W-Scheduler: whale optimization for task scheduling in cloud computing

Springer Science and Business Media LLC - Tập 22 - Trang 1087-1098 - 2017
Karnam Sreenu1, M. Sreelatha2
1Department of Computer Science and Engineering, ANU College of Engineering, Acharya Nagarjuna University, Guntur, India
2Department of Computer Science and Engineering, RVR & JC College of Engineering, Guntur, India

Tóm tắt

One of the important steps in cloud computing is the task scheduling. The task scheduling process needs to schedule the tasks to the virtual machines while reducing the makespan and the cost. Number of scheduling algorithms are proposed by various researchers for scheduling the tasks in cloud computing environments. This paper proposes the task scheduling algorithm called W-Scheduler based on the multi-objective model and the whale optimization algorithm (WOA). Initially, the multi-objective model calculates the fitness value by calculating the cost function of the central processing unit (CPU) and the memory. The fitness value is calculated by adding the makespan and the budget cost function. The proposed task scheduling algorithm with the whale optimization algorithm can optimally schedule the tasks to the virtual machines while maintaining the minimum makespan and cost. Finally, we analyze the performance of the proposed W-Scheduler with the existing methods, such as PBACO, SLPSO-SA, and SPSO-SA for the evaluation metrics makespan and cost. From the experimental results, we conclude that the proposed W-Scheduler can optimally schedule the tasks to the virtual machines while having the minimum makespan of 7 and minimum average cost of 5.8.

Tài liệu tham khảo

Mell, P., Grace, T.: The NIST definition of cloud computing. Natl. Inst. Stand. Technol. 53(6), 50 (2009) Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, Randy, Konwinski, Andy, Lee, Gunho, Patterson, David, Rabkin, Ariel, Stoica, Ion, Zaharia, Matei: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010) Hua, H.E., Guangquan, X.U., Shanchen, P.A.N.G., Zenghua, Z.H.A.O.: AMTS: adaptive multi-objective task scheduling strategy in cloud computing. China Commun. 13(4), 162–171 (2016) Lin, X., Wang, Y., Xie, Q., Pedram, M.: Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment. IEEE Trans. Serv. Comput. 8(2), 175–186 (2015) Navimipour, N.J., Rahmani, A.M., Navin, A.H., Hosseinzadeh, M.: Expert cloud: a cloud-based framework to share the knowledge and skills of human resources. Comput. Hum. Behav. 46, 57–74 (2015) Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds. Future Gener. Comput. Syst. 48, 1–18 (2015) Navimipour, N.J.: A formal approach for the specification and verification of a trustworthy human resource discovery mechanism in the expert cloud. Expert Syst. Appl. 42(15–16), 6112–6131 (2015) Keshanchi, B., Souri, A., Navimipour, N.J.: An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J. Syst. Softw. 124, 1–21 (2017) Alkhanak, E.N., Lee, S.P., Khan, S.U.R.: Cost-aware challenges for workflow scheduling approaches in cloud computing environments: taxonomy and opportunities. Future Gener. Comput. Syst. 50, 3–21 (2015) Rimal, B.P., Jukan, A., Katsaros, D., Goeleven, Y.: Architectural requirements for cloud computing systems: an enterprise cloud approach. J. Grid Comput. 9(1), 3–26 (2011) Rimal, B.P., Choi, E. and Lumb, I.: A taxonomy and survey of cloud computing systems. In: Proceedings of the Fifth International Joint Conference on IEEE, pp. 44–51 (2009) Navimipour, N.J., Rahmani, A.M., Hosseinzadehet, M.: Expert grid: new type of grid to manage the human resources and study the effectiveness of its task scheduler. Arab. J. Sci. Eng. 39(8), 6175–6188 (2014) Ullman, J.D.: NP-complete scheduling problems. J. Comput. Syst. Sci. 10(3), 384–393 (1975) Xua, Y., Li, K., He, L., Truong, T.K.: A DAG scheduling scheme on heterogeneous computing systems using double molecular structure-based chemical reaction optimization. J. Parallel Distrib. Comput. 73(9), 1306–1322 (2013) Yuming, X., Li, K., Jingtong, H., Li, K.: A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf. Sci. 270, 255–287 (2014) Khan, M.A.: Scheduling for heterogeneous systems using constrained critical paths. J. Parallel Comput. 38(4–5), 175–193 (2012) Gupta, S., Agarwal, G., and Kumar, V.: Task scheduling in multiprocessor system using genetic algorithm. In: Proceedings of Second International Conference on Machine Learning and Computing (ICMLC) (2010) Xiaolong, X., Cao, L., Wang, X.: Resource pre-allocation algorithms for low-energy task scheduling of cloud computing. J. Syst. Eng. Electron. 27(2), 457–469 (2016) Yuan, H., Bi, J., Tan, W., Li, B.H.: Temporal task scheduling with constrained service delay for profit maximization in hybrid clouds. IEEE Trans. Autom. Sci. Eng. 14(1), 337–348 (2017) Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016) Li, Y., Chen, M., Dai, W., Qiu, M.: Energy optimization with dynamic task scheduling mobile cloud computing. IEEE Syst. J. 11(1), 96–105 (2017) Zhong, Z., Chen, K., Zhai, X., Zhou, S.: Virtual machine-based task scheduling algorithm in a cloud computing environment. Tsinghua Sci. Technol. 21(6), 660–667 (2016) Cheng, C., Li, J., Wang, Y.: An energy-saving task scheduling strategy based on vacation queuing theory in cloud computing. Tsinghua Sci. Technol. 20(1), 28–39 (2015) Cui, Y.L., Liu, X., Ansari, N., Liu, Y.: Cloud service reliability modeling and optimal task scheduling Hongyan. IET Commun. 11(2), 161–167 (2017) Panda, S.K., Gupta, I., and Jana, P.K.: Task scheduling algorithms for multi-cloud systems: allocation-aware approach. Inf. Syst. Front. pp. 1–19 (2017) Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, Takahiro: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. Big Data Serv. Comput. Intell. Ind. Syst. 3, 2687–2699 (2015) Zuo, X., Zhang, G., Tan, W.: Self-adaptive learning PSO-based dead- line constrained task scheduling for hybrid IaaS cloud. IEEE Trans. Autom. Sci. Eng. 11(2), 564–573 (2014)