Abstract
Cloud computing is a collection of heterogeneous autonomous systems to support the emerging computational paradigm with the help of flexible computational architecture. In order to support enormous computational load, it exhibits huge energy consumption (EC) which causes the degradation of system performance. In this paper, we have proposed an optimized energy-efficient scheduling model using quantum-inspired genetic algorithm (QIGA) to minimize energy consumption in the cloud data center. Our model establishes a trade-off between execution time and EC without compromising system performance. A suitable comparison between our proposed model with some existing model has been performed to validate our task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, Y., Esseghir, M., Boulahia, L.M.: Evaluation of parameters importance in cloud service selection using rough sets. App. Math. 7(06), 527 (2016)
Manvi, S.S., Shyam, G.K.: Resource management for infrastructure as a service (IAAS) in cloud computing: a survey. J. Network Computer Appl. 41, 424–440 (2014)
Lei, H., Wang, R., Zhang, T., Liu, Y., Zha, Y.: A multi-objective co-evolutionary algorithm for energy efficient scheduling on a green data center. Computers Oper. Res. 75, 103–117 (2016)
Dayarathna, M., Wen, Y., Fan, R.: Data center energy consumption modeling: a survey. IEEE Commun. Surv. Tutor. 18(1), 732–794 (2016)
Guzek, M., Pecero, J.E., Dorronsoro, B., Bouvry, P.: Multi-objective evolutionary algorithms for energyaware scheduling on distributed computing systems. Appl. Soft Comput. 24, 432–446 (2014)
Iturriaga, S., Dorronsoro, B., Nesmachnow, S.: Multiobjective evolutionary algorithms for energy and service level scheduling in a federation of distributed datacenters. Int. Trans. Oper. Res. 24(1–2), 199–228 (2017)
Nesmachnow, S., Dorronsoro, B., Pecero, J.E., Bouvry, P.: Energy-aware scheduling on multicore heterogeneous grid computing systems. J. Grid Comput. 11(4), 653–680 (2013)
Iturriaga, S., Nesmachnow, S., Dorronsorro, B., Bouvry, P.: Energy efficient scheduling in heterogeneous systems with a parallel multiobjective local search. Comput. Inform. 32(2), 273–294 (2013)
Dorronsoro, B., Nesmachnow, S., Taheri, J., Zomaya, A.Y., Talbi, E.G., Bouvry, P.: A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sustain. Comput. Inform. Syst. 4(4), 252–261 (2014)
Teylo, L., de Paula, U., Frota, Y., de Oliveira, D., Drummond, L.M.: A hybrid evolutionary algorithm for task scheduling and data assignment of data-intensive scientific workflows on clouds. Future Gener. Computer Syst. 76, 1–17 (2017)
Zhou, J., Cao, K., Cong, P., Wei, T., Chen, M., Zhang, G., Yan, J., Ma, Y.: Reliability and temperature constrained task scheduling for makespan minimization on heterogeneous multi-core platforms. J. Syst. Software 133, 1–16 (2017)
Ye, X., Liu, S., Yin, Y., Jin, Y.: User-oriented many-objective cloud workflow scheduling based on an improved knee point driven evolutionary algorithm. Knowl.-Based Syst. 135, 113–124 (2017)
Konar, D., Bhattacharyya, S., Sharma, K., Sharma, S., Pradhan, S.R.: An improved hybrid quantuminspired genetic algorithm (HQIGA) for scheduling of real-time task in multiprocessor system. Appl. Soft Comput. 53, 296–307 (2017)
Mezmaz, M., Lee, Y.C., Melab, N., Talbi, E.-G., Zomaya, A.Y.: A bi-objective hybrid genetic algorithm to minimize energy consumption and makespan for precedence-constrained applications using dynamic voltage scaling. IEEE Congr. Evolution. Comput. 1, 1–8 (2010)
Sofia, A.S., GaneshKumar, P.: Multi-objective task scheduling to minimize energy consumption and makespan of cloud computing using NSGA-II. J. Network Syst. Manage. 26(2), 463–485(2018)
Ibrahim, H., Aburukba, R.O., El-Fakih, K.: An integer linear programming model and adaptive genetic algorithm approach to minimize energy consumption of cloud computing data centers. Computers Electr. Eng. 67, 551–565 (2018)
Kumar, M., Sharma, S.: Pso-cogent: cost and energy efficient scheduling in cloud environment with deadline constraint. In: Sustainable Computing: Informatics and Systems
Ramezani, F., Lu, J., Hussain, F.K.: Task-based system load balancing in cloud computing using particle swarm optimization. Int. J. Parallel Programming 42(5), 739–754 (2014)
Krishna, P.V.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13(5), 2292–2303 (2013)
Chen, H., Wang, F., Helian, N., Akanmu, G.: User-priority guided min-min scheduling algorithm for load balancing in cloud computing. In: National Conference on Parallel computing technologies (PARCOMPTECH), pp. 1–8. IEEE (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Misra, S.K., Kuila, P. (2022). Energy-Efficient Task Scheduling Using Quantum-Inspired Genetic Algorithm for Cloud Data Center. In: Gandhi, T.K., Konar, D., Sen, B., Sharma, K. (eds) Advanced Computational Paradigms and Hybrid Intelligent Computing . Advances in Intelligent Systems and Computing, vol 1373. Springer, Singapore. https://doi.org/10.1007/978-981-16-4369-9_46
Download citation
DOI: https://doi.org/10.1007/978-981-16-4369-9_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4368-2
Online ISBN: 978-981-16-4369-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)