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Web services and mobile architecture




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  • It is always good to have a small message. But in mobile computing, it is absolutely required because of the narrow bandwidth connection. * Wireless Connection vs. Wired Connection (Bandwidth) * Problems created by encoding/decoding (Increased message size and A text conversion) * HTTP: the most popular transport protocol in mobile Web Service
  • X.694, a specification that defines a mapping from XSD to ASN.1, is an important building block for Fast because XSD is used in WSDL to define the structure of messages. Consequently, the XML schema referenced in a WSDL document can be considered an abstract schema, with an equivalent ASN.1 description, whose instances can be encoded using XML or an ASN.1 encoding. In the latter case it is possible to use an efficient binary encoding such as Packed Encoding Rules (PER), also known as X.691 [ 15 ]. Figure 2 depicts this process. Stub and Tie is for WSDL DEFINE XML INFOSET WHAT DOES (PER) – Octet mean
  • Significance of the research: Investigating what are the obstacles of mobile Web Service / Grid Computing Investigating those obstacles in detail to find resolutions or bypasses
  • This is a Practical problem practical problem: problem you experience or you observe in the "reality" and which manifests itself as a cost in time, satisfaction, money, etc... It is important to motivate why the problem is important enough to be worth the research. The practical problem is associated with a topic, which is the area in which the research will be done. state of the art analysis: once a practical problem is identified, a state of the art analysis is done to identify and evaluate all the existing solutions to the practical problem. The state of the art analysis includes a literature analysis (i.e. review of the published research results) and a best practice analysis (i.e. review of the current industrial practice). At this point, either the practical problem is solved (still be worth to write a technical report on the results) or none of the existing solutions are satisfactory and you can carry the research. research problem: practical problem reformulated by the researcher in a way which states how the current state of the art presents an incomplete or flawed understanding. research solution: solution to the research problem, which could be applied to solve the practical problem. Usually, an hypothesis is stated and is then validated by using some methods. It is important to show how the research solution contributes to solve the practical problem. Note that hypothesis which have been proven invalid might also be published.
  • Streams help performance using WS-Context saving of replicated data And by amortizing negotiation Streams help HTTP
  • If you have a single message that has different structure and type, it should be exchanged in another stream. Phrase two WS nodes exchange a stream of messages Is FUNNY as always true – nothing to do with application domain In Sensor Grid example, no “mobile clients” Add a mobile example such as PDA web access to Grid job
  • DOM (Document Object Model) SAX (Simple API for XML) “ Use a data description file as a sample instance of messages in the stream” is an assumption we made and it could be the limitation of the current implementation. Maybe should be earlier as you use Infoset without definition earlier
  • Using the description language file  dynamically generated filter which converts representations. Note that current version of the HHFR prototype has only a binary format filter Current implementation handles header  hasn’t implemented A picture of filters and handlers and body processor showing different possible orders Could be good
  • Like the video application case, there are many undocumented specifications. Customized choice of transport Axis2 Axiom data model is SOAP Infoset compatible. What is 2 nd channel? Isn’t first bullet Transport and second bullet message representation? If so clearly label two issues
  • Not only save redundant/unchanging art, but also save negotiation information. General goal is to reduce the size of message. In mobile environment, the message size is tend to be small and latency is high. Stress that any WS enabled database could be used and in fact our WS-Context Built on Javaspaces which is a natural model with a SQL database “just” to store OGSA-DAI would also be possible
  • Could mention WS-Policy here specifying default strategy
  • Context size is 847bytes and the entire SOAP message size is 1.58KB
  • HTTP is not a mandatory transport protocol, though it is the most popular protocol in mobile computing.
  • Don’t understand Measured through HHFR to show bandwidth gain from using a Context-store
  • In the result of the scalability test, it should be stated that the time for processing result is not optimized and there is lots of possible improvement room in Axis. Axis2 should perform better. You should separate twsctx taxis ttrans measurements from discussion of N You haven’t even explained that N maximum supported by one server So measure fully a server Then pose question as to allowed N
  • In one second, there is N/T stream starts N/Tstream ends. Thus 2N/Tstream + N/Tstream access per second.
  • Results and claim Design and implement HHFR architecture which overcomes/bypasses obstacles.


  • 1. Web Service Architecture for Mobile Computing Sangyoon Oh Department of Computer Science Indiana University Sangyoon Oh
  • 2. Outline
    • Motivation
    • Research Issues
    • Our approach:
    • HandHeld Flexible Representation Architecture
    • Performance Evaluation
    • Conclusion
    • Future work
    Sangyoon Oh
  • 3. Motivation and Research Problem
  • 4. Web Service and Mobile Computing
    • Web Service inter-relates distributed functionalities (i.e. services) in an elegant and technology-neutral manner.
    • Mobile devices with wireless connections have become a vital part of people’s everyday life.
      • Play audio/video, Access Web, Multiplayer gaming through wireless connection, participate in collaboration session.
      • 3G cellular network ( downloading up to 500kbps ) , 802.11b/g (54Mbps), or WiBro / WiMAX (practical bandwidth up to 2Mbps)
    Sangyoon Oh
  • 5. Important Obstacles in Integrating W eb Services and Mobile Computing
  • 6. Some Current Approaches
    • Compressing XML Document
      • gzip, XMill
      • Able to reduce a document size
      • However, the additional layer required to compress and to decompress add a significant overheads
    • Attaching binary data to SOAP message
      • MTOM/XOP : MIME attachment
        • JPG, MP3: standardized format
      • DIME : Wrapping binary data
    Sangyoon Oh
  • 7. Fast Infoset
    • S pecifies a representation of an instance of the XML Infoset using binary encoding.
      • XML Infoset Specification is used to refer information in well formed XML.
      • Doesn’t tied up with XML API (e.g. DOM, SAX)
    • Use ASN. 1. for binary encoding
    Sangyoon Oh
  • 8. Fast Infoset: Example
    • <root> <tag> one </tag> <tag> two </tag> <anotherTag> one </anotherTag>
    • </root>
    • {0} <root> {1} <tag> {0} one [1] <> {1} two {2} <anotherTag> [0]
    Sangyoon Oh
    • No end tags
    • Indexing repeated string
    • Indexing qualified names
    Local Name Content 0 root 0 one 1 tag 1 two 2 anotherTag 2
  • 9. Motivation
    • Performance has many aspects
      • XML parsing and transmission overhead often can not be afforded
    • A lot of research on message representation ( e.g. binary XML) but not on the overall system framework
      • overcome or bypass possible performance overheads required to support optimizing messages
    • Security can be important and impact performance
    Sangyoon Oh
  • 10. Research Issues
    • Architecture of Interaction of mobile client and Web Service.
    • A negotiation architecture that allows protocol independent solutions
    • A data description language that allows conversion between multiple representations
    • Adopt database semantics to reduce message size and store negotiated characteristics
    Sangyoon Oh
  • 11. Our approach: HandHeld Flexible Representation Architecture
  • 12. Conventional Web Service Communication Model in Mobile Computing Sangyoon Oh
  • 13. Our Approach: HHFR
  • 14. Three Key Design Features
    • Distinguishes between message semantics and syntax
      • Using data description language e.g. Data Format Description Language (DFDL) style Simple_DFDL
    • Exchange messages in a streaming style
      • Use streaming at protocol and semantic level
    • Using Context-store to hold static data
      • Unchanging/redundant SOAP message parts
      • Simple_DFDL as a data representation
      • Negotiated stream characters
    Sangyoon Oh
  • 15. Messaging Style
    • Producer and Consumer of data have access to its Schema (Static data binding)
    • Stream -- set of related messages
      • Messages in the stream  the same structure and same data type
      • Mobile clients (e.g. PDAs or smart phones) access to Grid job
    • Message size is tend to be small (e.g. in mobile computing)
    Sangyoon Oh
  • 16. Distinguishing XML Syntax and Semantics
    • Our XML data model is defined by XML Infoset specification.
    • Our approach
      • Distinguish semantic (message content) and syntax
        • e.g. <year>2006</year>
        • Its syntax and value, 2006
      • To define the XML syntax
        • Use a data description language (Simple_DFDL )
        • Use a data description file as a sample instance of messages in the stream
    Sangyoon Oh
  • 17. Simple_DFDL and Processing Module
    • Mapping data between representations
    • Processing architecture
      • Simple_DFDL describes data format
      • Processor (DSParser) builds the HHFR Data model
      • Filter converts data from and to the preferred representation format
    • A follow-on project is to integrate HHFR with fully developed DFDL
      • HHFR starts with Simple_DFDL and will move to DFDL.
    Sangyoon Oh
  • 18. Example: Simple_DFDL document <xs:element name=&quot;HHFR&quot;> <xs:complexType> <xs:element name=“Float1&quot; type=“float&quot;/> <xs:element name=“Float2&quot; type=“float&quot;/> </xs:complexType> </xs:element>
  • 19. Message Handling
    • Filters
      • Convert representations
        • XML-to-binary, binary-to-language specific data model
    • Handler for Headers
      • Conventional Handler Approach: Convert back to SOAP
      • M ake a handler understand alternative representation
        • e.g. WS-RM handler could be taught alternative representation
  • 20. Negotiation Process
    • Use conventional SOAP message
    • Negotiate
      • HHFR-Capability
      • A preferred representation
      • Characteristics of Stream
      • QoS issues (e.g. reliability, security)
  • 21. Streaming Related Issues
    • Transport:
      • HTTP transport could be a performance bottleneck  well known fact
        • TCP/IP connection setup overhead , Request/Response.
        • Persistent Connection may be not guaranteed in Cellular environment
    • Representation:
      • Using Context-store saving of redundant / unchanging data
    Sangyoon Oh
  • 22. Context-store
    • Strategy: a rchiving s tatic m eta-data and negotiated information
      • Any WS enabled Database could be used
    • Guarantees semantically persistent recovery
    • WS-Context specification
      • Use URI to store and retrieve
      • Fault Tolerant High Performance Information Service (FTHPIS) of CGL
    Sangyoon Oh
  • 23. Normal Runtime Scenario
    • A HHFR-capable endpoint sends a negotiation request to intended service endpoint over SOAP.
      • Send an input data description
      • Service endpoint sends an output data description
    • Two endpoint s exchange message in stream fashion
      • Messages in the stream are in the form of negotiated representations
    • The redundant / unchanging static metadata and negotiation details are stored in Context-store
    Sangyoon Oh
  • 24. Summary
    • Bandwidth problems in limited wireless connection
      • Optimized message representation
      • Reducing message size using Context-store
    • Parsing & Serializing overhead to less powered processor in mobile device
      • Avoiding conventional SOAP processing
      • Simple_DFDL & Filters process message in efficient way.
    • HTTP request/response in high latency wireless connection
      • Transport level message streaming
    • Intermittent Wireless Connection
      • Context-store automates semantically persistent recovery
    Sangyoon Oh
  • 25. Performance and Analysis
  • 26. Performance Evaluation
    • Experiments are intended to show
      • Performance comparisons between a conventional SOAP based client and a HHFR based client
      • Savings and gains from Context-store
      • Analyzed optimal scalability using Context-store
    • Service clients are running on Treo600
    • Experiments run through actual 2G cellular connections
    Sangyoon Oh
  • 27. Connection Setup Sangyoon Oh
  • 28. Machine Configuration Service Provider: Grid Farm 8 Processor Intel® Xeon™ CPU (2.40GHz) RAM 2GB total Network Bandwidth 100Mbps OS GNU/Linux (kernel release 2.4.22) Java Version Java 2 platform, Standard Edition (1.5.0-06) SOAP Engine Axis 1.2 (in Tomcat 5.5.8) Service Client: Treo 600 Processor ARM (144MHz) RAM 32MB total, 24MB user available Network Bandwidth 14.4Kbps (Sprint PCS Vision) OS Palm 5.2.1.H Java Version Java 2 platform, Micro Edition CLDC 1.1 and MIDP 2.0
  • 29. System Parameters
    • t hhfr : time per message in a HHFR performance model
    • t soap : time per message in a conventional SOAP performance model
    • O a : overhead for accessing the Context-store Service
    • O b : overhead for negotiation
    • C hhfr : total time for finishing stream of the HHFR
    • C soap : total time for finishing stream of the conventional SOAP framework
  • 30.
    • C hhfr = nt hhfr + O a + O b
    • C soap = nt soap
    • Breakeven point:
    • n be t hhfr + O a + O b = n be t soap
    • O a (WS) is roughly 20 milliseconds
    Performance Model and Measurements O a : overhead for accessing the Context-store Service O b : overhead for negotiation Average ±error (sec) Stddev (sec) Context-store Access ( O a ) 4.127 ±0.042 0.516 Negotiation ( O b ) 5.133 ±0.036 0.825
  • 31. String Concatenation
    • Measure the total stream time i.e. summation of RTT
    • Independent variables
      • Number of messages per stream
      • Size of the message
    Sangyoon Oh
  • 32. Floating Point Number Addition
    • Large Slope of SOAP
      • high latency of the HTTP based communication
      • SOAP parsing/ serialization overhead
    • There exist in non-zero locations breakeven point
    Sangyoon Oh
  • 33. Performance saving by using Context-store
    • Experiments ran over HHFR
      • Optimized message exchanged over HHFR after saving redundant/unchanging parts to the Context-store
      • We use WS-Addressing message for the experiment.
      • Save on average 83% of message size, 41% of transit time
    Sangyoon Oh Summary of the Round Trip Time ( T RTT ) Message Size Full SOAP Message Optimized Message Ave.±error Stddev Ave.±error Stddev Medium: 513byte (sec) 2.76±0.034 0.187 1.75±0.040 0.217 Large: 2.61KB (sec) 5.20±0.158 0.867 2.81±0.098 0.538
  • 34. System Parameters
    • N: the maximum number of stream supported by one server
    • T wsctx : time consumed to process (setContext) an operation
    • T time-in-server : time consumed in Axis server
    • T axis-overhead : time consumed to process Axis data-binding and HTTP request/response
    • T stream : length of stream in seconds
    Sangyoon Oh
  • 35. Summary of T time-in-server measurements
    • T time-in-server = T wsctx + T axis-overhead
    • T wsctx =< 1 milliseconds
    • Axis 1.2 Beta3 is used
    • Data binding overhead
    • at Web Service Container
    • is the dominant factor to
    • message processing
    Sangyoon Oh
  • 36. Sangyoon Oh
  • 37. Allowed Maximum Number of Stream by Server
    • N: the maximum number of stream supported by one server
    • 3N/T stream ≈ 1 / T time-in-server (N/T stream starts and N/T stream ends)
    • N ≈ T stream / (3 * T time-in-server )
    • e.g. T stream = 600 (sec),
    • T time-in-server = 0.035 (when the context-size is 1.2 Kbyte)
    • N ≈ 600 / {3 * 0.035}
    • N ≈ 5700
    Sangyoon Oh
  • 38. Conclusions and Future Work
  • 39. Summary of Contributions
    • Design and implement an overall system framework architecture: The HHFR Architecture provides
      • A mechanism to negotiate the characteristics of a stream
      • A streaming communication channel
      • Simple_DFDL which distinguishes the semantics from the representation of message content
      • An interface to Information service (Context-store)
      • A semantically persistent recovery framework
    • Detailed performance evaluation
      • Benchmark applications, approach to use a Context-store
    Sangyoon Oh
  • 40. Future work
    • Streaming channel integrated with a Web Service Container.
    • Provide a plug-in API for filter implementation
    • Integration with fully developed DFDL
      • Support more message type
    • Secure Message stream using negotiation process
      • Bouncy Castle lightweight cryptography package
    • WS-Policy specifying the default strategy
      • Read from Context-store or negotiation message
    • Relevance to non mobile (conventional ) case
    Sangyoon Oh
  • 41. Related publications
    • Sangyoon Oh and Geoffrey Fox, “ Optimizing Web Service Messaging Performance in Mobile Computing ,” Future Generation Computer Systems Journal , Revision being processed.
    • M. Aktas, S. Oh , G. Fox, and M. Pierce, “XML Metadata Service” Proc. of the IEEE 2 nd International Conference on Semantics, Knowledge and Grid (SKG2006) , Nov. 2006
    • Sangyoon Oh , Mehmet Aktas, Marlon Pierce, and Geoffrey Fox, “Architecture for High-Performance Web Service Communications using an Information Service,” World Scientific and Engineering Academy and Society Transactions on Information Science and Applications , May 2006
    • Sangyoon Oh , Hasan Bulut, Ahmet Uyar, Wenjun Wu, and Geoffrey Fox, “Optimized Communication using the SOAP infoset For Mobile Multimedia Collaboration Applications,” Proc. Of the IEEE 2005 International Symposium on Collaborative Technologies and Systems (CTS 2005) , May 2005.
    Sangyoon Oh
  • 42. Full list of publications (I)
    • Wonil Kim, Sangyoon Oh , Sanggil Kang, Kyungro Yoon, A Novel Approach in Sports Image Classification , Lecture Notes in Computer Science (Proc. of the International Conference on Intelligent Computing ICIC 2006) , August 2006.
    • Wonil Kim, Sangyoon Oh , Sanggil Kang, Dongkyun Kim, Multi-module Image Classification System , Lecture Notes in Artificial Intelligence (Proc. of the 7th International Conference on Flexible Query Answering Systems FQAS 2006) , June 2006.
    • Sangyoon Oh , Mehmet S. Aktas, Geoffrey C. Fox, Marlon Pierce, Architecture for High-Performance Web Service Communications Using an Information Service , World Scientific and Engineering Academy and Society Transactions on Information Science and Applications , May 2006.
    • Geoffrey C. Fox, Mehmet S. Aktas, Galip Aydin, Hasan Bulut, Harshawardhan Gadgil, Sangyoon Oh , Shrideep Pallickara, Marlon E. Pierce, Ahmet Sayar, and Gang Zhai, Grids for Real Time Data Applications , Lecture Notes in Computer Science (Proc. of the 6th International Conference on Parallel Processing and Applied Mathematics PPAM 2005) , Poznan Poland, September 11-14 2005.
    • Sangyoon Oh , Sangmi Lee Pallickara, Sunghoon Ko, Jai-Hoon Kim, Geoffrey Fox, Cost Model and Adaptive Scheme for Publish/Subscribe Systems on Mobile Environments , Lecture Notes in Computer Science (Proc. of the 2nd International Workshop on Active and Programmable Grids Architectures and Components APGAC05) , May 2005.
    • Sangyoon Oh , Sangmi Lee Pallickara, Sunghoon Ko, Jai-Hoon Kim, Geoffrey Fox, Publish/Subscribe Systems on Node and Link Error Prone Mobile Environments , Lecture Notes in Computer Science (Proc. of Wireless and Mobile Systems Workshop in ICCS 2005) , May 2005.
    • G. Fox, S Ko, M Pierce, O Balsoy, J Kim, S Lee, K Kim, S Oh , X Rao, M Varank, H Bulut, G Gunduz, X Qui, S Pallickara, A Uyar, Grid Service for Earthquake Science , Concurrency and Computation: Practice and Experience in ACES Special Issue , 14, 371-393, October 2002.
    Sangyoon Oh
  • 43. Full list of publications (II)
    • Wenjun Wu, Ahmet Uyar, Hasan Bulut, Sangyoon Oh , Geoffrey Fox, Grid Service Architecture for Videoconferencing , to appear as chapter in book &quot; Grid Computational Methods &quot; Edited by M.P. Bekakos, G.A. Gravvanis and H.R. Arabnia.
    • M. Aktas, G. Aydin, H. Bulut, H. Gagdil, G. Fox, M. Nacar, M. Pierce, A. Sayar and S . Oh , XML Metadata Services and Application Usage Scenarios , Proc. of The IEEE 2nd International Conference on Semantics, Knowledge and Grid (SKG2006) , Guilin China, Oct. 31 – Nov. 3, 2006
    • Sangyoon Oh , Mehmet S. Aktas, Marlon Pierce, Geoffrey C. Fox, Optimizing Web Service Messaging Performance Using a Context Store for Static Data , Invited paper for 5th WSEAS International Conference on TELECOMMUNICATIONS and INFORMATICS (TELE-INFO '06) , Istanbul, Turkey, May 27-29, 2006.
    • Geoffrey C. Fox, Mehmet S. Aktas, Galip Aydin, Andrea Donnellan, Harshawardhan Gadgil, Robert Granat, Shrideep Pallickara, Jay Parker, Marlon E. Pierce, Sangyoon Oh , John Rundle, Ahmet Sayar, and Michael Scharber, Building Sensor Filter Grids: Information Architecture for the Data Deluge, Proc. of The IEEE International Conference on Semantics, Knowledge and Grid (SKG2005) , Beijing China November 27-29 2005.
    • Sangyoon Oh , Hasan Bulut, Ahmet Uyar, Wenjun Wu, Geoffrey C. Fox, Optimized Communication using the SOAP Infoset For Mobile Multimedia Collaboration Applications , Proc. of the IEEE 2005 International Symposium on Collaborative Technologies and Systems (CTS 2005), St. Louis, Missouri, USA, May. 2005.
    • Sangyoon Oh , Geoffrey C. Fox , Sunghoon Ko, GMSME: An Architecture for Heterogeneous Collaboration with Mobile Devices , Proc. of the Fifth IEEE/IFIP International Conference on Mobile and Wireless Communications Networks (MWCN 2003), Singapore, October, 2003
    Sangyoon Oh
  • 44. Full list of publications (III)
    • Geoffrey Fox, Sunghoon Ko, Kangseok Kim, Sangmi Lee, and Sangyoon Oh , Universal Accessible Collaboration Frameworks for Ubiquitous Computing Environments , Proc. of International Conference in Ubiquitous Computing (ICUC 2003) in Seoul, Korea , October 2003
    • Sangmi Lee, Sunghoon Ko, Geoffrey Fox, Kangseok Kim, and Sangyoon Oh , A Web Service Approach to Universal Accessibility in Collaboration Services , Proc. of the 1st International Conference on Web Services (ICWS ’03), Las Vegas, USA, June 2003.
    • Geoffrey Fox, Hasan Bulut, Kangseok Kim, Sung-Hoon Ko, Sangmi Lee, Sangyoon Oh , Shrideep Pallickara, Xiaohong Qiu, Ahmet Uyar, Minjun Wang, Wenjun Wu, Collaborative Web Services and Peer-to-Peer Grids , Proc. of the 2003 International Symposium on Collaborative Technologies and Systems (CTS 2003) , Orlando, Florida, USA, Jan. 2003.
    • Hasan Bulut, Geoffrey Fox, Dennis Gannon, Kangseok Kim, Sung-Hoon Ko, Sangmi Lee, Sangyoon Oh , Xi Rao, Shrideep Pallickara, Quinlin Pei, Marlon Pierce, Aleksander Slominski, Ahmet Uyar, Wenjun Wu, Choonhan Youn, An Architecture for e-Science and its Implications , Proc. of the 2002 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2002) , San Diego, CA, USA, July 2002.
    • Geoffrey C. Fox, Sunghoon Ko, Kangseok Kim, Sangyoon Oh and Sangmi Lee, Integration of Hand-Held Devices into Collaborative Environments , Proc. the 1st International Workshop on Wired/Wireless Internet Communications (WWIC 2002), Las Vegas, NV, USA, April 2002.
    Sangyoon Oh