Rapid Prototyping and Deployment of
Distributed Web / Grid Services in a
Service Oriented Architecture using
Scripting
Thesis Proposal
Harshawardhan Gadgil
hgadgil@cs.indiana.edu
http://www.hpsearch.org
Outline
 Motivation
 Literature Survey
 Research Issues
 HPSearch Architecture
 Contributions and Milestones
 Applications
 Summary
Motivation
 Critical Infrastructure systems connect disparate data
sources, high-performance computing applications
and visualization services for real-time data
processing.
 Real-time data processing

Results required in real-time. Data available in streams.
Requires pre-processing (e.g. filtering data to remove
unwanted parts).
 Scalability
 Potentially large number of data sources (Static,
dynamic) or data processing elements (services)
 Unpredictable behavior
 Fault-tolerance a key factor. E.g. Incorporate new data
sources or processing units on the fly
Motivation (contd.)
 System Management



Increasing complexity of application implies
more metadata.
Proper management required to ensure
smooth functioning of the system.
Require easy access to manage system
characteristics.
Motivation
Streaming data Processing

Critical Infrastructure systems
(Scientific applications)

Audio/video applications.

Real-time streaming sources exist
E.g. sensors, satellite stations

Real-time sources
E.g. Collaborative sessions
OR
Static data source (stored A/V files)
Data filtering / transformation
essential in most cases for
converting data to proper format for
processing application

Pre-processing required to modify
A/V characteristic
Real-time processing required.
Crucial for critical infrastructure
applications

OR
Static data sources (databases
containing previously warehoused
observations)



Format (encoding) / bit rate
(quality) etc…
Real-time processing crucial for
collaborative environments
Outline
 Motivation
 Literature Survey
 Research Issues
 HPSearch Architecture
 Contributions and Milestones
 Applications
 Summary
Literature Survey
 Services (Web / Grid)
 Scripting Languages
 Benefits
 Possible problems
 Handling data flow in applications
 File-based vs. Streaming
 Workflow Systems
 Enable gluing High performance components
 GUI – based building and programming flavor
 Component based architectures
 Messaging systems (for High throughput data
transfer)
 System Management
Service
 “Service is a logical manifestation of a logical /physical resource (DB,
programs, devices, humans etc) and/or some application logic exposed
to network”
-
Web Service Grids: An Evolutionary Approach (2004)
 Web Services
Simple mechanism for distributed computing
 Language independent, firewall friendly
 Grid Services
 Are essentially Web Services
 Transient – (can be created, destroyed, or die naturally)
 State – Maintained between calls to the Web Service

Scripting Languages

Benefits
 Enables Rapid prototyping (less code size and development time)
 Less effort to
 Perform complex tasks
 Interface with OS (hosting environment)
 Glue code to tie programs
 Usually portable
 Primarily for Plugging existing components together
However, some disadvantages too
 Weak typing
 Less structure, difficult to maintain
 Some examples
 Rhino – Java script for JAVA
 Perl, VBScript, (P/J)ython
 Scripting vs GUI builders
 GUI Builders – Ease of involvement of novice design engineer
 Scripting – Provides more flexibility thru direct access

Scripting Environments
Hosting Services
 OGSI:Lite & WSRF:Lite
 Based on Perl
 Rapidly deploy grid services
 Matlab / Jython from GEODISE
 GEODISE – Suite of CAD integrated with distributed
grid-enabled computing, data, analysis and knowledge
resources
 Uses Matlab to provide programatic access to GEODISE
functions along with an existing suite of Matlab tools
 Jython used to provide a hosting environment using
Java CoG kit.
Data flow in applications
 Real-time processing required.
 Typically data transfer involves temporary storing of
data. This data may be transferred using files (E.g.
Grid FTP).


Every component of the chain processes data from
input file, writes processed data to output file.
Time and Space critical in real-time applications hence
file-based transfer is undesirable for real-time
applications.
 Tools to automate data transfer and invoke
applications (E.g. Grid Ant, Karajan)
Workflow Architectures
 Triana – Graphical PSE to compose scientific
applications



Composed of one or more Triana engines.
Distributed version
Data transfer takes place using JXTA pipes.
 Taverna



Can interact with arbitrary services.
Plugins to mediate / operate the service in each case
Uses XScufl (derived from WSFL) workflow language.
 Kepler



Java packages for designing and execution.
Has a graphical interface for composing complex workflows
Can wrap existing code written in different languages. For e.g. Perl
script or Matlab script
Component Architectures
 XCAT @ IU-Extreme
Connects components (Provides and Uses ports)
 Jython based scripting to do application
management tasks (create application, set
properties, invoke application)
 Data transfer by GridFTP between components,
Globus Reliable File Transfer (fault tolerance).
 Many other systems
 Focus mainly on invocation of services as in a
Workflow

Messaging systems
 JXTA – P2P middleware, JMS for communication
 Pastry
Fault tolerant P2P middleware
 Based on Distributed Hash tables
 No real-time routing possible
 NaradaBrokering @ IU – http://www.naradabrokering.org
 Event- brokering system designed to run on a large network of cooperating brokers.
 Implements high-performance protocols (message transit time < 1
ms per broker)
 Order-preserving optimized message transport
 Interface with reliable storage for persistent events
 Fault tolerant data transport
 Support for different underlying transport implementations such as
TCP, UDP, Multicast, SSL, RTP

System Management


Increasing complexity of systems implies increasing
amount of metadata to be managed
Provide access to System and management of
System metadata
-


WS - Management
E.g. Performance metrics, logs, service metadata
Require ability to query system data and take
actions affecting the characteristics of the system.

For e.g. Perl provides hooks to query system data
Outline
 Motivation
 Literature Survey
 Research Issues
 HPSearch Architecture
 Contributions and Milestones
 Applications
 Summary
Research Issues
 Support for streaming data processing.


Data transfer and processing in real-time
Data transfer to be carried on between the end-points
(sender and recipient) without the flow engine mediating
- Grid Services Flow Language
 Design a run-time system that allows merging data sources,
data filtering and processing applications and visualization tools
in a service-oriented architecture
 Assume all components available as Web (Grid) services.
 Scalability an issue – Addition of data sources or processing
applications (Services) should not degrade the system
performance
 Fault-tolerance – Services and data sources may be lost.
Allow system to detect faults and discover and incorporate
new components.
Research Issues
 System Management Interface - Allow access to
system and manipulate the characteristics of system
by querying system metadata




Create Virtual topology for application deployment
Query performance metrics to design policies to
change routing substrate characteristics (E.g. Add new
brokers or links between existing brokers to aid
efficient routing)
Discover Services / brokers / topics of interest.
 To dynamically rewire components with data
streams.
Replay events
 Useful for achieving recovery after failure
Outline
 Motivation
 Literature Survey
 Research Issues
 HPSearch Architecture
 Contributions and Milestones
 Applications
 Summary
HPSearch

Binds URI to a scripting language


We use Mozilla Rhino (A Javascript implementation, Refer:
http://www.mozilla.org/rhino), but the principles may be applied to any other
scripting language
Every Resource may be identified by a URI and HPSearch allows us to manipulate the
resource using the URI.

For e.g. Read from a web address and write it to a local file
x = “http://trex.ucs.indiana.edu/data.txt”;
y = “file:///u/hgadgil/data.txt”;
Resource r = new Resource(“Copier”);
r.port[0].subscribeFrom(x);
/* read from */
r.port[0].publishTo(y);
/* write to */
f = new Flow();
f.addStartActivities(r);
f.start(“1”);

Adding support for WS-Addressing construct, under investigation
HPSearch (contd.)
 Currently provide bindings for the following





file://
socket://ip:port
http://, ftp://
topic://
jdbc:
 Host-objects to do specific tasks
– invoke web-services using SOAP
 PerfMetrics – Bind NaradaBrokering performance metrics.
Store published metrics and allow querying
 Resource – Every data source / filter / sink is a resource.
 Flow – To create a data flow between resources. Useful for
creating data flows
 For more information, visit

WSDL

http://www.hpsearch.org
Architecture
 Consists of

SHELL


TASK_SCHEDULER (FLOW_ENGINE)


Front end to scripting.
Distributes tasks among co-operating engines for load-balancing
purposes.
WSPROXY 
An AXIS web service wraps an actual service. The behavior of the
service can be controlled by making simple WS calls to this proxy.
 Can be controlled by any Workflow Engine
 WSProxy handles streaming data communication on behalf of the service.


Service only sees I/P and O/P streams. These could be files or a remote
data stream or even a file transferred via HTTP / FTP or results from a
database query
Can be deployed in standard Web Service containers (such as Tomcat)
Architecture
 WSProxy - Interfaces

Runnable




More control over execution (start, suspend, resume, stop…)
Basic idea (read block of data, process it, write it out)
Ideal for designing quick filtering applications that process data
in streams.
Wrapped



Wrap an existing service (Executables [*.exe], Matlab scripts,
shell / Perl scripts etc…)
Less control, can only start, stop
Ideal for wrapping existing programs / services to expose as a
pluggable component / web service
HPSearch
Architecture Overview
HPSearch Kernel
Files
Sockets
Topics
HPSearch Kernel
Request Handler
Request Handler
Java script Shell
DataBase
URIHandler
Task Scheduler
Flow Handler
Web
Service
DBHandler
Web Service EP
WSDLHandler
WSProxy
WSProxyHandler
Other Objects
Service
Broker Network
WSProxy
HPSearch
Kernel
Service
WSProxy
...
Service
So what is the overhead ?
Partial results as of now
 Taken on 1.6 GHz Pentium 4 machine w/ 256 MB RAM running
Java 1.4.1_02, NB version 0.98 rc2, Rhino 1.5R3
 Shell Init: 2085 mSec (average)
 Results from RDAHMM Script (26 lines, small script) takes about
15 mSec (average per line) to execute
 Task distribution (2 engine, 4 tasks) 3897.645 mSec
 WSProxy (Init – depends on number of streams to initialize) 700
– 2000 mSec (approximate value using System.currentTimeMillis).
Outline
 Motivation
 Literature Survey
 Research Issues
 HPSearch Architecture
 Contributions and Milestones
 Applications
 Summary
Contribution of this Thesis
 Stream and Service Management - Program data-flows



Incorporate static and dynamic data sources
WSProxy ensures that data flows directly between components
(Services) without the HPSearch engine mediating. Useful for
streaming large amounts of data without clouding the controller.
Scalable ?



We use NB as our messaging substrate which can handle large number
of clients
All components (data sources, data processing and visualization
applications) are clients. HPSearch manages streams and connects and
steers components.
Fault – tolerant ?


Data source, data filter (processing application) failure possible.
HPSearch can use the discovery service to invoke new services (in lieu
of failed services) and reconnect components via streams to continue
data flow
Contribution of this Thesis
(contd.)
 System Management - Scripting admin tasks
Creating network (virtual broker network) topology
 Querying Performance metrics
 Topic / Broker discovery
 Rapid deployment of applications
 Deploy Network topology
 Set Application properties
 Deploy Application
 In short:
 Provide alternative programmatic (scripting) access to
remote services / resources

Milestones
 Implement WS front-end to shell
 Remotely submit a script for execution, possibly through a portal
 WSProxy / Handler: Fault tolerance to handle situations when
The machine hosting the WSProxy dies
 The broker which is used by the proxy dies
 The HPSearch Engine dies
 Design Application Interface
 Allow users to create applications using this interface
 Set Application properties, Allow modification of application
properties at runtime using scripting
 NB Admin objects


NaradaBroker, PerfMetrics, NBDiscovery,
ReplayService
Milestones (contd.)
 Design stream negotiation module to allow
WSProxy to negotiate stream characteristics

Select best possible transport and other QoS
elements for data transfer between two
services (for a particular stream)
 Applications - To demonstrate the use


Audio / Video mixer application
Multiple data sources and data filtering
applications joined in a chain.
Outline
 Motivation
 Literature Survey
 Research Issues
 HPSearch Architecture
 Contributions and Milestones
 Applications
 Summary
Applications
Streaming Data Filtering
Sensor
Source
GPS Data
HPSearch
Kernel - TSE
Kernel - TSE
Data Filter
Filters the input data to get
only the estimate and error
values
Matlab Plotting
Script
Graph
Kernel - TSE
(Distributed)
Services
RDAHMM
Analyze the data
Applications
Creating Virtual Broker Network for deploying
applications
b = new NaradaBroker("school.cs.indiana.edu");
b.create(""); /* OR b.create("file:///u/hgadgil/alternateConfig.conf"); */
b.connectTo("156.56.104.170", "5045", "t", "");
b.requestNodeAddress("156-56-104-170.bl-dhcp.indiana.edu:5045", "0");
c = new NaradaBroker("trex.ucs.indiana.edu");
c.create("");
c.connectTo("156.56.104.170", "5045", "t", "");
c.requestNodeAddress("tcp://156-56-104-170.bl-dhcp.indiana.edu:5045", "0");
school.cs.indiana.edu
156.56.104.170
school.cs.indiana.edu
trex.ucs.indiana.edu
HPSearch
Shell
trex.cs.indiana.edu
Applications
Invoking Arbitrary Web Services
approved = false;
userID = "111-22-3333";
if(loanAmt < 10000)
approved = true;
else {
loanAmt < 10000
wsRA = new
WSDL("http://www.riskAssessor.com/services/RiskAssessor");
risk = wsRA.invoke("assessRisk", userID, loanAmt);
if(risk > 50)
approved = false;
else
risk = WS_riskAssessor(userID, loanAmt)
approved = true;
}
Print "Loan Approved: " + approved;
risk > 50
approved = false
approved = true
approved = true
Print result
Outline
 Motivation
 Literature Survey
 Research Issues
 HPSearch Architecture
 Contributions and Milestones
 Applications
 Summary
Summary
 This thesis addresses
 Managing data streams (Dynamic and static)
 Enabling connecting data sources and data processing
components (available as Web Services) for processing
data in real-time for critical infrastructure applications
 Develop a general purpose scripting architecture (like
Perl) for a multitude of tasks
 Goal is to create an architecture that is
 Pluggable / Extensible
 Manageable - Programmable
 Similar to the UNIX Pipe-Filter Architecture, but
implemented on a Distributed scale