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Modeling Resource-Aware Virtualized Applications for the Cloud in Real-Time ABS

  • Einar Broch Johnsen
  • Rudolf Schlatte
  • Silvia Lizeth Tapia Tarifa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7635)

Abstract

An application’s quality of service (QoS) depends on resource availability; e.g., response time is worse on a slow machine. On the cloud, a virtualized application leases resources which are made available on demand. When its work load increases, the application must decide whether to reduce QoS or increase cost. Virtualized applications need to manage their acquisition of resources. In this paper resource provisioning is integrated in high-level models of virtualized applications. We develop a Real-Time ABS model of a cloud provider which leases virtual machines to an application on demand. A case study of the Montage system then demonstrates how to use such a model to compare resource management strategies for virtualized software during software design. Real-Time ABS is a timed abstract behavioral specification language targeting distributed object-oriented systems, in which dynamic deployment scenarios can be expressed in executable models.

Keywords

Cloud Computing Virtual Machine Cloud Provider Client Application Resource Management Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Agha, G.A.: ACTORS: A Model of Concurrent Computations in Distributed Systems. The MIT Press, Cambridge (1986)Google Scholar
  2. 2.
    Albert, E., Arenas, P., Genaim, S., Gómez-Zamalloa, M., Puebla, G.: COSTABS: a cost and termination analyzer for ABS. In: Proc. Workshop on Partial Evaluation and Program Manipulation (PEPM 2012), pp. 151–154. ACM (2012)Google Scholar
  3. 3.
    Albert, E., Arenas, P., Genaim, S., Puebla, G., Zanardini, D.: Cost Analysis of Java Bytecode. In: De Nicola, R. (ed.) ESOP 2007. LNCS, vol. 4421, pp. 157–172. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Albert, E., Genaim, S., Gómez-Zamalloa, M., Johnsen, E.B., Schlatte, R., Tapia Tarifa, S.L.: Simulating Concurrent Behaviors with Worst-Case Cost Bounds. In: Butler, M., Schulte, W. (eds.) FM 2011. LNCS, vol. 6664, pp. 353–368. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Armstrong, J.: Programming Erlang: Software for a Concurrent World. Pragmatic Bookshelf (2007)Google Scholar
  6. 6.
    Bai, X., Li, M., Chen, B., Tsai, W.-T., Gao, J.: Cloud testing tools. In: Proc. 6th Symposium on Service Oriented System Engineering, pp. 1–12. IEEE (2011)Google Scholar
  7. 7.
    Balsamo, S., Marco, A.D., Inverardi, P., Simeoni, M.: Model-based performance prediction in software development: A survey. IEEE Transactions on Software Engineering 30(5), 295–310 (2004)CrossRefGoogle Scholar
  8. 8.
    Bjørk, J., de Boer, F.S., Johnsen, E.B., Schlatte, R., Tapia Tarifa, S.L.: User-defined schedulers for real-time concurrent objects. Innovations in Systems and Software Engineering (2012), http://dx.doi.org/10.1007/s11334-012-0184-5
  9. 9.
    Buyya, R., Murshed, M.: GridSim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurrency and Computation: Practice and Experience 14, 1175–1220 (2002)zbMATHCrossRefGoogle Scholar
  10. 10.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems 25(6), 599–616 (2009)CrossRefGoogle Scholar
  11. 11.
    Calheiros, R.N., Buyya, R., De Rose, C.A.F.: Building an automated and self-configurable emulation testbed for grid applications. Software: Practice and Experience 40(5), 405–429 (2010)Google Scholar
  12. 12.
    Calheiros, R.N., Netto, M.A., De Rose, C.A.F., Buyya, R.: EMUSIM: an integrated emulation and simulation environment for modeling, evaluation, and validation of performance of cloud computing applications. Software: Practice and Experience (2012)Google Scholar
  13. 13.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software, Practice and Experience 41(1), 23–50 (2011)CrossRefGoogle Scholar
  14. 14.
    Caromel, D., Henrio, L.: A Theory of Distributed Objects. Springer (2005)Google Scholar
  15. 15.
    Chakrabarti, A., de Alfaro, L., Henzinger, T.A., Stoelinga, M.: Resource Interfaces. In: Alur, R., Lee, I. (eds.) EMSOFT 2003. LNCS, vol. 2855, pp. 117–133. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  16. 16.
    Clavel, M., Durán, F., Eker, S., Lincoln, P., Martí-Oliet, N., Meseguer, J., Talcott, C. (eds.): All About Maude. LNCS, vol. 4350. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  17. 17.
    de Boer, F.S., Clarke, D., Johnsen, E.B.: A Complete Guide to the Future. In: De Nicola, R. (ed.) ESOP 2007. LNCS, vol. 4421, pp. 316–330. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    de Boer, F.S., Hähnle, R., Johnsen, E.B., Schlatte, R., Wong, P.Y.H.: Formal Modeling of Resource Management for Cloud Architectures: An Industrial Case Study. In: De Paoli, F., Pimentel, E., Zavattaro, G. (eds.) ESOCC 2012. LNCS, vol. 7592, pp. 91–106. Springer, Heidelberg (2012)Google Scholar
  19. 19.
    Deelman, E., Singh, G., Livny, M., Berriman, G.B., Good, J.: The cost of doing science on the cloud: The Montage example. In: Proc. High Performance Computing (SC 2008), pp. 1–12. IEEE/ACM (2008)Google Scholar
  20. 20.
    Epifani, I., Ghezzi, C., Mirandola, R., Tamburrelli, G.: Model evolution by run-time parameter adaptation. In: Proc. ICSE 2009, pp. 111–121. IEEE (2009)Google Scholar
  21. 21.
    Fersman, E., Krcál, P., Pettersson, P., Yi, W.: Task automata: Schedulability, decidability and undecidability. Information and Computation 205(8), 1149–1172 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Gulwani, S., Mehra, K.K., Chilimbi, T.M.: Speed: Precise and Efficient Static Estimation of Program Computational Complexity. In: Proc. POPL 2009, pp. 127–139. ACM (2009)Google Scholar
  23. 23.
    Haller, P., Odersky, M.: Scala actors: Unifying thread-based and event-based programming. Theoretical Computer Science 410(2-3), 202–220 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Jacob, J.C., Katz, D.S., Berriman, G.B., Good, J., Laity, A.C., Deelman, E., Kesselman, C., Singh, G., Su, M.-H., Prince, T.A., Williams, R.: Montage: a grid portal and software toolkit for science-grade astronomical image mosaicking. Intl. Journal of Computational Science and Engineering 4(2), 73–87 (2009)Google Scholar
  25. 25.
    Johnsen, E.B., Hähnle, R., Schäfer, J., Schlatte, R., Steffen, M.: ABS: A Core Language for Abstract Behavioral Specification. In: Aichernig, B.K., de Boer, F.S., Bonsangue, M.M. (eds.) FMCO 2010. LNCS, vol. 6957, pp. 142–164. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  26. 26.
    Johnsen, E.B., Owe, O.: An asynchronous communication model for distributed concurrent objects. Software and Systems Modeling 6(1), 35–58 (2007)Google Scholar
  27. 27.
    Johnsen, E.B., Owe, O., Schlatte, R., Tapia Tarifa, S.L.: Dynamic Resource Reallocation between Deployment Components. In: Dong, J.S., Zhu, H. (eds.) ICFEM 2010. LNCS, vol. 6447, pp. 646–661. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  28. 28.
    Johnsen, E. B., Owe, O., Schlatte, R., Tapia Tarifa, S.L.: Validating Timed Models of Deployment Components with Parametric Concurrency. In: Beckert, B., Marché, C. (eds.) FoVeOOS 2010. LNCS, vol. 6528, pp. 46–60. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  29. 29.
    Larsen, K.G., Pettersson, P., Yi, W.: UPPAAL in a nutshell. Intl. Journal on Software Tools for Technology Transfer 1(1-2), 134–152 (1997)zbMATHCrossRefGoogle Scholar
  30. 30.
    Meseguer, J.: Conditional rewriting logic as a unified model of concurrency. Theoretical Computer Science 96, 73–155 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Nuñez, A., Vázquez-Poletti, J., Caminero, A., Castañé, G., Carretero, J., Llorente, I.: iCanCloud: A flexible and scalable cloud infrastructure simulator. Journal of Grid Computing 10, 185–209 (2012)CrossRefGoogle Scholar
  32. 32.
    Petriu, D.B., Woodside, C.M.: An intermediate metamodel with scenarios and resources for generating performance models from UML designs. Software and System Modeling 6(2), 163–184 (2007)CrossRefGoogle Scholar
  33. 33.
    Verhoef, M., Larsen, P.G., Hooman, J.: Modeling and Validating Distributed Embedded Real-Time Systems with VDM++. In: Misra, J., Nipkow, T., Sekerinski, E. (eds.) FM 2006. LNCS, vol. 4085, pp. 147–162. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Einar Broch Johnsen
    • 1
  • Rudolf Schlatte
    • 1
  • Silvia Lizeth Tapia Tarifa
    • 1
  1. 1.Department of InformaticsUniversity of OsloNorway

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