
Typical Grid/Web Service Examples
1: Core Service Model
XML, WSDL, SOAP
2: Service Internet
WS-Addressing, WS-MessageDelivery; Reliable
Messaging WSRM; Efficient Messaging MOTM
3: Notification
WS-Notification, WS-Eventing (PublishSubscribe)
4: Workflow and Transactions
BPEL, WS-Choreography, WS-Coordination
5: Security
WS-Security, WS-Trust, WS-Federation, SAML,
WS-SecureConversation
6: Service Discovery
UDDI, WS-Discovery
7: System Metadata and State
WSRF, WS-MetadataExchange, WS-Context
8: Management
WSDM, WS-Management, WS-Transfer
9: Policy and Agreements
WS-Policy, WS-Agreement
10: Portals and User Interfaces
WSRP (Remote Portlets)
WS-* Areas and Web 2.0
WS-* Specification Area
Web 2.0 Approach
1: Core Service Model
XML becomes optional but still useful
SOAP becomes JSON RSS ATOM
WSDL becomes REST with API as GET PUT etc.
Axis becomes XmlHttpRequest
2: Service Internet
No special QoS. Use JMS or equivalent?
3: Notification
Hard with HTTP without polling– JMS perhaps?
4: Workflow and Transactions
(no Transactions in Web 2.0)
Mashups, Google MapReduce
Scripting with PHP JavaScript ….
5: Security
SSL, HTTP Authentication/Authorization,
OpenID is Web 2.0 Single Sign on
6: Service Discovery
http://www.programmableweb.com
7: System Metadata and State
Processed by application – no system state –
Microformats are a universal metadata approach
8: Management==Interaction
WS-Transfer style Protocols GET PUT etc.
9: Policy and Agreements
Service dependent. Processed by application
10: Portals and User Interfaces Start Pages, AJAX and Widgets(Netvibes) Gadgets
Looking to the Future
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Web 2.0 has momentum as it is driven by success of social web
sites and the user friendly protocols attracting many developers
of mashups
Grids momentum driven by the success of eScience and the
commercial web service thrusts largely aimed at Enterprise
We expect applications such as business and military where
predictability and robustness important might be built on a Web
Service (Narrow Grid) core with perhaps Web 2.0 functionality
enhancements
• But even this Web Service application may not survive
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Multicore usability driving Parallel Programming 2.0
Simplicity, supporting many developers are forces pressuring
Grids!
Robustness and coping with unstructured blooming of a 1000
flowers are forces pressuring Web 2.0