Context-Aware Programming for Hybrid and Diversity-Aware Collective Adaptive Systems

  • Hong-Linh TruongEmail author
  • Schahram Dustdar
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 202)


Collective adaptive systems (CASs) have been researched intensively since many years. However, the recent emerging developments and advanced models in service-oriented computing, cloud computing and human computation have fostered several new forms of CASs. Among them, Hybrid and Diversity-aware CASs (HDA-CASs) characterize new types of CASs in which a collective is composed of hybrid machines and humans that collaborate together with different complementary roles. This emerging HDA-CAS poses several research challenges in terms of programming, management and provisioning. In this paper, we investigate the main issues in programming HDA-CASs. First, we analyze context characterizing HDA-CASs. Second, we propose to use the concept of hybrid compute units to implement HDA-CASs that can be elastic. We call this type of HDA-CASs \(h^2\) CAS (Hybrid Compute Unit-based HDA-CAS). We then discuss a meta-view of \(h^2\) CAS  that describes a \(h^2\) CAS  program. We analyze and present program features for \(h^2\) CAS  in four main different contexts.


Programming Feature Task Graph Service Unit Task Context Independent Task 
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.



We thank Muhammad Z. C. Candra, Mirela Riveni, Ognjen Scekic and Vincenzo (Enzo) Maltese for fruitful discussions on hybrid compute units, elasticity, and collective adaptive systems. The work mentioned in this paper is partially supported by the EU FP7 SmartSociety project under grant \(N^{\circ }\) 600854.


  1. 1.
    Coronato, A., Florio, V.D., Bakhouya, M., Serugendo, G.D.M.: Formal modeling of socio-technical collective adaptive systems. In: Proceedings of the 2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Workshops. SASOW 2012, pp. 187–192. IEEE Computer Society, Washington, DC, USA (2012)Google Scholar
  2. 2.
    Fundamentals of collective adaptive systems.
  3. 3.
    Andrikopoulos, V., Saez, S.G., Karastoyanova, D., Weiss, A.: Towards collaborative, dynamic and complex systems (short paper). In: SOCA, pp. 241–245. IEEE (2013)Google Scholar
  4. 4.
    Bruni, R., Corradini, A., Gadducci, F., Lafuente, A.L., Vandin, A.: Modelling and analyzing adaptive self-assembly strategies with Maude. Sci. Comput. Program. 99, 75–94 (2015)CrossRefGoogle Scholar
  5. 5.
    Hybrid and diversity-aware collective adaptive systems.
  6. 6.
    Truong, H.L., Dustdar, S., Bhattacharya, K.: Conceptualizing and programming hybrid services in the cloud. Int. J. Coop. Info. Syst. 22, 1341003 (2013)CrossRefGoogle Scholar
  7. 7.
    Truong, H.-L., Dam, H.K., Ghose, A., Dustdar, S.: Augmenting complex problem solving with hybrid compute units. In: Lomuscio, A.R., Nepal, S., Patrizi, F., Benatallah, B., Brandić, I. (eds.) ICSOC 2013. LNCS, vol. 8377, pp. 95–110. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  8. 8.
    Little, G., Chilton, L.B., Goldman, M., Miller, R.C.: Exploring iterative and parallel human computation processes. In: Proceedings of the ACM SIGKDD Workshop on Human Computation. HCOMP 2010, pp. 68–76. ACM, New York, USA (2010)Google Scholar
  9. 9.
    Ahmad, S., Battle, A., Malkani, Z., Kamvar, S.: The jabberwocky programming environment for structured social computing. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology. UIST 2011, pp. 53–64. ACM, New York, USA (2011)Google Scholar
  10. 10.
    Dorn, C., Taylor, R.N.: Coupling software architecture and human architecture for collaboration-aware system adaptation. In: Notkin, D., Cheng, B.H.C., Pohl, K. (eds.) ICSE, pp. 53–62. IEEE / ACM, San Francisco (2013) Google Scholar
  11. 11.
    Quinn, A.J., Bederson, B.B.: Human computation: a survey and taxonomy of a growing field. In: Tan, D.S., Amershi, S., Begole, B., Kellogg, W.A., Tungare, M. (eds.) CHI, pp. 1403–1412. ACM, New York (2011) Google Scholar
  12. 12.
    Kulkarni, A.P., Can, M., Hartmann, B.: Turkomatic: automatic recursive task and workflow design for mechanical turk. In: Proceedings of the 2011 Annual Conference Extended Abstracts on Human Factors in Computing Systems. CHI EA 2011, pp. 2053–2058. ACM, New York, USA (2011)Google Scholar
  13. 13.
    Kittur, A., Nickerson, J.V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., Lease, M., Horton, J.: The future of crowd work. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work. CSCW 2013, pp. 1301–1318. ACM, New York, USA (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Distributed Systems GroupVienna University of TechnologyViennaAustria

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