Annals of Telecommunications

, Volume 72, Issue 1–2, pp 79–93 | Cite as

Scheduling internet of things applications in cloud computing

  • Husnu S. Narman
  • Md. Shohrab Hossain
  • Mohammed Atiquzzaman
  • Haiying Shen


Internet of Things (IoT) is one of the greatest technology revolutions in the history. Due to IoT potential, daily objects will be consciously worked in harmony with optimized performances. However, today, technology is not ready to fully bring its power to our daily life because of huge data analysis requirements in instant time. On the other hand, the powerful data management of cloud computing gives IoT an opportunity to make the revolution in our life. However, the traditional cloud computing server schedulers are not ready to provide services to IoT because IoT consists of a number of heterogeneous devices and applications which are far away from standardization. Therefore, to meet the expectations of users, the traditional cloud computing server schedulers should be improved to efficiently schedule and allocate IoT requests. There are several proposed scheduling algorithms for cloud computing in the literature. However, these scheduling algorithms are limited because of considering neither heterogeneous servers nor dynamic scheduling approach for different priority requests. Our objective is to propose dynamic dedicated server scheduling for heterogeneous and homogeneous systems to efficiently provide desired services by considering priorities of requests. Results show that the proposed scheduling algorithm improves throughput up to 40 % in heterogeneous and homogeneous cloud computing systems for IoT requests. Our proposed scheduling algorithm and related analysis will help cloud service providers build efficient server schedulers which are adaptable to homogeneous and heterogeneous environments by considering system performance metrics, such as drop rate, throughput, and utilization in IoT.


Internet of things Cloud computing Analytical model Heterogeneous and homogeneous multi server Multi-class Queuing system Performance Priority 



This research was supported in part by U.S. NSF grants NSF-1404981.


  1. 1.
    Wang C, Bi Z, Xu LD (2014) Iot and cloud computing in automation of assembly modeling systems. IEEE Trans Ind Inf 10(2):1426–1434CrossRefGoogle Scholar
  2. 2.
    Shon T, Cho J, Han K, Choi H (2014) Toward advanced mobile cloud computing for the internet of things: current issues and future direction. Mobile Networks and Applications 19(3):404–413CrossRefGoogle Scholar
  3. 3.
    Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805CrossRefzbMATHGoogle Scholar
  4. 4.
    Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Futur Gener Comput Syst 29(7):1645–1660CrossRefGoogle Scholar
  5. 5.
    Tan J, Koo S (2014) “A survey of technologies in internet of things”. In: IEEE International Conference on Distributed Computing in Sensor Systems, Marina Del Rey, CA May 26-28, pp 269–274Google Scholar
  6. 6.
    Richter F (2013) Smartphone sales break the billion barrier. Accessed: June 12, 2014. [Online]. Available:
  7. 7.
    Kim W (2009) Cloud computing: Today and tomorrow. Journal of Object Technology 8:65–72CrossRefGoogle Scholar
  8. 8.
    Wang L, Laszewski G, Younge A, He X, Kunze M, Tao J, Fu C (2010) Cloud computing: a perspective study. N Gener Comput 28(2):137–146CrossRefzbMATHGoogle Scholar
  9. 9.
    Heath T, Diniz B, Carrera EV, Jr WM, Bianchini R (2005) “Energy conservation in heterogeneous server clusters”. In: Principles and Practice of Parallel Programming, Chicago, IL, June 15-17, pp 186–195Google Scholar
  10. 10.
    Dastjerdi AV, Buyya R (2014) “Compatibility-aware cloud service composition under fuzzy preferences of users”. IEEE Transactions on Cloud Computing 2(1):1–34CrossRefGoogle Scholar
  11. 11.
    Mars J, Tang L (2013) “Whare-map: Heterogeneity in ”homogeneous” warehouse-scale computers”. SIGARCH Comput Archit News 41(3):619–630CrossRefGoogle Scholar
  12. 12.
    Mars J, Tang L (2013) “Whare-map: Heterogeneity in ”Homogeneous” Warehouse-scale Computers”. In: 40th Annual International Symposium on Computer Architecture, Tel-Aviv, Israel, June 23-27, pp 619–630Google Scholar
  13. 13.
    Delimitrou C, Kozyrakis C (2013) “Qos-aware scheduling in heterogeneous datacenters with paragon”. ACM Trans Comput Syst 4:31Google Scholar
  14. 14.
    Zhang Q, Zhani MF, Boutaba R, Hellerstein JL (2014) Dynamic heterogeneity-aware resource provisioning in the cloud. IEEE Transactions on Cloud Computing 2(2):1–34Google Scholar
  15. 15.
    Ellens W, Zivkovic M, Akkerboom J, Litjens R, van den Berg H (2012) “Performance of cloud computing centers with multiple priority classes”. In: IEEE 5th International Conference on Cloud Computing (CLOUD), Honolulu, HI, June 24-29, pp 245– 252Google Scholar
  16. 16.
    Hu Y, Wong J, Iszlai G, Litoiu M (2009) “Resource provisioning for cloud computing”. In: Conference of the Center for Advanced Studies on Collaborative Research (CASCON ’09), Toronto, Canada, Nov 2-5, pp 101–111Google Scholar
  17. 17.
    Narman HS, Hossain MS, Atiquzzaman M (2014) “DDSS:Dynamic dedicated servers scheduling for multi priority level classes in cloud servers”. In: IEEE International Conference on Communications (ICC), Sydney, Australia, June 10-14, pp 3082 – 3087Google Scholar
  18. 18.
    Narman HS, Hossain MS, Atiquzzaman M (2014) “h-DDSS:Heterogeneous dynamic dedicated servers scheduling in cloud computing”. In: IEEE International Conference on Communications (ICC), Sydney, Australia, June 10-14, pp 3475 – 3480Google Scholar
  19. 19.
    Goswami V, Patra SS, Mund GB (2012) “Performance analysis of cloud with queue-dependent virtual machines”. In: 1st International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, Mar15-17, pp 357–362Google Scholar
  20. 20.
    Peng Chen H, Chong Li S (2010) “A queueing-based model for performance management on cloud”. In: 6th International Conference on Advanced Information Management and Service (IMS), Seoul, Nov 30-Dec 2, pp 83–88Google Scholar
  21. 21.
    Iosup A, Ostermann S, Yigitbasi N, Prodan R, Fahringer T, Epema DHJ (2011) Performance analysis of cloud computing services for Many-Tasks scientific computing. IEEE Trans Parallel Distrib Syst 22:931–945CrossRefGoogle Scholar
  22. 22.
    Khazaei H, Misic J, Misic V (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
  23. 23.
    Xiong K, Perros HG (2009) “Service performance and analysis in cloud computing”. In: IEEE Congress on Services, Los Angeles, CA, July 6-10, pp 693–700Google Scholar
  24. 24.
    Ostermann S, Iosup A, Yigitbasi N, Prodan R, Fahringer T, Epema D (2010) A performance analysis of EC2 cloud computing services for scientific computing. Telecommun Policy 34:115–131Google Scholar
  25. 25.
    Henzinger TA, Singh AV, Singh V, Wies T, Zufferey D (2011) “Static scheduling in clouds”. In: 3rd USENIX Conference on Hot Topics in Cloud Computing, ser. Hotcloud’11, Portland, OR, June14–17Google Scholar
  26. 26.
    Casavant TL, Kuhl JG (1988) A taxonomy of scheduling in general-purpose distributed computing systems. IEEE Trans Softw Eng 14(2):141–154CrossRefGoogle Scholar
  27. 27.
    Xhafa F, Abraham A (2010) “Computational models and heuristic methods for grid scheduling problems”. Futur Gener Comput Syst 26(4):608–621CrossRefGoogle Scholar
  28. 28.
    Lee Y, Leu S, Chang R (2011) “Improving job scheduling algorithms in a grid environment”. Futur Gener Comput Syst 27(8):991–998CrossRefGoogle Scholar
  29. 29.
    Bansal S, Kothari B, Hota C (2011) Dynamic task-scheduling in grid computing using prioritized round robin algorithm. International Journal of Computer Science Issues 8(2):472–477Google Scholar
  30. 30.
    Ghanbari S, Othman M (2012) A priority based job scheduling algorithm in cloud computing. Procedia Eng 50:778– 785CrossRefGoogle Scholar
  31. 31.
    Yang L, Pan C, Zhang E, Liu H (2012) A new class of priority-based weighted fair scheduling algorithm. Phys Procedia 33:942–948CrossRefGoogle Scholar
  32. 32.
    Shah SNM, Zakaria MNB, Mahmood AKB, Pal AJ, Haron N (2012) Agent based priority heuristic for job scheduling on computational grids. Procedia Computer Science 9:479–488CrossRefGoogle Scholar
  33. 33.
    Abba HA, Zakaria N, Shah SNM, Pal A (2012) Design, development and performance analysis of deadline based priority heuristic for job scheduling on a grid. Procedia Computer Science 9Google Scholar
  34. 34.
    Chtourou H, Haouari M (2008) A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling. Comput Ind Eng 55(1):183–194CrossRefGoogle Scholar
  35. 35.
    Yang B, Tan F, Dai Y-S, Guo S (2009) “Performance evaluation of cloud service considering fault recovery”. In: Cloud Computing, Beijing, China, Dec 1-4, pp 571–576Google Scholar
  36. 36.
    Patel S, Bhoi U (2013) Priority based job scheduling techniques in cloud computing a systematic review. International Journal of Scientific & Technology Research 2(11):147–152Google Scholar
  37. 37.
    Gross D, Harris CM (1998) Fundamentals of Queueing Theory (Wiley Series in Probability and Statistics).1em plus 0.5em minus 0.4emWiley-InterscienceGoogle Scholar
  38. 38.
    Alves FSQ, Yehia HC, Pedrosa LAC, Cruz FRB, Kerbache L (2011) Upper bounds on performance measures of heterogeneous M/M/c queues. Math Probl Eng 2011Google Scholar
  39. 39.
    Narman HS , Hossain MS, Atiquzzaman M (2013) “Multi class traffic analysis of single and multi-band queuing system”. In: IEEE Global Communications Conference (GLOBECOM), Atlanta, GA, Dec 9-13, pp 1422–1427Google Scholar
  40. 40.
    Novet J (2015) Dropbox hits 150k paying business customers, with 50k joining this year. Accessed: May 20, 2016. [Online]. Available:
  41. 41.
    Appenzeller G, Keslassy I, McKeown N (2004) Sizing router buffers. Computer Communication Review 34:281–292CrossRefGoogle Scholar

Copyright information

© Institut Mines-Télécom and Springer-Verlag France 2016

Authors and Affiliations

  • Husnu S. Narman
    • 1
  • Md. Shohrab Hossain
    • 2
  • Mohammed Atiquzzaman
    • 3
  • Haiying Shen
    • 1
  1. 1.Holcombe Department of Electrical and Computer EngineeringClemson UniversityClemsonUSA
  2. 2.Department of Computer Science and EngineeringBangladesh University of Engineering and TechnologyDhakaBangladesh
  3. 3.School of Computer ScienceUniversity of OklahomaNormanUSA

Personalised recommendations