Abstract
With the rapid increase in the use of cloud computing systems, an efficient task scheduling policy, which deals with the assignment of tasks to resources, is required to obtain maximum performance. Cloud task scheduling (CTS) is an established NP-Hard optimization problem that can be effectively tackled with meta-heuristic algorithms. The cuckoo search (CS) algorithm is a powerful swarm-intelligence meta-heuristic that has been successfully applied over a wide-range of real-life optimization problems, including task scheduling problems. Besides its strong exploration ability, the CS algorithm suffers from insufficient local search, lack of solution diversity towards the end, and slow convergence problem. These drawbacks produce inefficient cloud task schedules resulting in sub-optimal performance. In this manuscript, an improved CS-based scheduling algorithm called CSDEO is introduced, which combines the features of the Opposition-based learning (OBL) method, Cuckoo search, and Differential evolution (DE) algorithms to optimize workload makespan and energy consumption of the cloud resources. Our CSDEO algorithm firstly uses the OBL method to produce an optimal initial population by providing solutions across the entire solution space. Then, the CSDEO uses an effective way of switching between the CS exploration phase and the DE exploitation phase, depending on each solution's fitness. Experiments are conducted on the CloudSim simulator by using the CEA-Curie and HPC2N supercomputing workloads. The observations show that in the case of CEA-Curie workloads, the proposed CSDEO algorithm achieves makespan improvement in the range of 6.29–29.76% and energy consumption improvement in the range of 3.76–201.98% over well-known scheduling algorithms. In the case of HPC2N workloads, the improvement ranges of the CSDEO approach for the makespan and energy consumption metrics are 9.86–281.69% and 6.12–233.3%, respectively compared to the tested scheduling algorithms.
Similar content being viewed by others
Data availability
All the input and output data file links are available in the manuscript.
References
Moghaddam, S.K., Buyya, R., Ramamohanarao, K.: Performance-aware management of cloud resources: a taxonomy and future directions. ACM Comput. Surv. 52, 1–37 (2019). https://doi.org/10.1145/3337956
Netto, M.A.S., Calheiros, R.N., Rodrigues, E.R., Cunha, R.L.F., Buyya, R.: HPC cloud for scientific and business applications: taxonomy, vision, and research challenges. ACM Comput. Surv. 51, 1–29 (2018). https://doi.org/10.1145/3150224
Amazon EC2 Instance Types—Amazon Web Services: https://aws.amazon.com/ec2/instance-types/ (2019). Accessed 26 June 2019
Ilager, S., Ramamohanarao, K., Buyya, R.: ETAS: Energy and thermal-aware dynamic virtual machine consolidation in cloud data center with proactive hotspot mitigation. Concurr. Comput. Pract. Exper. (2019). https://doi.org/10.1002/cpe.5221
Khattar, N., Sidhu, J., Singh, J.: Toward energy-efficient cloud computing: a survey of dynamic power management and heuristics-based optimization techniques. J Supercomput. 75, 4750–4810 (2019). https://doi.org/10.1007/s11227-019-02764-2
Brochard, L., Kamath, V., Corbalán, J., Holland, S., Mittelbach, W., Ott, M.: Energy-Efficient Computing and Data Centers. Wiley, New York (2019)
Ahmad, I., Khalil, M.I.K., Shah, S.A.A.: Optimization-based workload distribution in geographically distributed data centers: A survey. Int. J. Commun. Syst. (2020). https://doi.org/10.1002/dac.4453
Gill, S.S., Buyya, R.: A taxonomy and future directions for sustainable cloud computing: 360 degree view. ACM Comput. Surv. 51, 1–33 (2019). https://doi.org/10.1145/3241038
Lu, Y., Sun, N.: An effective task scheduling algorithm based on dynamic energy management and efficient resource utilization in green cloud computing environment. Cluster Comput. 22, 513–520 (2019). https://doi.org/10.1007/s10586-017-1272-y
Jennings, B., Stadler, R.: Resource management in clouds: survey and research challenges. J. Netw. Syst. Manage. 23, 567–619 (2015). https://doi.org/10.1007/s10922-014-9307-7
Reshmi, B., Poongodi, P.: Profit and resource availability-constrained optimal handling of high-performance scientific computing tasks. J Supercomput. 76, 4247–4261 (2020). https://doi.org/10.1007/s11227-018-2332-7
Stavrinides, G.L., Karatza, H.D.: Simulation-based performance evaluation of an energy-aware heuristic for the scheduling of HPC applications in large-scale distributed systems. In: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion (ICPE ’17) Companion, pp. 49–54. ACM Press, L’Aquila (2017)
Sukhoroslov, O., Nazarenko, A., Aleksandrov, R.: An experimental study of scheduling algorithms for many-task applications. J. Supercomput. 75, 7857–7871 (2019). https://doi.org/10.1007/s11227-018-2553-9
Prem Jacob, T., Pradeep, K.: A multi-objective optimal task scheduling in cloud environment using cuckoo particle swarm optimization. Wireless Pers Commun. 109, 315–331 (2019). https://doi.org/10.1007/s11277-019-06566-w
Mohamed, A.W., Hadi, A.A., Mohamed, A.K.: Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. Int. J. Mach. Learn. Cybern. (2019). https://doi.org/10.1007/s13042-019-01053-x
Madni, S.H.H., Abd Latiff, M.S., Abdullahi, M., Abdulhamid, S.M., Usman, M.J.: Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PLoS ONE 12, e0176321 (2017). https://doi.org/10.1371/journal.pone.0176321
Kalra, M., Singh, S.: A review of metaheuristic scheduling techniques in cloud computing. Egyp. Inf. J. 16, 275–295 (2015). https://doi.org/10.1016/j.eij.2015.07.001
Madni, S.H.H., Latiff, M.S.A., Coulibaly, Y., Abdulhamid, S.M.: Recent advancements in resource allocation techniques for cloud computing environment: a systematic review. Cluster Comput. 20, 2489–2533 (2017). https://doi.org/10.1007/s10586-016-0684-4
Kumar, M., Sharma, S.C., Goel, A., Singh, S.P.: A comprehensive survey for scheduling techniques in cloud computing. J. Netw. Comput. Appl. 143, 1–33 (2019). https://doi.org/10.1016/j.jnca.2019.06.006
Amini Motlagh, A., Movaghar, A., Rahmani, A.M.: Task scheduling mechanisms in cloud computing: a systematic review. Int. J. Commun. Syst. 33, e4302 (2020). https://doi.org/10.1002/dac.4302
Rekha, P.M., Dakshayini, M.: Efficient task allocation approach using genetic algorithm for cloud environment. Cluster Comput. 22, 1241–1251 (2019). https://doi.org/10.1007/s10586-019-02909-1
Sun, Y., Li, J., Fu, X., Wang, H., Li, H.: Application research based on improved genetic algorithm in cloud task scheduling. J. Intell. Fuzzy Syst. 38, 239–246 (2020). https://doi.org/10.3233/JIFS-179398
Shojafar, M., Javanmardi, S., Abolfazli, S., Cordeschi, N.: FUGE: a joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method. Cluster Comput. 18, 829–844 (2015). https://doi.org/10.1007/s10586-014-0420-x
Vila, S., Guirado, F., Lerida, J.L., Cores, F.: Energy-saving scheduling on IaaS HPC cloud environments based on a multi-objective genetic algorithm. J. Supercomput. 75, 1483–1495 (2019). https://doi.org/10.1007/s11227-018-2668-z
Shojafar, M., Kardgar, M., Hosseinabadi, A.A.R., Shamshirband, S., Abraham, A.: TETS: a genetic-based scheduler in cloud computing to decrease energy and makespan. In: Abraham, A., Han, S.Y., Al-Sharhan, S.A., Liu, H. (eds.) Hybrid Intelligent Systems, pp. 103–115. Springer, Cham (2016)
Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) Foundations of Fuzzy Logic and Soft Computing, pp. 789–798. Springer, Berlin (2007)
Dinesh Babu, L.D., Venkata Krishna, P.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13, 2292–2303 (2013)
Rastkhadiv, F., Kamran, Z.: Task scheduling based on load balancing using artificial bee colony in cloud computing environment. IJBR 7, 1058–1069 (2016)
Jena, R.K.: Task scheduling in cloud environment: a multi-objective ABC framework. J. Inf. Optim. Sci. 38, 1–19 (2017). https://doi.org/10.1080/02522667.2016.1250460
Li, G., Wu, Z.: Ant colony optimization task scheduling algorithm for SWIM based on load balancing. Fut. Int. 11, 90 (2019). https://doi.org/10.3390/fi11040090
Tawfeek, M.A., El-Sisi, A., Keshk, A.E., Torkey, F.A.: Cloud task scheduling based on ant colony optimization. In: 8th IEEE International Conference on Computer engineering & Systems (ICCES), pp. 64–69 (2013).
Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, T.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access. 3, 2687–2699 (2015). https://doi.org/10.1109/ACCESS.2015.2508940
Huang, X., Li, C., Chen, H., An, D.: Task scheduling in cloud computing using particle swarm optimization with time varying inertia weight strategies. Cluster Comput. 23, 1137–1147 (2020). https://doi.org/10.1007/s10586-019-02983-5
Zuo, X., Zhang, G., Tan, W.: Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Trans. Autom. Sci. Eng. 11, 564–573 (2014). https://doi.org/10.1109/TASE.2013.2272758
Zhao, G.: Cost-aware scheduling algorithm based on PSO in cloud computing environment. IJGDC 7, 33–42 (2014). https://doi.org/10.14257/ijgdc.2014.7.1.04
Beegom, A.S.A., Rajasree, M.S.: A particle swarm optimization based pareto optimal task scheduling in cloud computing. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds.) Advances in Swarm Intelligence, pp. 79–86. Springer, Cham (2014)
Kumar, M., Sharma, S.C.: PSO-COGENT: cost and energy efficient scheduling in cloud environment with deadline constraint. Sustain. Comput. Inf. Syst. 19, 147–164 (2018). https://doi.org/10.1016/j.suscom.2018.06.002
Kumar, M., Sharma, S.C.: PSO-based novel resource scheduling technique to improve QoS parameters in cloud computing. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04266-x
Abdullah, M., Al-Muta’a, E.A., Al-Sanabani, M.: Integrated MOPSO algorithms for task scheduling in cloud computing. IFS 36, 1823–1836 (2019). https://doi.org/10.3233/JIFS-181005
Zhou, Z., Li, F., Abawajy, J.H., Gao, C.: Improved PSO algorithm integrated with opposition-based learning and tentative perception in networked data centres. IEEE Access. 8, 55872–55880 (2020). https://doi.org/10.1109/ACCESS.2020.2981972
Chen, X., Long, D.: Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm. Cluster Comput. 22, 2761–2769 (2019). https://doi.org/10.1007/s10586-017-1479-y
Yang, X., Deb, S.: Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), Coimbatore, pp. 210–214 (2009)
Jafari Navimipour, N., Sharifi Milani, F.: Task scheduling in the cloud computing based on the cuckoo search algorithm. IJMO 5, 44–47 (2015). https://doi.org/10.7763/IJMO.2015.V5.434
Madni, S.H.H., Latiff, M.S.A., Ali, J., Abdulhamid, S.M.: Multi-objective-oriented cuckoo search optimization-based resource scheduling algorithm for clouds. Arab. J. Sci. Eng. 44, 3585–3602 (2019). https://doi.org/10.1007/s13369-018-3602-7
Madni, S.H.H., Abd Latiff, M.S., Abdulhamid, S.M., Ali, J.: Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment. Cluster Comput. 22, 301–334 (2019). https://doi.org/10.1007/s10586-018-2856-x
Pradeep, K., Jacob, T.P.: CGSA scheduler: a multi-objective-based hybrid approach for task scheduling in cloud environment. Inf. Security J. Glob. Perspect. 27, 77–91 (2018). https://doi.org/10.1080/19393555.2017.1407848
Natesha, B.V., Kumar Sharma, N., Domanal, S., Reddy Guddeti, R.M.: GWOTS: grey wolf optimization based task scheduling at the green cloud data center. In: 2018 14th International Conference on Semantics, Knowledge and Grids (SKG), pp. 181–187. IEEE, Guangzhou (2018)
Alzaqebah, A., Al-Sayyed, R., Masadeh, R.: Task scheduling based on modified grey wolf optimizer in cloud computing environment. In: 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS), pp. 1–6. IEEE, Amman, Jordan (2019)
Natesan, G., Chokkalingam, A.: Task scheduling in heterogeneous cloud environment using mean grey wolf optimization algorithm. ICT Express. 5, 110–114 (2019). https://doi.org/10.1016/j.icte.2018.07.002
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016). https://doi.org/10.1016/j.advengsoft.2016.01.008
Narendrababu Reddy, G., Kumar, S.P.: Multi objective task scheduling algorithm for cloud computing using whale optimization technique. In: Bhattacharyya, P., Sastry, H.G., Marriboyina, V., Sharma, R. (eds.) Smart and Innovative Trends in Next Generation Computing Technologies, pp. 286–297. Springer, Singapore (2018)
Sharma, M., Garg, R.: Energy-aware whale-optmized task scheduler in cloud computing. In: 2017 International Conference on Intelligent Sustainable Systems (ICISS), pp. 121–126. IEEE, Palladam (2017)
Sreenu, K., Sreelatha, M.: W-Scheduler: whale optimization for task scheduling in cloud computing. Cluster Comput. 22, 1087–1098 (2019). https://doi.org/10.1007/s10586-017-1055-5
Milan, S.T., Rajabion, L., Darwesh, A., Hosseinzadeh, M., Navimipour, N.J.: Priority-based task scheduling method over cloudlet using a swarm intelligence algorithm. Cluster Comput. 23, 663–671 (2020). https://doi.org/10.1007/s10586-019-02951-z
Nasr, A.A., Chronopoulos, A.T., El-Bahnasawy, N.A., Attiya, G., El-Sayed, A.: A novel water pressure change optimization technique for solving scheduling problem in cloud computing. Cluster Comput. 22, 601–617 (2019). https://doi.org/10.1007/s10586-018-2867-7
Praveen, S.P., Rao, K.T., Janakiramaiah, B.: Effective allocation of resources and task scheduling in cloud environment using social group optimization. Arab. J. Sci. Eng. 43, 4265–4272 (2018). https://doi.org/10.1007/s13369-017-2926-z
Haris, M., Khan, R.Z.: A systematic review on load balancing issues in cloud computing. In: Karrupusamy, P., Chen, J., Shi, Y. (eds.) Sustainable Communication Networks and Application, pp. 297–303. Springer, Cham (2020)
Agarwal, M., Srivastava, G.M.S.: Genetic algorithm-enabled particle swarm optimization (PSOGA)-based task scheduling in cloud computing environment. Int. J. Inf. Technol. Decis. Mak. 17, 1237–1267 (2018). https://doi.org/10.1142/S0219622018500244
Elaziz, M.A., Xiong, S., Jayasena, K.P.N., Li, L.: Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution. Knowl. Based Syst. 169, 39–52 (2019). https://doi.org/10.1016/j.knosys.2019.01.023
Gill, S.S., Chana, I., Singh, M., Buyya, R.: CHOPPER: an intelligent QoS-aware autonomic resource management approach for cloud computing. Cluster Comput. 21, 1203–1241 (2018). https://doi.org/10.1007/s10586-017-1040-z
Gill, S.S., Buyya, R., Chana, I., Singh, M., Abraham, A.: 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
Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06), pp. 695–701. IEEE, Vienna (2005)
Chi, R., Su, Y., Qu, Z., Chi, X.: A hybridization of cuckoo search and differential evolution for the logistics distribution center location problem. Math. Probl. Eng. 2019, 1–16 (2019). https://doi.org/10.1155/2019/7051248
Eltaeib, T., Mahmood, A.: Differential evolution: a survey and analysis. Appl. Sci. 8, 1945 (2018). https://doi.org/10.3390/app8101945
Rivera-Lopez, R., Canul-Reich, J.: Differential evolution algorithm in the construction of interpretable classification models. In: Aceves-Fernandez, M.A. (ed.) Artificial Intelligence—Emerging Trends and Applications. InTech, Rijeka (2018)
Fatih Tasgetiren, M., Liang, Y.-C., Sevkli, M., Gencyilmaz, G.: Particle swarm optimization and differential evolution for the single machine total weighted tardiness problem. Int. J. Prod. Res. 44, 4737–4754 (2006). https://doi.org/10.1080/00207540600620849
Gabaldon, E., Lerida, J.L., Guirado, F., Planes, J.: Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments. J Supercomput. 73, 354–369 (2017). https://doi.org/10.1007/s11227-016-1866-9
Srichandan, S., Ashok Kumar, T., Bibhudatta, S.: Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm. Future Computing and Informatics Journal. 3, 210–230 (2018). https://doi.org/10.1016/j.fcij.2018.03.004
jMetal 5 Web site: https://jmetal.github.io/jMetal/. Accessed July 2019.
Nebro, A.J., Durillo, J.J., Vergne, M.: Redesigning the jMetal multi-objective optimization framework. In: Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference—GECCO Companion ’15, pp. 1093–1100. ACM Press, Madrid (2015)
Rathor, V.S., Pateriya, R.K., Gupta, R.K.: An efficient virtual machine scheduling technique in cloud computing environment. IJCS 1, 1–14 (2014). https://doi.org/10.14257/ijcs.2014.1.1.01
Romeijn, H.E.: Random search methods. In: Floudas, C.A., Pardalos, P.M. (eds.) Encyclopedia of Optimization, pp. 3245–3251. Springer, Boston (2009)
Wei, J., Zeng, X.: Optimal computing resource allocation algorithm in cloud computing based on hybrid differential parallel scheduling. Cluster Comput. 22, 7577–7583 (2019). https://doi.org/10.1007/s10586-018-2138-7
Funding
This research received no external funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare no conflict of interest.
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
Chhabra, A., Singh, G. & Kahlon, K.S. Multi-criteria HPC task scheduling on IaaS cloud infrastructures using meta-heuristics. Cluster Comput 24, 885–918 (2021). https://doi.org/10.1007/s10586-020-03168-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-020-03168-1