Advertisement

Security-Aware Distributed Job Scheduling in Cloud Computing Systems: A Game-Theoretic Cellular Automata-Based Approach

  • Jakub GąsiorEmail author
  • Franciszek Seredyński
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

We consider the problem of security-aware scheduling and load balancing in Cloud Computing (CC) systems. This optimization problem we replace by a game-theoretic approach where players tend to achieve a solution by reaching a Nash equilibrium. We propose a fully distributed algorithm based on applying Iterated Spatial Prisoner’s Dilemma (ISPD) game and a phenomenon of collective behavior of players participating in the game. Brokers representing users participate in the game to fulfill their own three criteria: the execution time of the submitted tasks, their execution cost and the level of provided Quality of Service (QoS). We experimentally show that in the process of the game a solution is found which provides an optimal resource utilization while users meet their applications’ performance and security requirements with minimum expenditure and overhead.

Keywords

Collective behavior Multi-agent systems Spatial Prisoner’s Dilemma Game Second order cellular automata 

References

  1. 1.
    Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)CrossRefGoogle Scholar
  2. 2.
    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 Gener. Comput. Syst. 25(6), 599–616 (2009)CrossRefGoogle Scholar
  3. 3.
    Gąsior, J., Seredyński, F.: Decentralized job scheduling in the cloud based on a spatially generalized Prisoner’s Dilemma game. Appl. Math. Comput. Sci. 25(4), 737–751 (2015)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Gąsior, J., Seredyński, F., Tchernykh, A.: A security-driven approach to online job scheduling in IaaS cloud computing systems. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds.) PPAM 2017. LNCS, vol. 10778, pp. 156–165. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-78054-2_15CrossRefGoogle Scholar
  5. 5.
    Jansen, W.A.: Cloud hooks: security and privacy issues in cloud computing. In: Proceedings of the 2011 44th Hawaii International Conference on System Sciences, HICSS 2011, pp. 1–10. IEEE Computer Society, Washington (2011)Google Scholar
  6. 6.
    Katsumata, Y., Ishida, Y.: On a membrane formation in a spatio-temporally generalized Prisoner’s Dilemma. In: Umeo, H., Morishita, S., Nishinari, K., Komatsuzaki, T., Bandini, S. (eds.) ACRI 2008. LNCS, vol. 5191, pp. 60–66. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-79992-4_8CrossRefGoogle Scholar
  7. 7.
    Kolodziej, J., et al.: Security, energy, and performance-aware resource allocation mechanisms for computational grids. Future Gener. Comput. Syst. 31, 77–92 (2014)CrossRefGoogle Scholar
  8. 8.
    Nesmachnow, S., Perfumo, C., Goiri, Í.: Controlling datacenter power consumption while maintaining temperature and QoS levels. In: 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet), pp. 242–247, October 2014Google Scholar
  9. 9.
    Nowak, M.A., May, R.M.: Evolutionary games and spatial chaos. Nature 359, 826 (1992)CrossRefGoogle Scholar
  10. 10.
    Rossi, F., Bandyopadhyay, S., Wolf, M., Pavone, M.: Review of multi-agent algorithms for collective behavior: a structural taxonomy. IFAC-PapersOnLine 51(12), 112–117 (2018). IFAC Workshop on Networked & Autonomous Air and Space Systems NAASS 2018CrossRefGoogle Scholar
  11. 11.
    Seredynski, F.: Competitive coevolutionary multi-agent systems: the application to mapping and scheduling problems. J. Parallel Distrib. Comput. 47(1), 39–57 (1997)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Song, S., Hwang, K., Kwok, Y.-K.: Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling. IEEE Trans. Comput. 55(6), 703–719 (2006)CrossRefGoogle Scholar
  13. 13.
    Tchernykh, A., et al.: Online bi-objective scheduling for IaaS clouds ensuring quality of service. J. Grid Comput. 14(1), 5–22 (2016)CrossRefGoogle Scholar
  14. 14.
    Tchernykh, A., Pecero, J.E., Barrondo, A., Schaeffer, E.: Adaptive energy efficient scheduling in Peer-to-Peer desktop grids. Future Gener. Comput. Syst. 36, 209–220 (2014)CrossRefGoogle Scholar
  15. 15.
    Wolfram, S.: A New Kind of Science. Wolfram Media, Champaign (2002)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Mathematics and Natural SciencesCardinal Stefan Wyszyński UniversityWarsawPoland

Personalised recommendations