Enabling Business-Preference-Based Scheduling of Cloud Computing Resources

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10382)


Although cloud computing technology gets increasingly sophisticated, a resource allocation method still has to be proposed that allows providers to take into consideration the preferences of their customers. The existing engineering-based and economics-based resource allocation methods do not take into account jointly the different objectives that engineers and marketing employees of a cloud provider company follow. This article addresses this issue by presenting the system architecture and, in particular, the business-preference-based scheduling algorithm that integrates the engineering aspects of resource allocation with the economics aspects of resource allocation. To show the workings of the new business-preference-based scheduling algorithm, which integrates a yield management method and a priority-based scheduling method, a simulation has been performed. The results obtained are compared with results from the First-Come-First-Serve scheduling algorithm. The comparison shows that the proposed scheduling algorithm achieves higher revenue than the engineering-based scheduling algorithm.


Cloud computing Resource allocation FCFS Yield management Scheduling Pricing Economics-based resource allocation System architecture 



This research was conducted within the project BASMATI (Cloud Brokerage Across Borders for Mobile Users and Applications), which has received funding from the ICT R&D program of the Korean MSIP/IITP (R0115-16-0001) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 723131.


  1. 1.
    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. Futur. Gener. Comput. Syst. 6, 599–616 (2009)CrossRefGoogle Scholar
  2. 2.
    Altmann, J., Kashef, M.M.: Cost model based service placement in federated hybrid clouds. Futur. Gener. Comput. Syst. 41, 79–90 (2014)CrossRefGoogle Scholar
  3. 3.
    Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1, 7–18 (2010)CrossRefGoogle Scholar
  4. 4.
    Rimal, B.P., Choi, E., Lumb, I.: A taxonomy and survey of cloud computing systems. In: IMS IDC, pp. 44–51 (2009)Google Scholar
  5. 5.
    Jeferry, K., Kousiouris, G., Kyriazis, D., Altmann, J., Ciuffoletti, A., Maglogiannis, I., Nesi, P., Suzic, B., Zhao, Z.: Challenges emerging from future cloud application scenarios. Procedia Comput. Sci. 68, 227–237 (2015)CrossRefGoogle Scholar
  6. 6.
    Risch, M., Altmann, J., Guo, L., Fleming, A., Courcoubetis, C.: The gridecon platform: a business scenario testbed for commercial cloud services. In: International Workshop on GECON, pp. 46–59 (2009)Google Scholar
  7. 7.
    Teng, F., Magoules, F.: Resource pricing and equilibrium allocation policy in cloud computing. In: International Conference on Computer and Information Technology, pp. 195–202 (2010)Google Scholar
  8. 8.
    Mishra, M.K., Rashid, F.: An improved round robin CPU scheduling algorithm with varying time quantum. Int. J. Comput. Sci. Eng. Appl. 4, 1 (2014)Google Scholar
  9. 9.
    Buyya, R., Murshed, M.: Gridsim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurr. Comput. Pract. Exp. 14, 1175–1220 (2002)CrossRefzbMATHGoogle Scholar
  10. 10.
    Dong, F., Akl, S.G.: Scheduling algorithms for grid computing: state of the art and open problems. Technical report (2006)Google Scholar
  11. 11.
    Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop, pp. 1–10 (2008)Google Scholar
  12. 12.
    Osterwalder, A.: The business model ontology: a proposition in a design science approach (2004)Google Scholar
  13. 13.
    Buyya, R., Yeo, C.S., Venugopal, S.: Market-oriented cloud computing: vision, hype, and reality for delivering IT services as computing utilities. In: International Conference on High Performance Computing and Communications, pp. 5–13 (2008)Google Scholar
  14. 14.
    Mell, P., Grance, T.: The NIST definition of cloud computing (2011)Google Scholar
  15. 15.
    Haile, N., Altmann, J.: Value creation in software service platforms. Futur. Gener. Comput. Syst. 55, 495–509 (2016)CrossRefGoogle Scholar
  16. 16.
    Kashef, M.M., Uzbekov, A., Altmann, J., Hovestadt, M.: Comparison of two yield management strategies for cloud service providers. In: Park, James J.(Jong Hyuk), Arabnia, Hamid R., Kim, C., Shi, W., Gil, J.-M. (eds.) GPC 2013. LNCS, vol. 7861, pp. 170–180. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38027-3_18 CrossRefGoogle Scholar
  17. 17.
    Khankasikam, K.: An adaptive round robin scheduling algorithm: a dynamic time quantum approach. Int. J. Adv. Comput. Technol (2013)Google Scholar
  18. 18.
    Srinivasan, S., Kettimuthu, R., Subramani, V., Sadayappan, P.: Characterization of backfilling strategies for parallel job scheduling. In: Workshops at International Conference on Parallel Processing, pp. 514–519 (2002)Google Scholar
  19. 19.
    Sirohi, A., Pratap, A., Aggarwal, M.: Improvised round robin (CPU) scheduling algorithm. Int. J. Comput. Appl. 99, 40–43 (2014)Google Scholar
  20. 20.
    Alam, B.: Fuzzy round robin CPU scheduling algorithm. J. Comput. Sci. 9, 1079–1085 (2013)CrossRefGoogle Scholar
  21. 21.
    Ru, J., Keung, J.: An Empirical investigation on the simulation of priority and shortest-job-first scheduling for cloud-based software systems. In: Australian Software Engineering Conference, pp. 78–87 (2013)Google Scholar
  22. 22.
    Agarwal, D., Jain, S.: Efficient optimal algorithm of task scheduling in cloud computing environment. arXiv Prepr. arXiv:1404.2076 (2014)
  23. 23.
    Altmann, J., Hovestadt, M., Kao, O.: Business support service platform for providers in open cloud computing markets. In: International Conference on Networked Computing, INC, pp. 149–154 (2011)Google Scholar
  24. 24.
    Kjeldsen, A.H., Meyer, P.: Revenue Management - Theory and Practice. Master Thesis, Technical University of Denmark (2005)Google Scholar
  25. 25.
    Samimi, P., Teimouri, Y., Mukhtar, M.: A combinatorial double auction resource allocation model in cloud computing. Inf. Sci. (Ny) 357, 201–216 (2014)CrossRefGoogle Scholar
  26. 26.
    Breskovic, I., Maurer, M., Emeakaroha, V.C., Brandic, I., Altmann, J.: Towards autonomic market management in cloud computing infrastructures. In: CLOSER, pp. 24–34 (2011)Google Scholar
  27. 27.
    Breskovic, I., Altmann, J., Brandic, I.: Creating standardized products for electronic markets. Futur. Gener. Comput. Syst. 29, 1000–1011 (2013)CrossRefGoogle Scholar
  28. 28.
    Altmann, J., Courcoubetis, C., Risch, M.: A marketplace and its market mechanism for trading commoditized computing resources. Ann. des Télécommunications 65, 653–667 (2010)CrossRefGoogle Scholar
  29. 29.
    Weinhardt, C., Anandasivam, A., Blau, B., Borissov, N., Meinl, T., Michalk, W., Stößer, J.: Cloud computing - a classification, business models, and research directions. Bus. Inf. Syst. Eng. 1, 391–399 (2009)CrossRefGoogle Scholar
  30. 30.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53, 50–58 (2010)CrossRefGoogle Scholar
  31. 31.
    Al-Roomi, M., Al-Ebrahim, S., Buqrais, S., Ahmad, I.: Cloud computing pricing models: a survey. Int. J. Grid Distrib. Comput. 6, 93–106 (2013)CrossRefGoogle Scholar
  32. 32.
    Hamsanandhini, S., Mohana, R.S.: Maximizing the revenue with client classification in Cloud Computing market. In: International Conference on Computer, Communication and Informatics, ICCCI, pp. 1–7 (2015)Google Scholar
  33. 33.
    Wang, H., Tianfield, H., Mair, Q.: Auction based resource allocation in cloud computing. Multiagent Grid Syst. 10, 51–66 (2014)CrossRefGoogle Scholar
  34. 34.
    Jallat, F., Ancarani, F.: Yield management, dynamic pricing and CRM in telecommunications. J. Serv. Mark. 22, 465–478 (2008)CrossRefGoogle Scholar
  35. 35.
    Kimes, S.E.: The basics of yield management. Cornell Hotel Restaur. Adm. Q. 30, 14–19 (1989)CrossRefGoogle Scholar
  36. 36.
    Anandasivam, A., Neumann, D.: Managing revenue in Grids. In: 42nd Hawaii International Conference on System Sciences, pp. 1–10 (2009)Google Scholar
  37. 37.
    Netessine, S., Shumsky, R.: Introduction to the theory and practice of yield management. INFORMS Trans. Educ. 3, 34–44 (2002)CrossRefGoogle Scholar
  38. 38.
    Cherkasova, L., Gupta, M.: Analysis of enterprise media server workloads: access patterns, locality, content evolution, and rates of change. ACM Trans. Netw. 12, 781–794 (2004)CrossRefGoogle Scholar
  39. 39.
    Arlitt, M.F., Williamson, C.L.: Web server workload characterization: the search for invariants. ACM SIGMETRICS Perform. Evalu. Rev. 24, 126–137 (1996)CrossRefGoogle Scholar
  40. 40.
    Gmach, D., Rolia, J., Cherkasova, L., Kemper, A.: Workload analysis and demand prediction of enterprise data center applications. In: 10th International Symposium on Workload Characterization, pp. 171–180 (2007)Google Scholar
  41. 41.
    Belobaba, P.P.: Survey paper-airline yield management an overview of seat inventory control. Transp. Sci. 21, 63–73 (1987)CrossRefGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Technology Management, Economics, and Policy Program, Department of Industrial Engineering, College of EngineeringSeoul National UniversitySeoulSouth Korea

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