1
Web 2.0 for e
-
Science
Environments
SKG2007
Xi’an Hotel, Xi’an China
October 29 2007
Geoffrey Fox and Marlon Pierce
Computer Science, Informatics, Physics
Community Grids Laboratory
Indiana University Bloomington IN 47401
gcf@indiana.edu
http://www.infomall.org
Applications, Infrastructure,
Technologies
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
e
-
moreorlessanything
Relevance of Web 2.0
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
4
“Best Web 2.0 Sites”
--
2006
Extracted from
http://web2.wsj2.com/
All important capabilities for e
-
Science
Social Networking
Start Pages
Social Bookmarking
Peer Production News
Social Media Sharing
Online Storage
(Computing)
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, meta
-
data, discovery, notification etc.
“
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
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
Captures the incredible development of interactive
Web sites enabling people to create and collaborate
Web 2.0, Grids and Web Services II
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
REST
SOAP
XML
-
RPC
REST,
XML
-
RPC
REST,
XML
-
RPC,
SOAP
REST,
SOAP
JS
Other
google
maps
netvibes
live.com
virtual
earth
google
search
amazon S3
amazon
ECS
flickr
ebay
youtube
411sync
del.icio.us
yahoo! search
yahoo! geocoding
technorati
yahoo! images
trynt
yahoo! local
Number of
Mashups
Number of
APIs
SOAP is quite a small fraction
Where did Narrow Grids and Web Services go wrong?
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
Some Web 2.0 Activities at IU
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)
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)
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
11/4/2013
12
MSI
-
CIEC Portal
MSI
-
CIEC
Minority Serving Institution CyberInfrastructure Empowerment Coalition
NSF Grants Tag System
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.
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.
Can now
form research collaborations
by linking
researchers with common tags
Hopefully will enable
broader collaborations
and not
just those between “usual suspects”
Superior (from broad usage)
technologies of Web 2.0
Mash
-
ups can replace Workflow
Gadgets can replace Portlets
UDDI replaced by user generated
registries
16
Mashups v Workflow?
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
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
)
17
Grid Workflow Datamining in Earth Science
Work with
Scripps Institute
Grid services
controlled by scripting
workflow
process
real time data from ~70 GPS Sensors in Southern
California
Streaming Data
Support
Transformations
Data Checking
Hidden Markov
Datamining (JPL)
Display (GIS)
NASA GPS
Earthquake
Real Time
Archival
Grid Workflow Data Assimilation in Earth Science
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
Parallel Programming 2.0
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
Web 2.0 Mashups
and APIs
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
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
Now to Portals
22
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
science VLAB portal with
University of Minnesota
QuakeSim has a typical Grid technology portal
Such Server side Portlet
-
based approaches to portals are being challenged by client
side gadgets from Web 2.0
23
Portlets v. Google Gadgets
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!
Note the many competitions powering Web 2.0
Mashup and Gadget Development
Typical Google Gadget Structure
… 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
Google Gadgets are an example of
Start Page Web 2.0 term for portals)
technology
See
http://blogs.zdnet.com/Hinchcliffe/?p=8
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 S
A
LS
A
at CGL
S
ervice
A
ggregated
L
inked
S
equential
A
ctivities
•
http://www.infomall.org/multicore
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
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
Developing set of
services (library)
of
multicore parallel
data mining algorithms
Parallel Programming Model
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
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
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.
“Service Aggregation” in
SALSA
Kernels and Composition must be supported both
inside
chips
(the multicore problem) and
between machines
in
clusters (the traditional parallel computing problem) or
Grids.
The scalable parallelism (kernel) problem is typically only
interesting on true parallel computers as the algorithms
require low communication latency.
However
composition is similar in both parallel and
distributed scenarios
and it seems useful to allow the use of
Grid
and
Web 2.0
composition tools for the parallel problem.
•
This should allow parallel computing to exploit large
investment in service programming environments
Thus in SALSA we express parallel kernels not as traditional
libraries but as (some variant of) services so they can be used
by non expert programmers
For
parallelism expressed in CCR
,
DSS
represents the
natural service (composition) model.
Inside the SALSA Services
We generalize the well known
CSP
(Communicating
Sequential Processes) of Hoare to describe the low level
approaches to fine grain parallelism as “
L
inked
S
equential
A
ctivities” in
SALSA
.
We use term “
activities
” in
SALSA
to allow one to build
services from either
threads
,
processes
(usual MPI choice)
or even just other
services
.
We choose term “
linkage
” in
SALSA
to denote the
different
ways of synchronizing
the parallel activities that may
involve
shared memory
rather than some form of
messaging or communication.
There are several engineering and research issues for
SALSA
•
There is the critical
communication optimization
problem area for communication inside chips, clusters
and Grids.
•
We need to discuss what we mean by
services
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
Nemesis
Process
8
4.21
Intel8c:gf20
(8 core 2.33 Ghz)
Fedora
MPJE
Process
8
157
mpiJava
Process
8
111
MPICH2
Process
8
64.2
Intel8b
(8 core 2.66 Ghz)
Vista
MPJE
Process
8
170
Fedora
MPJE
Process
8
142
Fedora
mpiJava
Process
8
100
Vista
CCR (C#)
Thread
8
20.2
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
Intel4
(4 core 2.8 Ghz)
XP
CCR
Thread
4
25.8
SALSA Performance
The macroscopic inter
-
service DSS Overhead is about 35
µs
DSS is composed from CCR threads that have
4
µs
overhead for spawning threads in dynamic search applications
20µs
overhead for MPI Exchange
Renters
Total
Asian
Hispanic
Renters
IUB
Purdue
10 Clusters
Total
Asian
Hispanic
Renters
30 Clusters
Clustering
is typical of data mining methods that are needed for
tomorrow’s clients or servers bathed in a
data rich environment
Clustering Census data in Indiana on
dual quadcore processors
Implemented with CCR and DSS
Use
deterministic annealing
that uses
multiscale
method to avoid
local minima
Efficiency is 90%
limited by peculiar Windows thread scheduling
effects
Parallel Multicore GIS
Deterministic Annealing Clustering
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0
0.5
1
1.5
2
2.5
3
3.5
4
Parallel Overhead
on 8 Threads Intel 8b
Speedup = 8/(1+Overhead)
10000/(Grain Size
n
= points per core)
Overhead =
Constant1
+
Constant2
/
n
Constant1 =
0.02 to 0.1 (Windows) due to thread
runtime fluctuations
10 Clusters
20 Clusters
Web 2.0 v Narrow Grid I
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
35
Web 2.0 v Narrow Grid II
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
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
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
WSRP and Java
37
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 (Publish
-
Subscribe)
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
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
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
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