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1 Web 2.0 for e-Science Environments SKG2007 Xian Hotel, Xian China October 29 2007 Geoffrey Fox and Marlon Pierce Computer Science, Informatics, Physics.

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Presentation on theme: "1 Web 2.0 for e-Science Environments SKG2007 Xian Hotel, Xian China October 29 2007 Geoffrey Fox and Marlon Pierce Computer Science, Informatics, Physics."— Presentation transcript:

1 1 Web 2.0 for e-Science Environments SKG2007 Xian Hotel, Xian China October 29 2007 Geoffrey Fox and Marlon Pierce Computer Science, Informatics, Physics Community Grids Laboratory Indiana University Bloomington IN 47401

2 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

3 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 4 Best Web 2.0 Sites -- 2006 Extracted from All important capabilities for e-Science Social Networking Start Pages Social Bookmarking Peer Production News Social Media Sharing Online Storage (Computing)

5 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 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

6 Web 2.0 Systems like Grids have Portals, Services, Resources Captures the incredible development of interactive Web sites enabling people to create and collaborate

7 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 doesnt 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

8 Distribution of APIs and Mashups per Protocol RESTSOAPXML-RPCREST, XML-RPC REST, XML-RPC, SOAP REST, SOAP JSOther google maps netvibes virtual earth google search amazon S3 amazon ECS flickr ebay youtube 411sync yahoo! search yahoo! geocoding technorati yahoo! images trynt yahoo! local Number of Mashups Number of APIs SOAP is quite a small fraction

9 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 CPUs 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

10 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 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 URIs e.g. NSF TeraGrid use, PIs and Proposals Hopes to support collaboration (for Minority Serving Institution faculty) Multicore SALSA project using for Parallel Programming 2.0

11 Use blog to create posts. Display blog RSS feed in MediaWiki.

12 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, 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 2/6/2014 12

13 MSI-CIEC Portal MSI-CIEC Minority Serving Institution CyberInfrastructure Empowerment Coalition

14 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

15 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 16 Mashups v Workflow? Mashup Tools are reviewed at Workflow Tools are reviewed by Gannon and Fox 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 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

18 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

19 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

20 Web 2.0 Mashups and APIs http://www.programmable has (Sept 12 2007) 2312 Mashups and 511 Web 2.0 APIs and with GoogleMaps the most often used in Mashups http://www.programmable This is the Web 2.0 UDDI (service registry)

21 The List of Web 2.0 APIs Each site has API and its features Divided into broad categories Only a few used a lot (49 APIs used in 10 or more mashups) RSS feed of new APIs Google maps dominates but Amazon S3 growing in popularity

22 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 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

24 Typical Google Gadget Structure … Lots of HTML and JavaScript 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

25 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

26 Multicore SALSA at CGL Service Aggregated Linked Sequential Activities 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 10-100s µs Developing set of services (library) of multicore parallel data mining algorithms

27 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 todays 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.

28 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 as it supports both MPI and dynamic threading style of parallelism It already supports a Web 2.0 compatible service model DSS

29 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 doesnt seem elegant The new languages from Darpas HPCS program support task parallelism (composition of parallel components) decoupling composition and scalable parallelism will remain popular and must be supported.

30 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.

31 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 Linked Sequential Activities 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

32 MPI Exchange Latency in µs (20-30 µs computation between messaging) MachineOSRuntimeGrainsParallelismMPI Exchange Latency Intel8c:gf12 (8 core 2.33 Ghz) (in 2 chips) RedhatMPJE (Java)Process8181 MPICH2 (C)Process840.0 MPICH2: FastProcess839.3 NemesisProcess84.21 Intel8c:gf20 (8 core 2.33 Ghz) FedoraMPJEProcess8157 mpiJavaProcess8111 MPICH2Process864.2 Intel8b (8 core 2.66 Ghz) VistaMPJEProcess8170 FedoraMPJEProcess8142 FedorampiJavaProcess8100 VistaCCR (C#)Thread820.2 AMD4 (4 core 2.19 Ghz) XPMPJEProcess4185 RedhatMPJEProcess4152 mpiJavaProcess499.4 MPICH2Process439.3 XPCCRThread416.3 Intel4 (4 core 2.8 Ghz) XPCCRThread425.8 SALSA Performance The macroscopic inter-service DSS Overhead is about 35µs DSS is composed from CCR threads that have 4µs overhead for spawning threads in dynamic search applications 20µs overhead for MPI Exchange

33 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 tomorrows 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

34 Parallel Multicore GIS Deterministic Annealing Clustering 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

35 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

36 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 wont eventually dominate other application areas and with Enterprise 2.0 its invading Grids The world does itself in large numbers!

37 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 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

38 The Ten areas covered by the 60 core WS-* Specifications WS-* Specification AreaTypical Grid/Web Service Examples 1: Core Service ModelXML, WSDL, SOAP 2: Service InternetWS-Addressing, WS-MessageDelivery; Reliable Messaging WSRM; Efficient Messaging MOTM 3: NotificationWS-Notification, WS-Eventing (Publish- Subscribe) 4: Workflow and TransactionsBPEL, WS-Choreography, WS-Coordination 5: SecurityWS-Security, WS-Trust, WS-Federation, SAML, WS-SecureConversation 6: Service DiscoveryUDDI, WS-Discovery 7: System Metadata and StateWSRF, WS-MetadataExchange, WS-Context 8: ManagementWSDM, WS-Management, WS-Transfer 9: Policy and AgreementsWS-Policy, WS-Agreement 10: Portals and User InterfacesWSRP (Remote Portlets)

39 WS-* Areas and Web 2.0 WS-* Specification AreaWeb 2.0 Approach 1: Core Service ModelXML becomes optional but still useful SOAP becomes JSON RSS ATOM WSDL becomes REST with API as GET PUT etc. Axis becomes XmlHttpRequest 2: Service InternetNo special QoS. Use JMS or equivalent? 3: NotificationHard with HTTP without polling– JMS perhaps? 4: Workflow and Transactions (no Transactions in Web 2.0) Mashups, Google MapReduce Scripting with PHP JavaScript …. 5: SecuritySSL, HTTP Authentication/Authorization, OpenID is Web 2.0 Single Sign on 6: Service Discovery 7: System Metadata and StateProcessed by application – no system state – Microformats are a universal metadata approach 8: Management==InteractionWS-Transfer style Protocols GET PUT etc. 9: Policy and AgreementsService dependent. Processed by application 10: Portals and User InterfacesStart Pages, AJAX and Widgets(Netvibes) Gadgets

40 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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