AGWO: Cost Aware Task Scheduling in Cloud Fog Environment Using Hybrid Metaheuristic Algorithm

Santhosh Kumar Medishetty; Ganesh Reddy K

doi:10.52756/ijerr.2023.v33spl.005

Authors

Santhosh Kumar Medishetty VIT-AP University, Amaravathi, Andhra Pradesh, India https://orcid.org/0000-0001-5936-6582
Ganesh Reddy K VIT-AP University, Amaravathi, Andhra Pradesh, India

DOI:

https://doi.org/10.52756/ijerr.2023.v33spl.005

Keywords:

Internet of Things, grey wolf optimisation, ant colony optimisation, cloud-fog computing

Abstract

In IoT concepts, efficient approaches like cloud-fog computing are emerging, enhancing system benefits. The performance and output of such frameworks can be greatly improved by optimized scheduling of Internet of Things (IoT) task requests. This study presents a novel technique for scheduling Internet of Things requests in a cloud-fog environment, based on an adaption of ant grey wolf optimisation (AGWO). By combining the operators of ant colony optimisation (ACO) and grey wolf optimisation (GWO), AGWO aims to improve the speed and quality of ACO's solution discovery. The suggested AGWO approach is evaluated using numerous datasets of varying sizes, both synthetic and real-world. The effectiveness of AGWO is further investigated by comparing it to standard metaheuristic methods. The experimental results demonstrate that AGWO is superior to competing strategies in terms of makespan time and cost while resolving the task scheduling problem.

References

Abualigah, L., & Diabat, A. (2020). A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments, Cluster Comput., 24(1), 205-223. http://dx.doi.org/10.1007/s10586-020-03075-5

Abualigah, L., Elaziz, M.A., Hussien, A.G., Alsalibi, B., Jalali, S.M.J., Gandomi, A.H. (2020). Ts-gwo: Iot tasks scheduling in cloud computing using grey wolf optimizer. Swarm Intelligence for Cloud Computing. Chapman and Hall/CRC, 127-152. https://link.springer.com/article/10.1007/s10489-020-01947-2

Abualigah, L., Shehab, M., Alshinwan, M., Alabool, H., Abuaddous, H.Y., Khasawneh, A.M., & Al Diabat,M. (2020). TS-GWO: IoT tasks scheduling in cloud computing using Grey Wolf optimizer, in: Swarm Intelligence for Cloud Computing, Chapman and Hall/CRC, pp. 127–152. https://doi.org/10.1201/9780429020582-5

Ahmed, O.H., Lu, J., Xu, Q., Ahmed, A.M., Rahmani, A. M., & Hosseinzadeh, M. (2021). Using differential evolution and Moth–Flame optimization for scientific workflow scheduling in fog computing. Applied Soft Computing, 112, 107744. http://dx.doi.org/10.1016/j.asoc.2021.107744

Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M., Zivkovic, M. (2019). Task scheduling in cloud computing environment by grey wolf optimizer. IEEE, 2019, 27th telecommunications forum (TELFOR), Belgrade, Serbia, pp. 1-4. http://dx.doi.org/10.1109/TELFOR48224.2019.8971223

Chen, Z.G., Zhan, Z.H., Lin, Y., Gong, Y.J., Gu, T.L., Zhao, F., Yuan, H.Q., Chen, X., Li, Q., & Zhang, J. (2018). Multiobjective cloud workflow scheduling: A multiple populations ant colony system approach, IEEE Trans. Cybern., 49(8), 2912–2926. http://dx.doi.org/10.1109/TCYB.2018.2832640

Cheng, F., Huang, Y., Tanpure, B., Sawalani, P., Cheng, L., & Liu, C. (2022). Cost-aware job scheduling for cloud instances using deep reinforcement learning. Cluster Computing, 25(1), 619-631. https://doi.org/10.1007/s10586-021-03436-8

Elaziz, M.A., Xiong, S., Jayasena, K.P.N., & Li, L. (2019). Abd Elaziz, & Mohamed, et al. (2019) Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution. Knowledge-Based Systems, 169, 39-52. https://doi.org/10.1016/j.knosys.2019.01.023

Elaziz, M.A., Abualigah, L., Ibrahim, R. A., & Attiya, I. (2021). IoT workflow scheduling using intelligent arithmetic optimization algorithm in fog computing. Computational Intelligence and Neuroscience, 2021, 1-4. https://doi.org/10.1155/2021/9114113

Fu, J.S., Liu, Y., Chao, H.C., Bhargava, B.K., & Zhang, Z.J. (2018). Secure data storage and searching for industrial IoT by integrating fog computing and cloud computing, IEEE Trans. Ind. Inf., 14(10), 4519–4528. https://doi.org/10.1109/TII.2018.2793350

Ghasempour, A. (2019). Internet of things in smart grid: Architecture, applications, services, key technologies, and challenges, Inventions, 4(1), 22. https://doi.org/10.3390/inventions4010022

Gupta, S., & Singh, N. (2022). Fog-GMFA-DRL: Enhanced deep reinforcement learning with hybrid grey wolf and modified moth flame optimization to enhance the load balancing in the fog-IoT environment. Advances in Engineering Software, 174, 103295. https://doi.org/10.1016/j.advengsoft.2022.103295

Ghasempour, A., & Moon, T.K. (2016). Optimizing the number of collectors in machine-to-machine advanced metering infrastructure architecture for internet of things-based smart grid, IEEE Green Technologies Conference, GreenTech, pp. 51–55. http://dx.doi.org/10.1109/GreenTech.2016.17

Hussain, M., Wei, L. F., Rehman, A., Abbas, F., Hussain, A., & Ali, M. (2022). Deadline-constrained energy-aware workflow scheduling in geographically distributed cloud data centers. Future Generation Computer Systems, 132, 211-222. http://dx.doi.org/10.1016/j.future.2022.02.018

Jing, W., Zhao, C., Miao, Q., Song, H., & Chen, G. (2021). QoS-DPSO: QoS-aware task scheduling for the cloud computing system. Journal of Network and Systems Management, 29(1), 5. https://doi.org/10.1007/s10922-020-09573-6

Kaur, M., & Aron, R. (2021). Focalb: Fog computing architecture of load balancing for scientific workflow applications. Journal of Grid Computing, 19(4), 40. https://doi.org/10.1007/s10723-021-09584-w

Kumar, M. S., & Karri, G.R. (2023). EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework. Sensors, 23(5), 2445. http://dx.doi.org/10.3390/s23052445

Li, X., Zhang, G., Zheng, X., & Hua, S. (2020). Delay optimization based on improved differential evolutionary algorithm for task offloading in fog computing networks. IEEE 2020, International Conference on Wireless Communications and Signal Processing (WCSP). http://dx.doi.org/10.1109/WCSP49889.2020.9299850

Lin, B., Guo, W., Xiong, N., G. Chen, G., Vasilakos, A.V., & Zhang, H. (2016). A pretreatment workflow scheduling approach for big data applications in multicloud environments, IEEE Trans. Netw. Serv. Manag., 13(3), 581–594. http://dx.doi.org/10.1109/TNSM.2016.2554143

Liu, X.F., Zhan, Z.H., Deng, J.D., Li, Y., Gu, T., & Zhang, J. (2016). An energy efficient ant colony system for virtual machine placement in cloud computing, IEEE Trans. Evol. Comput., 22(1), 113–128. http://dx.doi.org/10.1109/TEVC.2016.2623803

Medishetti, S. K., & Karri, G. (2023). An Improved Dingo Optimization for Resource Aware Scheduling in Cloud Fog Computing Environment. Majlesi Journal of Electrical Engineering, 17(3), http://dx.doi.org/10.30486/MJEE.2023.1989335.1165

Medara, R., Singh, R. S., & Amit. (2021). Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization. Simulation Modelling Practice and Theory, 110, 102323. http://dx.doi.org/10.1016/j.simpat.2021.102323

Mohammadzadeh, A., Masdari, M., & Gharehchopogh, F. S. (2021). Energy and cost-aware workflow scheduling in cloud computing data centers using a multi-objective optimization algorithm. Journal of Network and Systems Management, 29(3), 1-34. https://link.springer.com/article/10.1007/s10922-021-09599-4

Najafizadeh, A., Salajegheh, A., Rahmani, A.M., & Sahafi, A. (2022). Multi-objective Task Scheduling in cloud-fog computing using goal programming approach. Cluster Computing, 25(1), 141-165. https://doi.org/10.1007/s10586-021-03371-8

Nguyen, B.M., Binh, H.T.T., Anh, T.T., & Son, D.B. (2019). Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud-fog computing environment, Appl. Sci., 9(9), 1730. https://doi.org/10.3390/app9091730

Saif, F.A., Latip, R., Hanapi, Z.M., Shafinah, K. (2023). Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing. IEEE Access, 11, 20635-20646. http://dx.doi.org/10.1109/ACCESS.2023.3241240

Subramoney, D., & Nyirenda, C.N. (2020). A comparative evaluation of population-based optimization algorithms for workflow scheduling in cloud-fog environments. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), http://dx.doi.org/10.1109/SSCI47803.2020.9308549

Subramoney, D., & Nyirenda, C.N. (2022). Multi-Swarm PSO Algorithm for Static Workflow Scheduling in Cloud-Fog Environments. IEEE Access, 10, 117199-117214. https://doi.org/10.1109/ACCESS.2022.3220239

Thekkepurayil, J.K.V., Suseelan, D.P., & Keerikkattil, P.M. (2022). Multi-objective Scheduling Policy for Workflow Applications in Cloud Using Hybrid Particle Search and Rescue Algorithm. Service Oriented Computing and Applications, 16(1), 45-65. https://doi.org/10.1007/s11761-021-00330-4

Vijayalakshmi, R., Vasudevan, V., Kadry, S., & Lakshmana Kumar, R. (2020). Optimization of makespan and resource utilization in the fog computing environment through task scheduling algorithm, Intl. J. Wavelets Multiresolut. Inform. Process., 18(01), 1941025. http://dx.doi.org/10.1142/S021969131941025X

Wang, S., Zhao, T., & Pang, S. (2020). Task scheduling algorithm based on improved firework algorithm in fog computing, IEEE Access, 8, 32385–32394. http://dx.doi.org/10.1109/ACCESS.2020.2973758

Wang, Y., Guo, C., & Yu, J. (2018). Immune scheduling network-based method for task scheduling in decentralized fog computing, Wirel. Commun. Mobile Comput., 33(16), e4583. http://dx.doi.org/10.1002/dac.4583

Yang, M., Ma, H., Wei, S., Zeng, Y., Chen, Y., & Hu, Y. (2020). A multi-objective task scheduling method for fog computing in cyber-physical-social services, IEEE Access, 8, 65085–65095. http://dx.doi.org/10.1109/ACCESS.2020.2983742

Yin, Z., Xu, F., Li, Y., Fan, C., Zhang, F., Han, G., Bi, Y. (2022). A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing. Sensors, 22(4), 1555. https://doi.org/10.3390/s22041555

Zhang, X., Kang, Q., Cheng, J., & Wang, X. (2018). A novel hybrid algorithm based on biogeography-based optimization and grey wolf optimizer. Applied Soft Computing 67, 197-214. https://doi.org/10.1016/j.asoc.2018.02.049

Zuo, L., Shu, L., Dong, S., Zhu, C., & Hara, T. (2015). A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing, IEEE Access, 3, 2687–2699. http://dx.doi.org/10.1109/ACCESS.2015.2508940.