[en] Given the increase diversity of smart devices and objectives of the application management such as energy consumption, makespan users expect their requests to be responded to in an appropriate computation environment as properly as possible. In this paper, a method of workflow scheduling based on the fog-cloud architecture has been designed given the high processing capability of the cloud and the close communication between the user and the fog computing node, which reduces delay in response. We also seek to minimize consumption and reduce energy use and monetary cost in order to maximize customer satisfaction with proper scheduling. Given the large number of variables that are used in workflow scheduling and the optimization of contradictory objectives, the problem is NP-hard, and the multi-objective metaheuristic krill herd algorithm is used to solve it. The initial population is generated in a smart fashion to allow fast convergence of the algorithm. For allocation of tasks to the available fog-cloud resources, the EFT (earliest finish time) technique is used, and resource voltage and frequency are assumed to be dynamic to reduce energy use. A comprehensive simulation has been made for assessment of the proposed method in different scenarios with various values of CCR. The simulation results indicate that makespan exhibits improvements by 9.9, 8.7% and 6.7% on average compared with respect to the methods of IHEFT, HEFT and IWO-CA, respectively. Moreover, the monetary cost of the method and energy use have simultaneously decreased in the fog-cloud environment.
Disciplines :
Computer science
Author, co-author :
KHALEDIAN, Navid ✱; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CritiX ; Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
Khamforoosh, Keyhan; Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
Azizi, Sadoon; Department of Computer Engineering and IT, University of Kurdistan, Sanandaj, Iran
Maihami, Vafa; Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
✱ These authors have contributed equally to this work.
External co-authors :
yes
Language :
English
Title :
IKH-EFT: An improved method of workflow scheduling using the krill herd algorithm in the fog-cloud environment
Not applicable. none. Not applicable. The authors declare that they have no competing interests. The specific contributions made by each author is as follows: NK: Conceptualization, methodology, implementation, writing-original draft, KK: writing—review & Editing, formal analysis. SA & VM: validation, review & editing, all authors read and approved the final manuscript.
Aghdam, Z.N., Rahmani, A.M., Hosseinzadeh, M., The role of the internet of things in healthcare: future trends and challenges. Comput. Methods Prog. Biomed., 199, 2021, 105903.
Kassab, Wa, Darabkh, K.A., A–Z survey of internet of things: architectures, protocols, applications, recent advances, future directions and recommendations. J. Netw. Comput. Appl., 163, 2020, 102663.
Luo, Q., et al., Resource scheduling in edge computing: A survey. IEEE Communications Surveys & Tutorials, 2021.
Islam, A., et al. A survey on task offloading in multi-access edge computing. J. Syst. Archit., 118, 2021, 102225.
Belgacem, A., et al. Efficient dynamic resource allocation method for cloud computing environment. Clust. Comput. 23:4 (2020), 2871–2889.
Laroui, M., et al. Edge and fog computing for IoT: A survey on current research activities & future directions. Comput. Commun. 180 (2021), 210–231.
Guevara, J.C., da Fonseca, N.L., Task scheduling in cloud-fog computing systems. Peer to Peer Netw. Appl. 14:2 (2021), 962–977.
Bonomi, F., et al. Fog computing and its role in the internet of things. in Proceedings of the first edition of the MCC workshop on Mobile cloud computing. 2012.
Kaur, N., Kumar, A., Kumar, R., A systematic review on task scheduling in Fog computing: taxonomy, tools, challenges, and future directions. Concurr. Comput.: Pract. Exp., 33(21), 2021, e6432.
Azizi, S., et al. Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: a semi-greedy approach. J. Netw. Comput. Appl., 2022, 103333.
Hosseinioun, P., et al. aTask scheduling approaches in fog computing: a survey. Trans. Emerg. Telecommun. Technol., 2020, e3792.
Khan, W.Z., et al. Edge computing: a survey. Future Gener. Comput. Syst. 97 (2019), 219–235.
Javanmardi, S., et al. FUPE: A security driven task scheduling approach for SDN-based IoT–Fog networks. J. Inf. Secur. Appl., 60, 2021, 102853.
Tychalas, D., Karatza, H., A scheduling algorithm for a fog computing system with bag-of-tasks jobs: simulation and performance evaluation. Simul. Model. Pract. Theory, 98, 2020, 101982.
Hussain, M., et al. Deadline-constrained energy-aware workflow scheduling in geographically distributed cloud data centers. Future Gener. Comput. Syst., 2022.
Versluis, L., Iosup, A., A survey of domains in workflow scheduling in computing infrastructures: Community and keyword analysis, emerging trends, and taxonomies. Future Gener. Comput. Syst. 123 (2021), 156–177.
Ahmad, Z., et al. Scientific workflows management and scheduling in cloud computing: taxonomy, prospects, and challenges. IEEE Access 9 (2021), 53491–53508.
Abdel-Basset, M., et al. Energy-aware metaheuristic algorithm for industrial-Internet-of-Things task scheduling problems in fog computing applications. IEEE Inter. Things J. 8:16 (2020), 12638–12649.
Tanha, M., Hosseini Shirvani, M., Rahmani, A.M., A hybrid meta-heuristic task scheduling algorithm based on genetic and thermodynamic simulated annealing algorithms in cloud computing environments. Neural Comput. Appl. 33:24 (2021), 16951–16984.
Belgacem, A., Beghdad-Bey, K., Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost. Clust. Comput. 25:1 (2022), 579–595.
Shirvani, M.H., A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems. Eng. Appl. Artif. Intell., 90, 2020, 103501.
Arora, N., Banyal, R.K., A particle grey wolf hybrid algorithm for workflow scheduling in cloud computing. Wirel. Pers. Commun. 122:4 (2022), 3313–3345.
Taghinezhad-Niar, A., Pashazadeh, S., Taheri, J., QoS-aware online scheduling of multiple workflows under task execution time uncertainty in clouds. Clust. Comput., 2022, 1–18.
Ghafari, R., Kabutarkhani, F.H., Mansouri, N., Task scheduling algorithms for energy optimization in cloud environment: a comprehensive review. Clust. Comput., 2022, 1–59.
Ijaz, S., et al. Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103:9 (2021), 2033–2059.
Choudhary, A., et al. Energy-aware scientific workflow scheduling in cloud environment. Clust. Comput., 2022, 1–30.
Taghinezhad-Niar, A., Pashazadeh, S., Taheri, J., Energy-efficient workflow scheduling with budget-deadline constraints for cloud. Computing 104:3 (2022), 601–625.
Hoseiny, F., et al. PGA: A Priority-aware Genetic Algorithm for Task Scheduling in Heterogeneous Fog-Cloud Computing. in IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). 2021. IEEE.
Hosseinioun, P., et al. aTask scheduling approaches in fog computing: a survey. Trans. Emerg. Telecommun. Technol., 33(3), 2022, e3792.
Houssein, E.H., et al. Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm Evolut. Comput., 62, 2021, 100841.
Gandomi, A.H., Alavi, A.H., Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17:12 (2012), 4831–4845.
Guerrero, C., Lera, I., Juiz, C., Genetic-based optimization in fog computing: current trends and research opportunities. Swarm Evolut. Comput., 2022, 101094.
Cheng, F., et al. Cost-aware job scheduling for cloud instances using deep reinforcement learning. Clust. Comput. 25:1 (2022), 619–631.
Chai, X., Task scheduling based on swarm intelligence algorithms in high performance computing environment. J. Ambient Intell. Humaniz. Comput., 2020, 1–9.
Sharma, M., Garg, R., An artificial neural network based approach for energy efficient task scheduling in cloud data centers. Sustain. Comput. Inform. Syst., 26, 2020, 100373.
Pirozmand, P., et al. GSAGA: A hybrid algorithm for task scheduling in cloud infrastructure. J. Supercomput., 2022, 1–27.
Xia, X., et al. Multi-objective workflow scheduling based on genetic algorithm in cloud environment. Inf. Sci., 2022.
Chhabra, A., Singh, G., Kahlon, K.S., QoS-Aware energy-efficient task scheduling on HPC cloud infrastructures using swarm-intelligence meta-heuristics. CMC Comput. Mater. Contin. 64:2 (2020), 813–834.
Jamil, B., et al. A job scheduling algorithm for delay and performance optimization in fog computing. Concurr. Comput. Pract. Exp., 32(7), 2020, e5581.
Zhou, Z., et al. An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments. Neural Comput. Appl. 32:6 (2020), 1531–1541.
Delavar, A.G., Akraminejad, R., Mozafari, S., HDECO: a method for decreasing energy and cost by using virtual machine migration by considering hybrid parameters. Comput. Commun. 195 (2022), 49–60.
Yadav, A.M., Tripathi, K.N., Sharma, S.C., A bi-objective task scheduling approach in fog computing using hybrid fireworks algorithm. J. Supercomput. 78:3 (2022), 4236–4260.
Doostali, S., Babamir, S.M., Eini, M., CP-PGWO: multi-objective workflow scheduling for cloud computing using critical path. Clust. Comput. 24:4 (2021), 3607–3627.
NoorianTalouki, R., M.H. Shirvani, H. Motameni, A heuristic-based task scheduling algorithm for scientific workflows in heterogeneous cloud computing platforms. Journal of King Saud University-Computer and Information Sciences, 2021.
Hosseinioun, P., et al. A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm. J. Parallel Distrib. Comput. 143 (2020), 88–96.
Abualigah, L., Diabat, A., A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Clust. Comput. 24:1 (2021), 205–223.
Kakkottakath Valappil Thekkepuryil, J., Suseelan, D.P., Keerikkattil, P.M., An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment. Clust. Comput. 24:3 (2021), 2367–2384.
Ahmed, O.H., et al. Using differential evolution and Moth–Flame optimization for scientific workflow scheduling in fog computing. Appl. Soft Comput., 112, 2021, 107744.
Natesan, G., Chokkalingam, A., Multi-objective task scheduling using hybrid whale genetic optimization algorithm in heterogeneous computing environment. Wirel. Pers. Commun. 110:4 (2020), 1887–1913.
Bacanin, N., et al. Modified firefly algorithm for workflow scheduling in cloud-edge environment. Neural Comput. Appl. 34:11 (2022), 9043–9068.
Manikandan, N., Gobalakrishnan, N., Pradeep, K., Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment. Comput. Commun. 187 (2022), 35–44.
Dabiri, S., Azizi, S., Abdollahpouri, A., Optimizing deadline violation time and energy consumption of IoT jobs in fog–cloud computing. Neural Comput. Appl., 2022, 1–17.
Javaheri, D., et al. An improved discrete harris hawk optimization algorithm for efficient workflow scheduling in multi-fog computing. Sustain. Comput. Inform. Syst., 36, 2022, 100787.
Nazari, A., et al., An Intelligent SDN-Based Clustering Approach for Optimizing IoT Power Consumption in Smart Homes. Wireless Communications and Mobile Computing, 2022. 2022.
Bittencourt, L.F., et al. Scheduling in distributed systems: a cloud computing perspective. Comput. Sci. Rev. 30 (2018), 31–54.
