Web 2.0 for e-Science

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