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..
Similar content being viewed by others
References
Buyya R, Broberg J, Goscinski AM (2011) Cloud Computing Principles and Paradigms. Wiley Publishing, United states
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
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
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
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
Mell PM, Grance T (2011) Sp 800-145. the nist definition of cloud computing. Tech. rep., Gaithersburg, MD, USA
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Mytton D (2020) How much energy do data centers use?. URL https://davidmytton.blog/how-much-energy-do-data-centers-use/
Laganà D, Mastroianni C, Meo M, Renga D (2018) Reducing the operational cost of cloud data centers through renewable energy. Algorithms 11(10):145
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
Singh S, Chana I, Singh M, Buyya R (2016) Soccer: self-optimization of energy-efficient cloud resources. Clust Comput 19(4):1787–1800
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
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
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
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
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
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
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
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
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
Lakra AV, Yadav DK (2015) Multi-objective tasks scheduling algorithm for cloud computing throughput optimization. Procedia Comput Sci 48:107–113
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
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
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
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
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
Huang CL, Yeh WC (2019) A new sso-based algorithm for the bi-objective time-constrained task scheduling problem in cloud computing services
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
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
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
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
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
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
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
of Melbourne, U.: Cloudsim 3.0 api (2012). URL http://www.cloudbus.org/cloudsim/doc/api/index.html
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
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
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
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
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
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
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-021-03894-2