ABSTRACT
Networked applications continuously move towards service-based and modular solutions. At the same time, web technologies, proven to be modular and distributed, are applied to these application areas. However, web technologies have to be adapted to the new characteristics of the involved systems -- no explicit client and server roles, use of heterogeneous devices, or high frequency and low latency data communication. To this end, we present an approach for describing distributed applications in terms of graphs of communicating nodes. In particular, we develop a formal model for capturing the communication between nodes, by including dynamic and static data producing devices, data consuming client applications, as well as devices that can serve as data produces and consumers at the same time. In our model, we characterise nodes by their frequencies of data exchange. We complement our model with a decision algorithm for determining the pull/push communication direction to optimise the amount of redundantly transferred data (i.e., data that is pushed but cannot be processed or data that is pulled but is not yet updated). The presented work lays the foundation for creating distributed applications which can automatically optimise data exchange.
- Z. Cheng, M. Perillo, and W. B. Heinzelman. General Network Lifetime and Cost Models for Evaluating Sensor Network Deployment Strategies. IEEE Transactions on Mobile Computing, 7(4):484--497, Apr. 2008. Google ScholarDigital Library
- R. Chinnici, J.-J. Moreau, A. Ryman, and S. Weerawarana. Web Services Description Language (WSDL) Version 2.0 Part 1: Core Language. Recommendation, W3C, June 2007. http://www.w3.org/TR/2007/REC-wsdl20-20070626. Latest version available at http://www.w3.org/TR/wsdl20.Google Scholar
- R. T. Fielding. Architectural Styles and the Design of Network-based Software Architectures. PhD thesis, University of California, Irvine, USA, 2000. Google ScholarDigital Library
- M. Gudgin, M. Hadley, N. Mendelsohn, J.-J. Moreau, H. F. Nielsen, A. Karmarkar, and Y. Lafon. SOAP Version 1.2 Part 1: Messaging Framework (Second Edition). Recommendation, W3C, Apr. 2007. http://www.w3.org/TR/2007/REC-soap12-part1-20070427/. Latest version available at http://www.w3.org/TR/soap12-part1/.Google Scholar
- D. Guinard, V. Trifa, F. Mattern, and E. Wilde. From the Internet of Things to the Web of Things: Resource-oriented Architecture and Best Practices. In Architecting the Internet of Things. Springer Berlin Heidelberg, 2011.Google ScholarCross Ref
- D. Guinard, V. Trifa, and E. Wilde. A Resource Oriented Architecture for the Web of Things. In Proceedings of the Internet of Things Conference, 2010.Google ScholarCross Ref
- X. Liu, Q. Huang, and Y. Zhang. Combs, Needles, Haystacks: Balancing Push and Pull for Discovery in Large-Scale Sensor Networks. In Proceedings of the Conference on Embedded Networked Sensor Systems, 2004. Google ScholarDigital Library
- V. P. Mhatre, C. Rosenberg, D. Kofman, R. Mazumdar, and N. Shro. A Minimum Cost Heterogeneous Sensor Network with a Lifetime Constraint. IEEE Transactions on Mobile Computing, 4(1):4{15, Jan. 2005. Google ScholarDigital Library
- A. Narayanan, C. Jennings, A. Bergkvist, and D. Burnett. WebRTC 1.0: Real-time Communication Between Browsers. Working draft, W3C, Sept. 2013. http://www.w3.org/TR/2013/WD-webrtc-20130910/. Latest version available at http://www.w3.org/TR/webrtc/.Google Scholar
Index Terms
- Towards Optimising the Data Flow in Distributed Applications
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