A Security-Driven Approach to Online Job Scheduling in IaaS Cloud Computing Systems

  • Jakub Gąsior
  • Franciszek Seredyński
  • Andrei Tchernykh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10778)


The paper presents a general framework to study issues of multi-objective on-line scheduling in the Infrastructure as a Service model of Cloud Computing (CC) systems taking into account the aspects of the total work-flow execution cost while meeting the deadline and risk rate constraints. Our goal is providing fairness between concurrent job submissions by minimizing tardiness of individual applications and dynamically rescheduling them to the best suited resources. The system, via the scheduling algorithms, is responsible to guarantee the corresponding Quality of Service (QoS) and Service Level Agreement (SLA) for all accepted jobs.


Cloud Computing Service Level Agreement Security-aware scheduling Infrastructure as a Service 


  1. 1.
    Ali, M., Khan, S.U., Vasilakos, A.V.: Security in cloud computing: opportunities and challenges. Inf. Sci. 305, 357–383 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Benoit, A., Marchal, L., Pineau, J.-F., Robert, Y., Vivien, F.: Scheduling concurrent bag-of-tasks applications on heterogeneous platforms. IEEE Trans. Comput. 59(2), 202–217 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Buyya, R., Abramson, D., Giddy, Í.: Nimrod/G: an architecture for a resource management and scheduling system in a global computational grid. In: Proceedings of the Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region (HPC Asia 2000), pp. 283–289. IEEE Computer Society Press (2000)Google Scholar
  4. 4.
    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
  5. 5.
    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. Softw. Pract. Exp. 41(1), 23–50 (2011)CrossRefGoogle Scholar
  6. 6.
    Celaya, J., Marchal, L.: A fair decentralized scheduler for bag-of-tasks applications on desktop grids. In: 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), pp. 538–541, May 2010Google Scholar
  7. 7.
    Chang, V.: The business intelligence as a service in the cloud. Future Gener. Comput. Syst. 37, 512–534 (2014). Special Section: Innovative Methods and Algorithms for Advanced Data-Intensive Computing. Special Section: Semantics, Intelligent Processing and Services for Big Data. Special Section: Advances in Data-Intensive Modelling and Simulation. Special Section: Hybrid Intelligence for Growing Internet and its ApplicationsCrossRefGoogle Scholar
  8. 8.
    Chang, V.: Towards a big data system disaster recovery in a private cloud. Ad Hoc Netw. 35, 65–82 (2015). Special Issue on Big Data Inspired Data Sensing, Processing and Networking TechnologiesCrossRefGoogle Scholar
  9. 9.
    Chang, V., Kuo, Y.-H., Ramachandran, M.: Cloud computing adoption framework: a security framework for business clouds. Future Gener. Comput. Syst. 57, 24–41 (2016)CrossRefGoogle Scholar
  10. 10.
    Chang, V., Walters, R.J., Wills, G.B.: Organisational sustainability modelling-an emerging service and analytics model for evaluating cloud computing adoption with two case studies. Int. J. Inf. Manag. 36(1), 167–179 (2016)CrossRefGoogle Scholar
  11. 11.
    de Assunção, M.D., Buyya, R.: Performance analysis of multiple site resource provisioning: effects of the precision of availability information. In: Sadayappan, P., Parashar, M., Badrinath, R., Prasanna, V.K. (eds.) HiPC 2008. LNCS, vol. 5374, pp. 157–168. Springer, Heidelberg (2008). CrossRefGoogle Scholar
  12. 12.
    Gąsior, J., Seredyński, F.: A Sandpile cellular automata-based scheduler and load balancer. J. Comput. Sci. 21(Suppl. C), 460–468 (2017)CrossRefGoogle Scholar
  13. 13.
    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, DC (2011)Google Scholar
  14. 14.
    Kolodziej, J., Khan, S.U., Wang, L., Kisiel-Dorohinicki, M., Madani, S.A., Niewiadomska-Szynkiewicz, E., Zomaya, A.Y., Xu, C.-Z.: Security, energy, and performance-aware resource allocation mechanisms for computational grids. Future Gener. Comput. Syst. 31, 77–92 (2014)CrossRefGoogle Scholar
  15. 15.
    Modi, P.J., Shen, W.-M., Tambe, M., Yokoo, M.: Adopt: asynchronous distributed constraint optimization with quality guarantees. Artif. Intell. 161(1–2), 149–180 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Nesmachnow, S., Perfumo, C., Goiri, I.: 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
  17. 17.
    Raycroft, P., Jansen, R., Jarus, M., Brenner, P.R.: Performance bounded energy efficient virtual machine allocation in the global cloud. Sustain. Comput.: Inform. Syst. 4(1), 1–9 (2014)Google Scholar
  18. 18.
    Viswanathan, S., Veeravalli, B., Robertazzi, T.G.: Resource-aware distributed scheduling strategies for large-scale computational cluster/grid systems. IEEE Trans. Parallel Distrib. Syst. 18(10), 1450–1461 (2007)CrossRefGoogle Scholar
  19. 19.
    Singh, G., Kesselman, C., Deelman, E.: A provisioning model and its comparison with best-effort for performance-cost optimization in grids. In: Proceedings of the 16th International Symposium on High Performance Distributed Computing, HPDC 2007, pp. 117–126. ACM, New York (2007)Google Scholar
  20. 20.
    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
  21. 21.
    Tchernykh, A., Lozano, L., Bouvry, P., Pecero, J.E., Schwiegelshohn, U., Nesmachnow, S.: Energy-aware on-line scheduling: ensuring quality of service for IaaS clouds. In: 2014 International Conference on High Performance Computing Simulation (HPCS), pp. 911–918, July 2014Google Scholar
  22. 22.
    Tchernykh, A., Lozano, L., Schwiegelshohn, U., Bouvry, P., Pecero, J.E., Nesmachnow, S., Drozdov, A.Y.: Online bi-objective scheduling for IaaS clouds ensuring quality of service. J. Grid Comput. 14(1), 5–22 (2016)CrossRefGoogle Scholar
  23. 23.
    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). Special Section: Intelligent Big Data Processing. Special Section: Behavior Data Security Issues in Network Information Propagation. Special Section: Energy-Efficiency in Large Distributed Computing Architectures. Special Section: eScience Infrastructure and ApplicationsCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jakub Gąsior
    • 1
  • Franciszek Seredyński
    • 1
  • Andrei Tchernykh
    • 2
  1. 1.Department of Mathematics and Natural SciencesCardinal Stefan Wyszyński UniversityWarsawPoland
  2. 2.CICESE Research CenterEnsenadaMexico

Personalised recommendations