Rapid Prototyping and Deployment of Distributed Web / Grid Services in a Service Oriented Architecture using Scripting

1. 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

2. Outline • Motivation • Literature Survey • Research Issues • HPSearch Architecture • Contributions and Milestones • Applications • Summary

3. 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

4. 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.

5. Critical Infrastructure systems (Scientific applications) Real-time streaming sources exist E.g. sensors, satellite stations OR Static data sources (databases containing previously warehoused observations) Data filtering / transformation essential in most cases for converting data to proper format for processing application Real-time processing required. Crucial for critical infrastructure applications Audio/video applications. Real-time sources E.g. Collaborative sessions OR Static data source (stored A/V files) Pre-processing required to modify A/V characteristic Format (encoding) / bit rate (quality) etc… Real-time processing crucial for collaborative environments MotivationStreaming data Processing

6. Outline • Motivation • Literature Survey • Research Issues • HPSearch Architecture • Contributions and Milestones • Applications • Summary

7. 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

8. 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

9. 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

10. Scripting EnvironmentsHosting 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.

11. 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)

12. 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

13. 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

14. 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 co-operating 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 differentunderlying transport implementations such as TCP, UDP, Multicast, SSL, RTP

15. 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

16. Outline • Motivation • Literature Survey • Research Issues • HPSearch Architecture • Contributions and Milestones • Applications • Summary

17. 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.

18. 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

19. Outline • Motivation • Literature Survey • Research Issues • HPSearch Architecture • Contributions and Milestones • Applications • Summary

20. 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

21. HPSearch (contd.) • Currently provide bindings for the following • file:// • socket://ip:port • http://, ftp:// • topic:// • jdbc: • Host-objects to do specific tasks • WSDL– 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 • http://www.hpsearch.org

22. Architecture • Consists of • SHELL • Front end to scripting. • TASK_SCHEDULER (FLOW_ENGINE) • 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)

23. 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

24. HPSearch Kernel HPSearch Kernel WSProxy WSProxy WSProxy Service Service Service Request Handler Request Handler Java script Shell URIHandler Task Scheduler Flow Handler Broker Network DBHandler Web Service EP WSDLHandler WSProxyHandler Other Objects HPSearchArchitecture Overview Files Sockets Topics DataBase Web Service . . . HPSearch Kernel

25. 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).

26. Outline • Motivation • Literature Survey • Research Issues • HPSearch Architecture • Contributions and Milestones • Applications • Summary

27. 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

28. Contribution of this Thesis(contd.) • System Management - Scripting admin tasks • Creating network (virtual broker network) topology • Querying Performance metrics • Topic / Broker discovery • Rapiddeployment of applications • Deploy Network topology • Set Application properties • Deploy Application • In short: • Provide alternative programmatic (scripting) access to remote services / resources

29. 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

30. 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.

31. Outline • Motivation • Literature Survey • Research Issues • HPSearch Architecture • Contributions and Milestones • Applications • Summary

32. 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 RDAHMM Analyze the data (Distributed) Services

33. school.cs.indiana.edu trex.ucs.indiana.edu 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"); 156.56.104.170 school.cs.indiana.edu trex.cs.indiana.edu HPSearch Shell

34. Applications Invoking Arbitrary Web Services approved = false; userID = "111-22-3333"; if(loanAmt < 10000) approved = true; else { wsRA = new WSDL("http://www.riskAssessor.com/services/RiskAssessor"); risk = wsRA.invoke("assessRisk", userID, loanAmt); if(risk > 50) approved = false; else approved = true; } Print "Loan Approved: " + approved; loanAmt < 10000 risk = WS_riskAssessor(userID, loanAmt) risk > 50 approved = true approved = false approved = true Print result

35. Outline • Motivation • Literature Survey • Research Issues • HPSearch Architecture • Contributions and Milestones • Applications • Summary

36. 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