Abstract
Stream processing engines have appeared as the next generation of data processing systems, facing the needs for low-delay processing. While these systems have been widely studied recently, their ability to adapt their processing logics at run time upon the detection of some events calling for adaptation is still an open issue.
Chemistry-inspired models of computation have been shown to ease the specification of adaptive systems. In this paper, we argue that a higher-order chemical model can be used to specify such an adaptive SPE in a natural way. We also show how such programming abstractions can get enacted in a decentralised environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
We assume, that after some time, data is sent to the workflow, filling the in-tagged list in \(S_1\) triggering the workflow.
- 2.
- 3.
- 4.
Each SA is allowed to store only its own description.
References
Abadi, D.J., Ahmad, Y., Balazinska, M., Çetintemel, U., Cherniack, M., Hwang, J., Lindner, W., Maskey, A., Rasin, A., Ryvkina, E., Tatbul, N., Xing, Y., Zdonik, S.B.: The design of the Borealis stream processing engine. In: Second Biennial Conference on Innovative Data Systems Research, Asilomar, CA (2005)
Apache Software Foundation: Apache Storm. https://storm.apache.org/
Banâtre, J.P., Fradet, P., Radenac, Y.: Generalised multisets for chemical programming. Math. Struct. Comput. Sci. 16(4), 557–580 (2006)
Caeiro, M., Németh, Z., Priol, T.: A chemical model for dynamic workflow coordination. In: The 19th Euromicro International Conference on Parallel, Distributed and network-based Processing. Ayia Napa, Cyprus, February 2011
Di Napoli, C., Giordano, M., Németh, Z., Tonellotto, N.: Adaptive instantiation of service workflows using a chemical approach. In: The 16th International Euro-Par Conference on Parallel Processing, Ischia, Italy (2010)
Fernández, H., Priol, T., Tedeschi, C.: Decentralized approach for execution of composite web services using the chemical paradigm. In: The 8th IEEE International Conference on Web Services, Miami, FL, July 2010
Fernández, H., Tedeschi, C., Priol, T.: Rule-driven service coordination middleware for scientific applications. Future Gener. Comput. Syst. 35, 1–13 (2014)
Gedik, B., Andrade, H., Wu, K., Yu, P.S., Doo, M.: SPADE: the system S declarative stream processing engine. In: The ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, June 2008
Giavitto, J., Michel, O.: MGS: a rule-based programming language for complex objects and collections. Electronic Notes Theoretical Computer Science, 59(4), 286–304 (2001). http://dx.doi.org/10.1016/S1571-0661(04)00293-2
Gulisano, V., Jiménez-Peris, R., Patiño-Martínez, M., Soriente, C., Valduriez, P.: StreamCloud: an elastic and scalable data streaming system. IEEE Trans. Parallel Distrib. Syst. 23(12), 2351–2365 (2012)
Herath, C., Plale, B.: Streamflow programming model for data streaming in scientific workflows. In: The 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, Melbourne, Australia, May 2010
Németh, Z., Pérez, C., Priol, T.: Workflow enactment based on a chemical methaphor. In: The 3rd IEEE International Conference on Software Engineering and Formal Methods, Koblenz, Germany, September 2005
Neumeyer, L., Robbins, B., Nair, A., Kesari, A.: S4: distributed stream computing platform. In: The 13th IEEE International Conference on Data Mining Workshops, Sydney, Australia, December 2010
Paun, G., Rozenberg, G., Salomaa, A.: The Oxford Handbook of Membrane Computing. Oxford University Press Inc, New York, NY, USA (2010)
Paun, G.: Computing with membranes. J. Comput. Syst. Sci. 61(1), 108–143 (2000)
Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M. (eds.): Workflows for e-Science. Springer, London (2007)
Tolosana-Calasanz, R., Bañares, J.A., Rana, O.F., Álvarez, P., Ezpeleta, J., Hoheisel, A.: Adaptive exception handling for scientific workflows. Concurr. Comput. Pract. Experience 22(5), 617–642 (2010)
Verma, R., Ahmed, T., Srivastava, A.: Expressing workflow and workflow enactment using P systems. In: The 15th International Conference on Membrane Computing. Prague, Czech Republic, August 2014
Zinn, D., Hart, Q., McPhillips, T., Ludäscher, B., Simmhan, Y., Giakkoupis, M., Prasanna, V.K.: Towards reliable, performant workflows for streaming-applications on cloud platforms. In: The 11th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, Newport Beach, CA, May 2011
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Balderrama, J.R., Simonin, M., Tedeschi, C. (2015). Chemistry-Inspired Adaptive Stream Processing. In: Rozenberg, G., Salomaa, A., Sempere, J., Zandron, C. (eds) Membrane Computing. CMC 2015. Lecture Notes in Computer Science(), vol 9504. Springer, Cham. https://doi.org/10.1007/978-3-319-28475-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-28475-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28474-3
Online ISBN: 978-3-319-28475-0
eBook Packages: Computer ScienceComputer Science (R0)