Abstract
Cloud resource scheduling requires mapping of cloud resources to cloud workloads. Scheduling results can be optimized by considering Quality of Service (QoS) parameters as inherent requirements of scheduling. In existing literature, only a few resource scheduling algorithms have considered cost and execution time constraints but efficient scheduling requires better optimization of QoS parameters. The main aim of this research paper is to present an efficient strategy for execution of workloads on cloud resources. A particle swarm optimization based resource scheduling technique has been designed named as BULLET which is used to execute workloads effectively on available resources. Performance of the proposed technique has been evaluated in cloud environment. The experimental results show that the proposed technique efficiently reduces execution cost, time and energy consumption along with other QoS parameters.
Similar content being viewed by others
References
Moens, H., Truyen, E., Walraven, S., Joosen, W., Dhoedt, B., De Turck, F.: Cost-effective feature placement of customizable multi-tenant applications in the cloud. J. Netw. Syst. Manag. 22(4), 517–558 (2014)
Singh, S., Chana, I.: QoS-aware autonomic resource management in cloud computing: a systematic review. ACM Comput. Surv. 48(3), 1–46 (2015)
Singh, S., Chana, I., Singh, M.: The journey of QoS based autonomic cloud computing. IT Prof. Mag. 19(2), 42–49 (2017)
Singh, S., Chana, I.: Resource provisioning and scheduling in clouds: QoS perspective. J. Supercomput. 72(3), 926–960 (2016)
Chiang, M.L.: Efficient diagnosis protocol to enhance the reliability of a cloud computing environment. J. Netw. Syst. Manag. 20(4), 579–600 (2012)
Calheiros, R.N., Ranjan, R., Beloglazov, A., Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Soft: Pract. Exper. 41(1), 23–50 (2011)
Singh, S., Chana, I.: Q-aware: quality of service based cloud resource provisioning. Comput. Electr. Eng. 47, 138–160 (2015)
Singh, S., Chana, I.: QRSF: QoS-aware resource scheduling framework in cloud computing. J. Supercomput. 71(1), 241–292 (2015)
Singh, S., Chana, I., Buyya, R.: STAR: SLA-aware autonomic management of cloud resources. IEEE Trans. Cloud Comput. (2017). doi:10.1109/TCC.2017.2648788
Daniel, L.A., Madeira, E., Medhi, D.: On makespan, migrations, and QoS workloads’ execution times in high speed data centers. IEICE Trans. Commun. 98(11), 2099–2110 (2015)
Lago, D., Madeira, E., Medhi, D.: High speed network impacts and power consumption estimation for cloud data centers. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 615–620. ACM (2015)
Varalakshmi, P., Ramaswamy, A., Balasubramanian, A., Vijaykumar, P.: An optimal workflow based scheduling and resource allocation in cloud. In: Advances in Computing and Communications, pp. 411–420. Springer, Berlin (2011)
Xing, L.-N., Chen, Y.-W., Wang, Pe, Zhao, Q.-S., Xiong, J.: A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl. Soft Comput. 10(3), 888–896 (2010)
Topcuoglu, H., Hariri, S., Wu, M.-Y.: Task scheduling algorithms for heterogeneous processors. In: Heterogeneous Computing Workshop (HCW’99). San Juan (1999)
El-kenawy, E.-S.T., El-Desoky, A.I., Al-rahamawy, M.F.: Extended max-min scheduling using petri net and load balancing. Int. J. Soft Comput. Eng. (IJSCE) 2(4), 198–203 (2012)
Liu, K., Jin, H., Chen, J., Liu, X., Yuan, D., Yang, Y.: A compromised-time-cost scheduling algorithm in swindew-c for instance-intensive cost-constrained workflows on a cloud computing platform. Int. J. High Perform. Comput. Appl. 24(4), 445–456 (2010)
Verma, A., Kaushal, S.: Deadline and budget distribution based cost-time optimization workflow scheduling algorithm for cloud. In: IJCA Proceedings on International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012) (2012)
Pandey, S., Wu, L., Guru, S., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: Advanced Information Networking and Applications (AINA), 24th IEEE International Conference, Perth (2010)
Somasundaram, T.S., Govindarajan, K.: CLOUDRB: A framework for scheduling and managing High-Performance Computing (HPC) applications in science cloud. Future Generation Computer Systems 34, 47–65 (2014)
Netjinda, N., Sirinaovakul, B., Achalakul, T.: Cost optimal scheduling in IaaS for dependent workload with particle swarm optimization. J. Supercomput. 68(3), 1579–1603 (2014)
Yassa, S., Chelouah, R., Kadima, H., Granado, B.: Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. Sci. World J. (2013). doi:10.1155/2013/350934
Singh, S., Chana, I., Singh, M., Buyya, R.: SOCCER: self-optimization of energy-efficient cloud resources. Cluster Comput. 19(4), 1787–1800 (2016)
Chen, G., Yu, J.: Particle swarm optimization algorithm. Inf. Control-Shenyang 34(3), 318 (2005)
Acknowledgements
One of the authors, Dr. Sukhpal Singh Gill (Post Doctorate Fellow), gratefully acknowledges the Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Australia, for awarding him the Fellowship to carry out this research work. This research work is partially supported by Melbourne-Chindia Cloud Computing Research Network funded by the University of Melbourne.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gill, S.S., Buyya, R., Chana, I. et al. BULLET: Particle Swarm Optimization Based Scheduling Technique for Provisioned Cloud Resources. J Netw Syst Manage 26, 361–400 (2018). https://doi.org/10.1007/s10922-017-9419-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10922-017-9419-y