The Journal of Supercomputing

, Volume 74, Issue 5, pp 2116–2150 | Cite as

Preemptive cloud resource allocation modeling of processing jobs

  • Shahin Vakilinia
  • Mohamed Cheriet


Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.


Big data processing Markov chain Cloud computing Queuing theory 


  1. 1.
    Gandhi A, Harchol-Balter M (2011) How data center size impacts the effectiveness of dynamic power management? In: Proceedings of 49th Annual Allerton Conference on Communication, Control, and Computing, USA, Allerton. pp 1164–1169Google Scholar
  2. 2.
    Gandhi A, Harchol-Balter M, Rajarshi D, Lefurgy C (2009) Optimal power allocation in server farms. ACM SIGMETRICS Perform Eval 37(1):157–168Google Scholar
  3. 3.
    Gandhi A, Harchol-Balter M, Raghunathan R, Kozuch MA (2012) Autoscale: dynamic, robust capacity management for multi-tier data centers. ACM Trans Comput Syst TOCS 30(4):14–26Google Scholar
  4. 4.
    Iosup A, Dumitrescu C, Epema DHJ, Li H, Wolters L (2006) How are real grids used? In: Proceeding of 7th IEEE/ACM International Conference on Grid Computing. pp 262–269Google Scholar
  5. 5.
    Lublin U, Feitelson DG (2003) Workload on parallel supercomputers: modeling characteristics of rigid jobs. J Parallel Distrib Comput 63(11):1105–1122CrossRefMATHGoogle Scholar
  6. 6.
    Alexandru I, Ostermann S, Nezih M, Yigitbasi C, Prodan R, Fahringer T, Epema D (2011) Performance analysis of cloud computing services for many-tasks large data stream processing. IEEE Trans Parallel Distrib Syst 22(6):931–945CrossRefGoogle Scholar
  7. 7.
    Salehi MA, Amini M, Javadi B, Buyya R (2014) Resource provisioning based on preempting VMs in distributed systems. Concurr Comput Pract Exp J 26(2):412–433CrossRefGoogle Scholar
  8. 8.
    Vakilinia S, Heidarpour B, Cheriet M (2016) Energy efficient resource allocation in cloud computing environments. IEEE Access J PP(99):1–13Google Scholar
  9. 9.
    Vakilinia S, Zhu X, Qiu D (2016) Analysis and optimization of big-data stream processing. In: Proceeding of IEEE Globecom, Washington, DC, USAGoogle Scholar
  10. 10.
    Iosup A, Sonmez OO, Anoep S, Epema DHJ (2008) The performance of bags-of-tasks in large-scale distributed systems. In: Proceedings of the 17th ACM International Symposium on High Performance Distributed Computing. pp 97–108Google Scholar
  11. 11.
    Brandwajn A, Begin T, Castel-Taleb H (2016) Performance evaluation of cloud computing centers with general arrivals and service. IEEE Trans Parallel Distrib Syst PP(99):1–8Google Scholar
  12. 12.
    Vakilinia S, Ali MM, Qiu D (2015) Modeling of the resource allocation in cloud computing centers. Comput Netw 91(3):453–470CrossRefGoogle Scholar
  13. 13.
    Khazaei H, Misic J, Misic VB, Rashwand S (2012) Analysis of a pool management scheme for cloud computing centers. IEEE Trans Parallel Distrib Syst 99(5):849–861Google Scholar
  14. 14.
    Khazaei H, Misic J, Misic VB (2012) Performance analysis of cloud computing centers using m/g/m/m\(+\) r queuing systems. IEEE Trans Parallel Distrib Syst 23(5):936–943CrossRefGoogle Scholar
  15. 15.
    Meng X, Pappas V, Zhang L (2010) Improving the scalability of data center networks with traffic-aware VM placement. In: The Proceeding of INFOCOM. pp 1–9Google Scholar
  16. 16.
    Li Xin, Wu J, Tang Sh, Lu S (2014) Let’s stay together: towards traffic aware VM placement in data centers. In: The Proceeding of INFOCOM. pp 1842–1850Google Scholar
  17. 17.
    Deelman E, Blythe J, Gil Y, Kesselman C, Mehta G, Patil S, Su M-H, Vahi K, Livny M (2004) Pegasus: mapping large data streams onto the grid. In: European Across Grids Conference. p 1120Google Scholar
  18. 18.
    Ramakrishnan L, Koelbel C, Kee Y-S, Wolski R, Nurmi D, Gannon D, Obertelli G, YarKhan A, Mandal A, Huang TM, Thyagaraja K, Zagorodnov D (2009) VGrADS: Enabling e-science workflows on grids and clouds with fault tolerance. In: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis. pp 1–12Google Scholar
  19. 19.
    Wang L, Tao J, Kunze M, Castellanos AC, Kramer D, Karl W (2008) Stream cloud computing: early definition and experience. In: Proceeding of 10th IEEE International Conference on High Performance Computing and Communications. pp 825–830Google Scholar
  20. 20.
    Warneke D, Kao O (2011) Exploiting dynamic resource allocation for efficient parallel data processing in the cloud. IEEE Trans Parallel Distrib Syst 22(6):985–997CrossRefGoogle Scholar
  21. 21.
    Deelman E, Singh G, Livny M, Berriman B, John Good (2008) The cost of doing science on the cloud: the montage example. In: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing. pp 50–55Google Scholar
  22. 22.
    Ludascher B, Altintas I, Berkley C et al (2006) Scientific workflow management and the Kepler system. Concurrency Comput: Pract Experience 18(10):1039–1065Google Scholar
  23. 23.
    Oinn T, Addis M, Ferris J, Marvin D, Senger M, Greenwood M, Carver T, Glover K, Pocock MR, Wipat A, Li P (2004) Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20(17):3045–3054CrossRefGoogle Scholar
  24. 24.
    Khojasteh H, Misic J, Misic V (2016) Prioritization of overflow tasks to improve performance of mobile cloud. IEEE Trans Cloud Comput PP(99)Google Scholar
  25. 25.
    Sossa RM, Buyya R (2014) Deadline based resource provisioning and scheduling algorithm for large data streams on clouds. IEEE Trans Cloud Comput 2(2):222–235CrossRefGoogle Scholar
  26. 26.
    Yang L, Zhu X, Chen H, Wang Ji, Yin Shu, Liu X (2014) Real-time tasks oriented energy-aware scheduling in virtualized clouds. IEEE Trans Cloud Comput 2(2):168–180CrossRefGoogle Scholar
  27. 27.
    Yao Y, Tai J, Sheng B, Mi N (2015) LsPS: A job size-based scheduler for efficient task assignments in Hadoop. IEEE Trans Cloud Comput 3(4):411–424CrossRefGoogle Scholar
  28. 28.
    Zhao H, Pan M, Liu X, Li X, Fang Y (2015) Exploring fine-grained resource rental planning in cloud computing. IEEE Trans Cloud Comput 3(3):304–317CrossRefGoogle Scholar
  29. 29.
    Kleinrock L (1976) Queuing systems, vol I. Wiley, HobokenMATHGoogle Scholar
  30. 30.
    Vakilinia S (2015) Performance modeling and optimization of resource allocation in cloud computing systems. Doctoral dissertation, Concordia UniversityGoogle Scholar
  31. 31.
    Conway R, Richard W, Maxwell L, Miller L (2012) Theory of scheduling. Courier Dover Publications, New YorkMATHGoogle Scholar
  32. 32.
    Shengbo Ch, Sun Y, Ulas D, Huang KL, Sinha P, Liang G, Liu X, Shroff NB (2014) When queueing meets coding: optimal-latency data retrieving scheme in storage clouds. In: Proceeding of IEEE INFOCOMGoogle Scholar
  33. 33.
    Liang G, Kozat U (2013) FAST CLOUD: Pushing the envelope on delay performance of cloud storage with coding. IEEE/ACM Trans Netw 1(1):2012–2025Google Scholar
  34. 34.
    Simon F, Wong R, Vasilakos A (2015) Accelerated pso swarm search feature selection for data stream mining big data. IEEE Trans Serv Comput PP(99):33–45Google Scholar
  35. 35.
    Keilson J (2012) Markov chain models—rarity and exponentially. Springer, LondonMATHGoogle Scholar
  36. 36.
    Latouche G, Ramaswami V (1999) Introduction to matrix analytic methods in stochastic modeling. J Appl Math Stoch Anal 12(4):435–436CrossRefMATHGoogle Scholar
  37. 37.
    Stewart WJ (1994) Introduction to the numerical solution of markov chains. Princeton University Press, Princeton, pp 121–138Google Scholar
  38. 38.
    Kaufman JF (1981) Blocking in a shared resource environment. IEEE Trans Commun 29:1474–1481CrossRefGoogle Scholar
  39. 39.
    Vinodrai DP, McIntosh GD (2005) Virtualization of input/output devices in a logically partitioned data processing system. U.S. Patent, 6,944,847, issuedGoogle Scholar
  40. 40.
    Ben-Yehuda S, Liguori AN, Wasserman OL, Yassour BA (2013) Multiple layers of virtualization in a computing system. United States patent, US 8,392,916Google Scholar
  41. 41.
    Fox A, Griffith R, Joseph A, Katz R et al. (2009) Above the clouds: a Berkeley view of cloud computing. Department of Electrical Engineering and Computer Science, University of California, Berkeley, Rep. UCB/EECS, vol 28. p 13Google Scholar
  42. 42.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringÉcole de technologie supérieureMontrealCanada

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