Skip to main content
Log in

A cost-effective power-aware approach for scheduling cloudlets in cloud computing environments

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

On a cloud platform, the user requests are managed through workload units called cloudlets which are assigned to virtual machines through cloudlet scheduling mechanism that mainly aims at minimizing the request processing time by producing effective small length schedules. The efficient request processing, however, requires excessive utilization of high-performance resources which incurs large overhead in terms of monetary cost and energy consumed by physical machines, thereby rendering cloud platforms inadequate for cost-effective green computing environments. This paper proposes a power-aware cloudlet scheduling (PACS) algorithm for mapping cloudlets to virtual machines. The algorithm aims at reducing the request processing time through small length schedules while minimizing energy consumption and the cost incurred. For allocation of virtual machines to cloudlets, the algorithm iteratively arranges virtual machines (VMs) in groups using weights computed through optimization and rescaling of parameters including VM resources, cost of utilization of resources, and power consumption. The experiments performed with a diverse set of configurations of cloudlets and virtual machines show that the PACS algorithm achieves a significant overall performance improvement factor ranging from 3.80 to 23.82 over other well-known cloudlet scheduling algorithms..

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Buyya R, Broberg J, Goscinski AM (2011) Cloud Computing Principles and Paradigms. Wiley Publishing, United states

    Book  Google Scholar 

  2. Fazio M, Ranjan R, Girolami M, Taheri J, Dustdar S, Villari M (2018) A note on the convergence of iot, edge, and cloud computing in smart cities. IEEE Cloud Comput 5(05):22–24. https://doi.org/10.1109/MCC.2018.053711663

    Article  Google Scholar 

  3. AlJahdali H, Albatli A, Garraghan P, Townend P, Lau L, Xu J (2014) Multi-tenancy in cloud computing. In: 2014 IEEE 8th International Symposium on Service Oriented System Engineering, pp. 344–351. IEEE

  4. Herbst NR, Kounev S, Reussner R (2013) Elasticity in cloud computing: What it is, and what it is not. In: Proceedings of the 10th International Conference on Autonomic Computing (\(\{\)ICAC\(\}\) 13), pp. 23–27

  5. Kondo D, Javadi B, Malecot P, Cappello F, Anderson DP (2009) Cost-benefit analysis of cloud computing versus desktop grids. In: 2009 IEEE International Symposium on Parallel Distributed Processing, pp. 1–12. https://doi.org/10.1109/IPDPS.2009.5160911

  6. Mell PM, Grance T (2011) Sp 800-145. the nist definition of cloud computing. Tech. rep., Gaithersburg, MD, USA

  7. Varghese B, Buyya R (2018) Next generation cloud computing: New trends and research directions. Futur Gener Comput Syst 79:849–861. https://doi.org/10.1016/j.future.2017.09.020

    Article  Google Scholar 

  8. Birke R, Chen LY, Smirni E (2012) Data centers in the cloud: A large scale performance study. In: 2012 IEEE Fifth International Conference on Cloud Computing, pp. 336–343. IEEE

  9. Mann ZA (2015) Allocation of virtual machines in cloud data centers—a survey of problem models and optimization algorithms. ACM Comput Surv 48(1). https://doi.org/10.1145/2797211

  10. Salimian L, Safi F (2013) Survey of energy efficient data centers in cloud computing. In: Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, UCC ’13, p. 369–374. IEEE Computer Society, USA

  11. Masdari M, Nabavi SS, Ahmadi V (2016) An overview of virtual machine placement schemes in cloud computing. J Netw Comput Appl 66:106–127. https://doi.org/10.1016/j.jnca.2016.01.011

    Article  Google Scholar 

  12. Gao Y, Guan H, Qi Z, Hou Y, Liu L (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79(8):1230–1242

    Article  MathSciNet  Google Scholar 

  13. Xu M, Tian W, Buyya R (2017) A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurrency and Computation: Practice and Experience 29(12):e4123. https://doi.org/10.1002/cpe.4123. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.4123. E4123 cpe.4123

  14. Li X, Garraghan P, Jiang X, Wu Z, Xu J (2018) Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy. IEEE Trans Parallel Distrib Syst 29(6):1317–1331. https://doi.org/10.1109/TPDS.2017.2688445

    Article  Google Scholar 

  15. Xiao Z, Jiang J, Zhu Y, Ming Z, Zhong S, Cai S (2015) A solution of dynamic vms placement problem for energy consumption optimization based on evolutionary game theory. J Syst Software 101:260–272. https://doi.org/10.1016/j.jss.2014.12.030. URL http://www.sciencedirect.com/science/article/pii/S016412121400288X

  16. Bobroff N, Kochut A, Beaty K (2007) Dynamic placement of virtual machines for managing sla violations. In: 2007 10th IFIP/IEEE International Symposium on Integrated Network Management, pp. 119–128. IEEE

  17. Beloglazov A, Buyya R (2012) Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans Parallel Distrib Syst 24(7):1366–1379

    Article  Google Scholar 

  18. Paul I, Yalamanchili S, John LK (2012) Performance impact of virtual machine placement in a datacenter. In: 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC), pp. 424–431. IEEE

  19. Ari A, Irépran D, Titouna C, Labraoui N, Gueroui A (2017) Efficient and scalable aco-based task scheduling for green cloud computing environment. In: Proceedings of the 2017 IEEE International Conference on Smart Cloud, pp. 66–71. https://doi.org/10.1109/SmartCloud.2017.17

  20. Al-Olimat HS, Alam M, Green R, Lee JK (2015) Cloudlet scheduling with particle swarm optimization. In: 2015 Fifth International Conference on Communication Systems and Network Technologies, pp. 991–995. IEEE

  21. Yu J, Buyya R (2006) Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci. Program. 14(3,4), 217–230. URL http://dl.acm.org/citation.cfm?id=1376960.1376967

  22. Mytton D (2020) How much energy do data centers use?. URL https://davidmytton.blog/how-much-energy-do-data-centers-use/

  23. Laganà D, Mastroianni C, Meo M, Renga D (2018) Reducing the operational cost of cloud data centers through renewable energy. Algorithms 11(10):145

    Article  Google Scholar 

  24. Wu CM, Chang RS, Chan HY (2014) A green energy-efficient scheduling algorithm using the dvfs technique for cloud datacenters. Future Generation Computer Systems 37, 141 – 147. https://doi.org/10.1016/j.future.2013.06.009. 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 Applications

  25. Singh S, Chana I, Singh M, Buyya R (2016) Soccer: self-optimization of energy-efficient cloud resources. Clust Comput 19(4):1787–1800

    Article  Google Scholar 

  26. Hameed A, Khoshkbarforoushha A, Ranjan R, Jayaraman PP, Kolodziej J, Balaji P, Zeadally S, Malluhi QM, Tziritas N, Vishnu A et al (2016) A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7):751–774

    Article  MathSciNet  Google Scholar 

  27. Duy TVT, Sato Y, Inoguchi Y (2010) Performance evaluation of a green scheduling algorithm for energy savings in cloud computing. In: 2010 IEEE international symposium on parallel & distributed processing, workshops and Phd forum (IPDPSW), pp. 1–8. IEEE

  28. Lin C, Lu S (2011) Scheduling scientific workflows elastically for cloud computing. In: Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing, CLOUD ’11, p. 746–747. IEEE Computer Society, USA. https://doi.org/10.1109/CLOUD.2011.110

  29. Xu M, Cui L, Wang H, Bi Y (2009) A multiple qos constrained scheduling strategy of multiple workflows for cloud computing. In: 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications, pp. 629–634. IEEE

  30. Lu G, Sun Y, Zhang Z, et al (2013) A concurrent level based scheduling for workflow applications within cloud computing environment. In: Joint International Conference on Pervasive Computing and the Networked World, pp. 400–411. Springer

  31. Nasr AA, El-Bahnasawy NA, Attiya G, El-Sayed A (2019) Using the tsp solution strategy for cloudlet scheduling in cloud computing. J Netw Syst Manage 27(2):366–387. https://doi.org/10.1007/s10922-018-9469-9

    Article  Google Scholar 

  32. Genez TA, Bittencourt LF, Madeira ER (2012) Workflow scheduling for saas/paas cloud providers considering two sla levels. In: 2012 IEEE Network Operations and Management Symposium, pp. 906–912. IEEE

  33. Zhu L, Li Q, He L (2012) Study on cloud computing resource scheduling strategy based on the ant colony optimization algorithm. Int J Comput Sci Issues (IJCSI) 9(5):54

    Google Scholar 

  34. Rodriguez M, Buyya R (2014) Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds. IEEE Trans Cloud Comput 2(02):1–1. https://doi.org/10.1109/TCC.2014.2314655

    Article  Google Scholar 

  35. Lakra AV, Yadav DK (2015) Multi-objective tasks scheduling algorithm for cloud computing throughput optimization. Procedia Comput Sci 48:107–113

    Article  Google Scholar 

  36. Chen ZG, Du KJ, Zhan ZH, Zhang J (2015) Deadline constrained cloud computing resources scheduling for cost optimization based on dynamic objective genetic algorithm. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 708–714. IEEE. https://doi.org/10.1109/CEC.2015.7256960

  37. Ge JW, Yuan YS (2013) Research of cloud computing task scheduling algorithm based on improved genetic algorithm. In: Instruments, Measurement, Electronics and Information Engineering, Applied Mechanics and Materials, vol. 347, pp. 2426–2429. Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/AMM.347-350.2426

  38. Rekha P, Dakshayini M (2019) Efficient task allocation approach using genetic algorithm for cloud environment. Clust Comput 22:1–11. https://doi.org/10.1007/s10586-019-02909-1

    Article  Google Scholar 

  39. Liu H, Xu D, Miao HK (2011) Ant colony optimization based service flow scheduling with various qos requirements in cloud computing. In: Proceedings of the 2011 First ACIS International Symposium on Software and Network Engineering, SSNE ’11, p. 53–58. IEEE Computer Society, USA. https://doi.org/10.1109/SSNE.2011.18

  40. Li H, Fu Y, Zhan Z, Li J (2015) Renumber strategy enhanced particle swarm optimization for cloud computing resource scheduling. In: IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, May 25-28, 2015, pp. 870–876. IEEE. https://doi.org/10.1109/CEC.2015.7256982

  41. Huang CL, Yeh WC (2019) A new sso-based algorithm for the bi-objective time-constrained task scheduling problem in cloud computing services

  42. Yang L, Cao J, Liang G, Han X (2016) Cost aware service placement and load dispatching in mobile cloud systems. IEEE Trans Comput 65(5):1440–1452. https://doi.org/10.1109/TC.2015.2435781

    Article  MathSciNet  MATH  Google Scholar 

  43. Mahmud R, Srirama SN, Ramamohanarao K, Buyya R (2020) Profit-aware application placement for integrated fog-cloud computing environments. Journal of Parallel and Distributed Computing 135:177–190. https://doi.org/10.1016/j.jpdc.2019.10.001. URL http://www.sciencedirect.com/science/article/pii/S0743731519300346

  44. Balagoni Y, Rao RR (2017) Locality-load-prediction aware multi-objective task scheduling in the heterogeneous cloud environment. Indian Journal of Science and Technology 10(9). URL http://www.indjst.org/index.php/indjst/article/view/106576

  45. Kaja S, Shakshuki E, Guntuka S, Yasar AUH, Malik H (2019) Acknowledgment scheme using cloud for node networks with energy-aware hybrid scheduling strategy. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-019-01629-z

  46. Zhao C, Zhang S, Liu Q, Xie J, Hu J (2009) Independent tasks scheduling based on genetic algorithm in cloud computing. In: Proceedings of the 5th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM’09, p. 5548–5551. IEEE Press

  47. Hamad S, Omara F (2016) Genetic-based task scheduling algorithm in cloud computing environment. Int J Adv Comput Sci Appl 7:550–556. https://doi.org/10.14569/IJACSA.2016.070471

    Article  Google Scholar 

  48. Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exper 41(1):23–50. https://doi.org/10.1002/spe.995

    Article  Google Scholar 

  49. of Melbourne, U.: Cloudsim 3.0 api (2012). URL http://www.cloudbus.org/cloudsim/doc/api/index.html

  50. Ye Z, Zhou X, Bouguettaya A (2011) Genetic algorithm based qos-aware service compositions in cloud computing. In: Proceedings of the 16th International Conference on Database Systems for Advanced Applications: Part II, DASFAA’11, p. 321–334. Springer-Verlag, Berlin, Heidelberg

  51. Zhong Z, Chen K, Zhai X, Zhou S (2016) Virtual machine-based task scheduling algorithm in a cloud computing environment. Tsinghua Sci Technol 21(6):660–667

    Article  Google Scholar 

  52. Chen WN, Zhang J (2012) A set-based discrete pso for cloud workflow scheduling with user-defined qos constraints. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 773–778. IEEE

  53. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comp 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Minhaj Ahmad Khan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, M.A. A cost-effective power-aware approach for scheduling cloudlets in cloud computing environments. J Supercomput 78, 471–496 (2022). https://doi.org/10.1007/s11227-021-03894-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03894-2

Keywords

Navigation