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Published in: Arabian Journal for Science and Engineering 3/2024

29-11-2023 | Research Article-Computer Engineering and Computer Science

DE-GWO: A Multi-objective Workflow Scheduling Algorithm for Heterogeneous Fog-Cloud Environment

Authors: Prashant Shukla, Sudhakar Pandey

Published in: Arabian Journal for Science and Engineering | Issue 3/2024

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Abstract

The demand for a quick response from cloud services is rapidly increasing day-by-day. Fog computing is a trending solution to fulfil the demands. When integrated with the cloud, this technology can tremendously improve the performance. Like any other technology, Fog also has the shortcoming of limited resources. The difficulty of efficient scheduling of tasks among limited resources to minimize makespan and energy consumption, while still guaranteeing appropriate execution cost, continues to be a significant issue for research. Hence, this study introduces a Differential Evolution-Grey Wolf Optimization (DE-GWO) technique to enhance the scheduling of scientific workflows under cloud-fog settings. The objective of the proposed DE-GWO algorithm is to mitigate the issue of slow convergence and low accuracy that is often seen in the classical GWO algorithm. The DE method is chosen as the evolutionary pattern of wolves to speed up convergence and enhance GWO’s accuracy. This study further formulates a weighted sum based objective function which incorporates three criteria, namely makespan, cost and energy consumption. In this study, the DE-GWO technique is evaluated and compared with many conventional and hybrid optimization algorithms. The simulations use five scientific workflows datasets which includes Montage, Cybershake, Epigenomics, LIGO and SIPHT. The DE-GWO algorithm demonstrates superior performance compared to all conventional algorithms across several scientific workflows and performance criteria. The methodology has a commendable level of competitiveness when compared to other methods, since DE incorporates evolution and elimination mechanisms in GWO and GWO retains a good balance between exploration and exploitation.

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Literature
1.
go back to reference Rodriguez, M.A.; Buyya, R.: A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments. Concurren. Comput.: Pract. Exp 29(8), e4041 (2017)CrossRef Rodriguez, M.A.; Buyya, R.: A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments. Concurren. Comput.: Pract. Exp 29(8), e4041 (2017)CrossRef
2.
go back to reference Miller, M.A.; Pfeiffer, W. and Schwartz, T., (2011) The CIPRES science gateway: a community resource for phylogenetic analyzes. In: Proceedings of the 2011 TeraGrid Conference: extreme digital discovery (pp. 1–8) Miller, M.A.; Pfeiffer, W. and Schwartz, T., (2011) The CIPRES science gateway: a community resource for phylogenetic analyzes. In: Proceedings of the 2011 TeraGrid Conference: extreme digital discovery (pp. 1–8)
3.
go back to reference Jha, S.; Lathrop, S.; Nabrzyski, J.; Ramakrishnan, L.: Incorporating scientific workflows in computing research processes. Comput. Sci. Eng. 21(4), 4–6 (2019)CrossRef Jha, S.; Lathrop, S.; Nabrzyski, J.; Ramakrishnan, L.: Incorporating scientific workflows in computing research processes. Comput. Sci. Eng. 21(4), 4–6 (2019)CrossRef
4.
go back to reference Yu, J.; Buyya, R.: A taxonomy of workflow management systems for grid computing. J. Grid Comput. 3, 171–200 (2005)CrossRef Yu, J.; Buyya, R.: A taxonomy of workflow management systems for grid computing. J. Grid Comput. 3, 171–200 (2005)CrossRef
5.
go back to reference Duan, R.; Prodan, R.; Li, X.: Multi-objective game theoretic schedulingof bag-of-tasks workflows on hybrid clouds. IEEE Trans. Cloud Comput. 2(1), 29–42 (2014)CrossRef Duan, R.; Prodan, R.; Li, X.: Multi-objective game theoretic schedulingof bag-of-tasks workflows on hybrid clouds. IEEE Trans. Cloud Comput. 2(1), 29–42 (2014)CrossRef
6.
go back to reference Zhao, Y.; Li, Y.; Raicu, I.; Lu, S.; Lin, C.; Zhang, Y.; Tian, W.; Xue, R.: A service framework for scientific workflow management in the cloud. IEEE Trans. Serv. Comput. 8(6), 930–944 (2014)CrossRef Zhao, Y.; Li, Y.; Raicu, I.; Lu, S.; Lin, C.; Zhang, Y.; Tian, W.; Xue, R.: A service framework for scientific workflow management in the cloud. IEEE Trans. Serv. Comput. 8(6), 930–944 (2014)CrossRef
7.
go back to reference Song, W.; Chen, F.; Jacobsen, H.A.; Xia, X.; Ye, C.; Ma, X.: Scientific workflow mining in clouds. IEEE Trans. Parallel Distrib. Syst. 28(10), 2979–2992 (2017)CrossRef Song, W.; Chen, F.; Jacobsen, H.A.; Xia, X.; Ye, C.; Ma, X.: Scientific workflow mining in clouds. IEEE Trans. Parallel Distrib. Syst. 28(10), 2979–2992 (2017)CrossRef
8.
go back to reference Buyya, R.; Pandey, S; and Vecchiola, C.; (2009). Cloudbus toolkit for market-oriented cloud computing. In: Cloud Computing: First International Conference, CloudCom 2009, Beijing, China. Springer Berlin Heidelberg Buyya, R.; Pandey, S; and Vecchiola, C.; (2009). Cloudbus toolkit for market-oriented cloud computing. In: Cloud Computing: First International Conference, CloudCom 2009, Beijing, China. Springer Berlin Heidelberg
9.
go back to reference Masdari, M.; Salehi, F.; Jalali, M.; Bidaki, M.: A survey of PSO-based scheduling algorithms in cloud computing. J. Netw. Syst. Manage. 25(1), 122–158 (2017)CrossRef Masdari, M.; Salehi, F.; Jalali, M.; Bidaki, M.: A survey of PSO-based scheduling algorithms in cloud computing. J. Netw. Syst. Manage. 25(1), 122–158 (2017)CrossRef
10.
go back to reference Mahmud, R.; Kotagiri, R; and Buyya, R.; (2018). Fog computing: A taxonomy, survey and future directions. Internet of Everything: Algorithms, Methodologies, Technologies and Perspectives, pp.103–130, Mahmud, R.; Kotagiri, R; and Buyya, R.; (2018). Fog computing: A taxonomy, survey and future directions. Internet of Everything: Algorithms, Methodologies, Technologies and Perspectives, pp.103–130,
11.
go back to reference Shukla, P.; Pandey, S.; Hatwar, P; and Pant, A., (2023). FAT-ETO: Fuzzy-AHP-TOPSIS-based efficient task offloading algorithm for scientific workflows in heterogeneous fog–cloud environment. In: Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, pp.1–15, Shukla, P.; Pandey, S.; Hatwar, P; and Pant, A., (2023). FAT-ETO: Fuzzy-AHP-TOPSIS-based efficient task offloading algorithm for scientific workflows in heterogeneous fog–cloud environment. In: Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, pp.1–15,
12.
go back to reference Shukla, P.; Pandey, S.: MAA: multi-objective artificial algae algorithm for workflow scheduling in heterogeneous fog-cloud environment. J. Supercomput. 79(10), 11218–11260 (2023)CrossRef Shukla, P.; Pandey, S.: MAA: multi-objective artificial algae algorithm for workflow scheduling in heterogeneous fog-cloud environment. J. Supercomput. 79(10), 11218–11260 (2023)CrossRef
13.
go back to reference Verma, A.; Kaushal, S.: A hybrid multi-objective particle swarm optimization for scientific workflow scheduling. Parallel Comput. 62, 1–19 (2017)MathSciNetCrossRef Verma, A.; Kaushal, S.: A hybrid multi-objective particle swarm optimization for scientific workflow scheduling. Parallel Comput. 62, 1–19 (2017)MathSciNetCrossRef
14.
go back to reference Xie, Y.; Zhu, Y.; Wang, Y.; Cheng, Y.; Xu, R.; Sani, A.S.; Yuan, D.; Yang, Y.: A novel directional and non-local-convergent particle swarm optimization-based workflow scheduling in cloud–edge environment. Futur. Gener. Comput. Syst. 97, 361–378 (2019)CrossRef Xie, Y.; Zhu, Y.; Wang, Y.; Cheng, Y.; Xu, R.; Sani, A.S.; Yuan, D.; Yang, Y.: A novel directional and non-local-convergent particle swarm optimization-based workflow scheduling in cloud–edge environment. Futur. Gener. Comput. Syst. 97, 361–378 (2019)CrossRef
15.
go back to reference Kothyari Y.; Singh, A.; (2018) A multi-objective workflow scheduling algorithm for cloud environment. In: 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU) (pp. 1–6). IEEE. Kothyari Y.; Singh, A.; (2018) A multi-objective workflow scheduling algorithm for cloud environment. In: 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU) (pp. 1–6). IEEE.
16.
go back to reference Yassa, S.; Chelouah, R.; Kadima, H.; Granado, B.: Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. Sci. World J. 2013, 350934 (2013)CrossRef Yassa, S.; Chelouah, R.; Kadima, H.; Granado, B.: Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. Sci. World J. 2013, 350934 (2013)CrossRef
17.
go back to reference Manasrah, A.M.; Ba Ali, H.: Workflow scheduling using hybrid GA-PSO algorithm in cloud computing. Wireless Commun. Mob. Comput. 2018, 1–16 (2018)CrossRef Manasrah, A.M.; Ba Ali, H.: Workflow scheduling using hybrid GA-PSO algorithm in cloud computing. Wireless Commun. Mob. Comput. 2018, 1–16 (2018)CrossRef
18.
go back to reference Farid, M.; Latip, R.; Hussin, M.; Abdul Hamid, N.A.W.: A survey on QoS requirements based on particle swarm optimization scheduling techniques for workflow scheduling in cloud computing. Symmetry 12(4), 551 (2020)ADSCrossRef Farid, M.; Latip, R.; Hussin, M.; Abdul Hamid, N.A.W.: A survey on QoS requirements based on particle swarm optimization scheduling techniques for workflow scheduling in cloud computing. Symmetry 12(4), 551 (2020)ADSCrossRef
19.
go back to reference Subramoney, D.; Nyirenda, C.N.: Multi-swarm PSO algorithm for static workflow scheduling in cloud-fog environments. IEEE Access 10, 117199–117214 (2022)CrossRef Subramoney, D.; Nyirenda, C.N.: Multi-swarm PSO algorithm for static workflow scheduling in cloud-fog environments. IEEE Access 10, 117199–117214 (2022)CrossRef
20.
go back to reference Parpinelli, R.S.; Lopes, H.S.: New inspirations in swarm intelligence: a survey. Int. J. Bio-Insp. Comput. 3(1), 1–16 (2011)CrossRef Parpinelli, R.S.; Lopes, H.S.: New inspirations in swarm intelligence: a survey. Int. J. Bio-Insp. Comput. 3(1), 1–16 (2011)CrossRef
21.
go back to reference Črepinšek, M.; Liu, S.H.; Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 1–33 (2013)CrossRef Črepinšek, M.; Liu, S.H.; Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 1–33 (2013)CrossRef
22.
go back to reference De Maio, V.; Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Futur. Gener. Comput. Syst. 106, 171–184 (2020)CrossRef De Maio, V.; Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in fog. Futur. Gener. Comput. Syst. 106, 171–184 (2020)CrossRef
23.
go back to reference Nyirenda, C.N.; Dawoud, D.S.; Dong, F.; Negnevitsky, M.; Hirota, K.: A fuzzy multi-objective particle swarm optimized TS fuzzy logic congestion controller for wireless local area networks. J. Adv. Comput. Int. Int. Inf. 15(1), 41–54 (2011) Nyirenda, C.N.; Dawoud, D.S.; Dong, F.; Negnevitsky, M.; Hirota, K.: A fuzzy multi-objective particle swarm optimized TS fuzzy logic congestion controller for wireless local area networks. J. Adv. Comput. Int. Int. Inf. 15(1), 41–54 (2011)
24.
go back to reference Marler, R.T.; Arora, J.S.: The weighted sum method for multi-objective optimization: new insights. Struct. Multidiscip. Optim. 41, 853–862 (2010)MathSciNetCrossRef Marler, R.T.; Arora, J.S.: The weighted sum method for multi-objective optimization: new insights. Struct. Multidiscip. Optim. 41, 853–862 (2010)MathSciNetCrossRef
25.
go back to reference Chen, W; Deelman, E., (2012) Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th international conference on E-science (pp. 1–8). IEEE. Chen, W; Deelman, E., (2012) Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th international conference on E-science (pp. 1–8). IEEE.
26.
go back to reference Liu, X.; Fan, L.; Xu, J.; Li, X.; Gong, L.; Grundy, J; and Yang, Y., (2019) FogWorkflowSim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 1114–1117). IEEE. Liu, X.; Fan, L.; Xu, J.; Li, X.; Gong, L.; Grundy, J; and Yang, Y., (2019) FogWorkflowSim: An automated simulation toolkit for workflow performance evaluation in fog computing. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 1114–1117). IEEE.
27.
go back to reference Ghaleb, M.; Azzedin, F.: Toward scalable and efficient architecture for modeling trust in iot environments. Sensors 21(9), 2986 (2021)ADSCrossRef Ghaleb, M.; Azzedin, F.: Toward scalable and efficient architecture for modeling trust in iot environments. Sensors 21(9), 2986 (2021)ADSCrossRef
28.
go back to reference Confais, B.; Lebre, A.; Parrein, B.: A fog storage software architecture for the internet of things. Adv. Edge Comput.: Massive Parallel Process. Appl. 35, 61 (2020) Confais, B.; Lebre, A.; Parrein, B.: A fog storage software architecture for the internet of things. Adv. Edge Comput.: Massive Parallel Process. Appl. 35, 61 (2020)
29.
go back to reference Singh RM; Awasthi LK; Sikka G; (2020).Techniques for task scheduling in cloud and fog environment: a survey. In: Futuristic Trends in Networks and Computing Technologies: Second International Conference, FTNCT 2019, Chandigarh, India. Springer Singapore. Singh RM; Awasthi LK; Sikka G; (2020).Techniques for task scheduling in cloud and fog environment: a survey. In: Futuristic Trends in Networks and Computing Technologies: Second International Conference, FTNCT 2019, Chandigarh, India. Springer Singapore.
30.
go back to reference Bitam, S.; Zeadally, S.; Mellouk, A.: Fog computing job scheduling optimization based on bees swarm. Enterprise Inf. Syst. 12(4), 373–397 (2018)ADSCrossRef Bitam, S.; Zeadally, S.; Mellouk, A.: Fog computing job scheduling optimization based on bees swarm. Enterprise Inf. Syst. 12(4), 373–397 (2018)ADSCrossRef
31.
go back to reference Rafique, H.; Shah, M.A.; Islam, S.U.; Maqsood, T.; Khan, S.; Maple, C.: A novel bio-inspired hybrid algorithm (NBIHA) for efficient resource management in fog computing. IEEE Access 7, 115760–115773 (2019)CrossRef Rafique, H.; Shah, M.A.; Islam, S.U.; Maqsood, T.; Khan, S.; Maple, C.: A novel bio-inspired hybrid algorithm (NBIHA) for efficient resource management in fog computing. IEEE Access 7, 115760–115773 (2019)CrossRef
32.
go back to reference Ghobaei-Arani, M.; Souri, A.; Safara, F.; Norouzi, M.: An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Trans. Emerg. Telecommun. Technol. 31(2), e3770 (2020)CrossRef Ghobaei-Arani, M.; Souri, A.; Safara, F.; Norouzi, M.: An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Trans. Emerg. Telecommun. Technol. 31(2), e3770 (2020)CrossRef
33.
go back to reference Wang, J.; Li, D.: Task scheduling based on a hybrid heuristic algorithm for smart production line with fog computing. Sensors 19(5), 1023 (2019)ADSCrossRef Wang, J.; Li, D.: Task scheduling based on a hybrid heuristic algorithm for smart production line with fog computing. Sensors 19(5), 1023 (2019)ADSCrossRef
34.
go back to reference Xu, J.; Hao, Z.; Zhang, R.; Sun, X.: A method based on the combination of laxity and ant colony system for cloud-fog task scheduling. IEEE Access 7, 116218–116226 (2019)CrossRef Xu, J.; Hao, Z.; Zhang, R.; Sun, X.: A method based on the combination of laxity and ant colony system for cloud-fog task scheduling. IEEE Access 7, 116218–116226 (2019)CrossRef
35.
go back to reference Li, G.; Liu, Y.; Wu, J.; Lin, D.; Zhao, S.: Methods of resource scheduling based on optimized fuzzy clustering in fog computing. Sensors 19(9), 2122 (2019)ADSCrossRef Li, G.; Liu, Y.; Wu, J.; Lin, D.; Zhao, S.: Methods of resource scheduling based on optimized fuzzy clustering in fog computing. Sensors 19(9), 2122 (2019)ADSCrossRef
36.
go back to reference Wu, C.G.; Li, W.; Wang, L.; Zomaya, A.Y.: Hybrid evolutionary scheduling for energy-efficient fog-enhanced internet of things. IEEE Trans. Cloud Comput. 9(2), 641–653 (2018)CrossRef Wu, C.G.; Li, W.; Wang, L.; Zomaya, A.Y.: Hybrid evolutionary scheduling for energy-efficient fog-enhanced internet of things. IEEE Trans. Cloud Comput. 9(2), 641–653 (2018)CrossRef
37.
go back to reference Nguyen, B.M.; Binh, H.T.T.; Anh, T.T.; Son, D.B.: 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 (2019)CrossRef Nguyen, B.M.; Binh, H.T.T.; Anh, T.T.; Son, D.B.: 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 (2019)CrossRef
38.
go back to reference Stavrinides, G.L.; Karatza, H.D.: A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments. Multimed. Tools Appl. 78, 24639–24655 (2019)CrossRef Stavrinides, G.L.; Karatza, H.D.: A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments. Multimed. Tools Appl. 78, 24639–24655 (2019)CrossRef
39.
go back to reference Ghaffari, E. (2019) Providing a new scheduling method in fog network using the ant colony algorithm, Collection of Articles on Computer Science Ghaffari, E. (2019) Providing a new scheduling method in fog network using the ant colony algorithm, Collection of Articles on Computer Science
40.
go back to reference Li, G.; Yan, J.; Chen, L.; Wu, J.; Lin, Q.; Zhang, Y.: Energy consumption optimization with a delay threshold in cloud-fog cooperation computing. IEEE Access 7, 159688–159697 (2019)CrossRef Li, G.; Yan, J.; Chen, L.; Wu, J.; Lin, Q.; Zhang, Y.: Energy consumption optimization with a delay threshold in cloud-fog cooperation computing. IEEE Access 7, 159688–159697 (2019)CrossRef
41.
go back to reference Rahbari, D; Nickray, M., (2017) Scheduling of fog networks with optimized knapsack by symbiotic organisms search. In: 2017 21st Conference of Open Innovations Association (FRUCT) (pp. 278–283). IEEE. Rahbari, D; Nickray, M., (2017) Scheduling of fog networks with optimized knapsack by symbiotic organisms search. In: 2017 21st Conference of Open Innovations Association (FRUCT) (pp. 278–283). IEEE.
42.
go back to reference Pham, X.Q; Huh, E.N., (2016) Toward task scheduling in a cloud-fog computing system. In: 2016 18th Asia-Pacific network operations and management symposium (APNOMS) (pp. 1–4). IEEE. Pham, X.Q; Huh, E.N., (2016) Toward task scheduling in a cloud-fog computing system. In: 2016 18th Asia-Pacific network operations and management symposium (APNOMS) (pp. 1–4). IEEE.
43.
go back to reference Agarwal, S.; Yadav, S.; Yadav, A.K.: An efficient architecture and algorithm for resource provisioning in fog computing. Int. J. Inf. Eng. Electron Bus. 8(1), 48 (2016) Agarwal, S.; Yadav, S.; Yadav, A.K.: An efficient architecture and algorithm for resource provisioning in fog computing. Int. J. Inf. Eng. Electron Bus. 8(1), 48 (2016)
44.
go back to reference Rahbari, D.; Nickray, M.: Low-latency and energy-efficient scheduling in fog-based IoT applications. Turk. J. Electr. Eng. Comput. Sci. 27(2), 1406–1427 (2019)CrossRef Rahbari, D.; Nickray, M.: Low-latency and energy-efficient scheduling in fog-based IoT applications. Turk. J. Electr. Eng. Comput. Sci. 27(2), 1406–1427 (2019)CrossRef
45.
go back to reference Ghenai, A.; Kabouche, Y; Dahmani, W., (2018) Multi-user dynamic scheduling-based resource management for Internet of Things applications. In: 2018 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC) (pp. 126–131). IEEE. Ghenai, A.; Kabouche, Y; Dahmani, W., (2018) Multi-user dynamic scheduling-based resource management for Internet of Things applications. In: 2018 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC) (pp. 126–131). IEEE.
46.
go back to reference Xu, R.; Wang, Y.; Cheng, Y.; Zhu, Y.; Xie, Y.; Sani, A.S; Yuan, D., (2019). Improved particle swarm optimization-based workflow scheduling in cloud-fog environment. In Business Process Management Workshops: BPM 2018 International Workshops, Sydney, NSW, Australia, Springer International Publishing. Xu, R.; Wang, Y.; Cheng, Y.; Zhu, Y.; Xie, Y.; Sani, A.S; Yuan, D., (2019). Improved particle swarm optimization-based workflow scheduling in cloud-fog environment. In Business Process Management Workshops: BPM 2018 International Workshops, Sydney, NSW, Australia, Springer International Publishing.
47.
go back to reference Kabirzadeh, S.; Rahbari, D; Nickray, M.; (2017) A hyper heuristic algorithm for scheduling of fog networks. In: 2017 21st Conference of Open Innovations Association (FRUCT) (pp. 148–155). IEEE. Kabirzadeh, S.; Rahbari, D; Nickray, M.; (2017) A hyper heuristic algorithm for scheduling of fog networks. In: 2017 21st Conference of Open Innovations Association (FRUCT) (pp. 148–155). IEEE.
48.
go back to reference Bezdan, T.; Zivkovic, M.; Bacanin, N.; Strumberger, I.; Tuba, E.; Tuba, M.: Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm. J. Int. Fuzzy Syst. 42(1), 411–423 (2022) Bezdan, T.; Zivkovic, M.; Bacanin, N.; Strumberger, I.; Tuba, E.; Tuba, M.: Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm. J. Int. Fuzzy Syst. 42(1), 411–423 (2022)
49.
go back to reference Bezdan, T.; Zivkovic, M.; Antonijevic, M.; Zivkovic, T.; Bacanin, N.: Enhanced Flower Pollination Algorithm for Task Scheduling in Cloud Computing Environment. In: Joshi, A.; Khosravy, M.; Gupta, N. (Eds.) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, Springer, Singapore (2021) Bezdan, T.; Zivkovic, M.; Antonijevic, M.; Zivkovic, T.; Bacanin, N.: Enhanced Flower Pollination Algorithm for Task Scheduling in Cloud Computing Environment. In: Joshi, A.; Khosravy, M.; Gupta, N. (Eds.) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, Springer, Singapore (2021)
50.
go back to reference Rahbari, D.; Kabirzadeh, S; Nickray, M., (2017) A security aware scheduling in fog computing by hyper heuristic algorithm. In: 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS) (pp. 87–92). IEEE. Rahbari, D.; Kabirzadeh, S; Nickray, M., (2017) A security aware scheduling in fog computing by hyper heuristic algorithm. In: 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS) (pp. 87–92). IEEE.
51.
go back to reference Bian, S.; Huang, X; Shao, Z., (2019) Online task scheduling for fog computing with multi-resource fairness. In: 2019 IEEE 90th vehicular technology conference (VTC2019-Fall) (pp. 1–5). IEEE. Bian, S.; Huang, X; Shao, Z., (2019) Online task scheduling for fog computing with multi-resource fairness. In: 2019 IEEE 90th vehicular technology conference (VTC2019-Fall) (pp. 1–5). IEEE.
52.
go back to reference Storn, R.; Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)MathSciNetCrossRef Storn, R.; Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)MathSciNetCrossRef
53.
go back to reference Mirjalili, S.; Mirjalili, S.M.; Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRef Mirjalili, S.; Mirjalili, S.M.; Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRef
54.
go back to reference Wang, J.S.; Li, S.X.: An improved grey wolf optimizer based on differential evolution and elimination mechanism. Sci. Rep. 9(1), 7181 (2019)ADSCrossRef Wang, J.S.; Li, S.X.: An improved grey wolf optimizer based on differential evolution and elimination mechanism. Sci. Rep. 9(1), 7181 (2019)ADSCrossRef
55.
go back to reference Pham, T.P.; Fahringer, T.: Evolutionary multi-objective workflow scheduling for volatile resources in the cloud. IEEE Trans. Cloud Comput. 10(3), 1780–1791 (2020)CrossRef Pham, T.P.; Fahringer, T.: Evolutionary multi-objective workflow scheduling for volatile resources in the cloud. IEEE Trans. Cloud Comput. 10(3), 1780–1791 (2020)CrossRef
56.
go back to reference Ma, K.; Bagula, A.; Ajayi, O; Nyirenda, C.: Aiming at QoS: A Modified DE Algorithm for Task Allocation in Cloud Computing. In: ICC 2020–2020 IEEE International Conference on Communications (ICC) (pp. 1–7). IEEE (2020) Ma, K.; Bagula, A.; Ajayi, O; Nyirenda, C.: Aiming at QoS: A Modified DE Algorithm for Task Allocation in Cloud Computing. In: ICC 2020–2020 IEEE International Conference on Communications (ICC) (pp. 1–7). IEEE (2020)
57.
go back to reference Bharathi, S.; Chervenak, A.; Deelman, E.; Mehta, G.; Su, M.H; Vahi, K.: Characterization of scientific workflows. In: 2008 third workshop on workflows in support of large-scale science (pp. 1–10). IEEE (2008) Bharathi, S.; Chervenak, A.; Deelman, E.; Mehta, G.; Su, M.H; Vahi, K.: Characterization of scientific workflows. In: 2008 third workshop on workflows in support of large-scale science (pp. 1–10). IEEE (2008)
58.
go back to reference Subramoney, D.; Nyirenda, C.N.: A comparative evaluation of population-based optimization algorithms for workflow scheduling in cloud-fog environments. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 760–767). IEEE (2020) Subramoney, D.; Nyirenda, C.N.: A comparative evaluation of population-based optimization algorithms for workflow scheduling in cloud-fog environments. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 760–767). IEEE (2020)
59.
go back to reference Guo, Q. (2017) Task scheduling based on ant colony optimization in cloud environment. In: AIP conference proceedings AIP Publishing Guo, Q. (2017) Task scheduling based on ant colony optimization in cloud environment. In: AIP conference proceedings AIP Publishing
60.
go back to reference Natesha, B.V.; Sharma, N.K.; Domanal, S.; Guddeti, RMR.: GWOTS: grey wolf optimization-based task scheduling at the green cloud data center. In: 2018 14th international conference on semantics, knowledge and grids (SKG) (181–187). IEEE (2018) Natesha, B.V.; Sharma, N.K.; Domanal, S.; Guddeti, RMR.: GWOTS: grey wolf optimization-based task scheduling at the green cloud data center. In: 2018 14th international conference on semantics, knowledge and grids (SKG) (181–187). IEEE (2018)
61.
go back to reference Arora, N.; Banyal, R.K.: HPSOGWO: a hybrid algorithm for scientific workflow scheduling in cloud computing. Int. J. Adv. Comput. Sci. Appl. 11(10), 0111078 (2020) Arora, N.; Banyal, R.K.: HPSOGWO: a hybrid algorithm for scientific workflow scheduling in cloud computing. Int. J. Adv. Comput. Sci. Appl. 11(10), 0111078 (2020)
64.
go back to reference Shukla, P.; Pandey, S.; Agarwal, D.: An Efficient Offloading Technique using DQN for MEC-IoT Networks. In: 2023 6th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1–7). IEEE (2023) Shukla, P.; Pandey, S.; Agarwal, D.: An Efficient Offloading Technique using DQN for MEC-IoT Networks. In: 2023 6th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1–7). IEEE (2023)
Metadata
Title
DE-GWO: A Multi-objective Workflow Scheduling Algorithm for Heterogeneous Fog-Cloud Environment
Authors
Prashant Shukla
Sudhakar Pandey
Publication date
29-11-2023
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 3/2024
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-023-08425-0

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