A systematic review of workflow scheduling techniques in a fog environment
DOI:
https://doi.org/10.52756/ijerr.2023.v30.011Keywords:
Cloud computing, cloudsim, DAG, fog computing, metricsAbstract
Various recent trends in computer science include machine learning, block chain technology, IoT, Cloud computing, etc. Fog computing is one of the research areas used everywhere in science or other fields. Due to it providing very fast service in the heterogeneous platform, more security, and low latency. Workflow scheduling is one of the current research areas in the fog computing platform. Workflow scheduling allocates the different jobs into the available fog server and cloud servers. In this paper, we have identified some methods based on workflow scheduling in a fog environment and compared these methods based on tools and performance metrics.
Keywords: Cloud Computing, Fog Computing, Metrics, DAG, Cloudsim.
References
Abdel‐Basset, M., Mohamed, R., Chakrabortty, R. K., & Ryan, M. J. (2021). IEGA: an improved elitism‐based genetic algorithm for task scheduling problem in fog computing. International Journal of Intelligent Systems, 36(9), 4592-4631. https://doi.org/10.1002/int.22470
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. https://doi.org/10.1016/j.asoc.2021.107744
Ashi, Z., Al-Fawa’reh, M., & Al-Fayoumi, M. (2020). Fog computing: security challenges and countermeasures. Int. J. Comput. Appl., 175(15), 30-36. https://doi.org/10.5120/ijca2020920648
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the internet of things. Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing (MCC), pp. 13–16. doi/pdf/10.1145/2342509.2342513
Cardellini, V., Grassi, V., Presti, F. L., & Nardelli, M. (2015). On QoS-aware scheduling of data stream applications over fog computing infrastructures. IEEE. In 2015 IEEE Symposium on Computers and Communication (ISCC), pp. 271-276. https://doi.org/10.1109/ISCC.2015.7405527
Chakraborty, M. (2019). Fog computing vs. cloud computing. arXiv preprint, arXiv,1904.04026.
Choudhari, T., Moh, M., & Moh, T. S. (2018). Prioritized task scheduling in fog computing. In Proceedings of the ACMSE 2018 Conference, pp. 1-8. doi/abs/10.1145/3190645.3190699
Chowdhury, A., Karmakar, G., & Kamruzzaman, J. (2019). The co-evolution of cloud and IoT applications: Recent and future trends. IGI Global. In Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization, pp. 213-234.
Dastjerdi, A. V., Gupta, H., Calheiros, R. N., Ghosh, S. K., & Buyya, R. (2016). Fog computing: Principles, architectures, and applications. In Internet of Things, pp. 61-75.
Diaby, T., & Rad, B.B. (2017). Cloud computing: a review of the concepts and deployment models. International Journal of Information Technology and Computer Science, 9(6), 50-58. https://doi.org/10.5815/ijitcs.2017.06.07
Firdhous, M., Ghazali, O., & Hassan, S. (2014). Fog computing: Will it be the future of cloud computing? Proceedings of the Third International Conference on Informatics & Applications, Kuala Terengganu, Malaysia, 2014, 8-15.
Ghobaei-Arani, M., Souri, A., & Rahmanian, A. A. (2020). Resource management approaches in fog computing: a comprehensive review. Journal of Grid Computing, 18(1), 1-42. https://doi.org/10.1007/s10723-019-09491-1
Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., & Uhlig, S. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, 100514. https://doi.org/10.1016/j.iot.2022.100514
Hosseinioun, P., Kheirabadi, M., Tabbakh, S. R. K., & Ghaemi, R. (2020). A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm. Journal of Parallel and Distributed Computing, 143, 88-96. https://doi.org/10.1016/j.jpdc.2020.04.008
Hu, P., Dhelim, S., Ning, H., & Qiu, T. (2017). Survey on fog computing: architecture, key technologies, applications and open issues. Journal of Network and Computer Applications, 98, 27-42. https://doi.org/10.1016/j.jnca.2017.09.002
Jamil, B., Shojafar, M., Ahmed, I., Ullah, A., Munir, K., & Ijaz, H. (2020). A job scheduling algorithm for delay and performance optimization in fog computing. Concurrency and Computation: Practice and Experience, 32(7), e5581. https://doi.org/10.1002/cpe.5581
Kaur, M., & Aron, R. (2022). An energy-efficient load balancing approach for scientific workflows in fog computing. Wireless Personal Communications, 125(4), 3549-3573.
https://doi.org/10.1007/s11277-022-09724-9
Luan, T. H., Gao, L., Li, Z., Xiang, Y., Wei, G., & Sun, L. (2015). Fog computing: Focusing on mobile users at the edge. arXiv preprint, arXiv,1502.01815.10.
Mehta, R., Sahni, J., & Khanna, K. (2023).Task scheduling for improved response time of latency sensitive applications in fog integrated cloud environment. Multimedia Tools and Applications, pp. 1-24. https://doi.org/10.1007/s11042-023-14565-0
Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. National Institute of Standards and Technology Special Publication, 53, 1-7.
Miyachi, C. (2018). What is “Cloud”? It is time to update the NIST definition? IEEE Cloud computing, 5(03), 6-11.
Pham, X. Q., & Huh, E. N. (2016).Towards task scheduling in a cloud-fog computing system. In 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1-4. IEEE. https://doi.org/10.1109/APNOMS.2016.7737240
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. https://doi.org/10.1109/ICSPIS.2017.8311595
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. https://doi.org/10.1109/ACCESS.2023.3241240
Sarkar, S., & Misra, S. (2016). Theoretical modelling of fog computing: a green computing paradigm to support IoT applications. Iet Networks, 5(2), 23-29. https://doi.org/10.1049/iet-net.2015.0034
Stavrinides, G. L., & Karatza, H. D. (2021). Orchestrating real-time IoT workflows in a fog computing environment utilizing partial computations with end-to-end error propagation. Cluster Computing, 24(4), 3629-3650. https://doi.org/10.1007/s10586-021-03327-y
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
Tsai, W. T., Sun, X., & Balasooriya, J. (2010). Service-oriented cloud computing architecture. IEEE, In 2010 Seventh International Conference on Information Technology: New Generations, pp. 684-689. https://doi.org/10.1109/ITNG.2010.214.
Verma, M., Bhardwaj, N., &Yadav, A. K. (2016). Real time efficient scheduling algorithm for load balancing in fog computing environment. Int. J. Inf. Technol. Comput. Sci., 8(4), 1-10. https://doi.org/10.5815/ijitcs.2016.04.01
Xie, Y., Zhu, Y., Wang, Y., Cheng, Y., Xu, R., Sani, A. S., Yuan, D., & Yang, Y. (2019). A novel directional and non-local-convergent particle swarm optimization based workflow scheduling in cloud–edge environment. Future Generation Computer Systems, 97, 361-378. https://doi.org/10.1016/j.future.2019.03.005
Yadav, A. M., Tripathi, K. N., & Sharma, S. C. (2022).A bi-objective task scheduling approach in fog computing using hybrid fireworks algorithm. The Journal of Supercomputing, 78(3), 4236-4260. https://doi.org/10.1007/s11227-021-04018-6
Yin, C., Li, H., Peng, Y., Fang, Q., Xu, X., & Dan, T. (2023). An optimized resource scheduling algorithm based on GA and ACO algorithm. Pre-print (Version 1) available at Research Square, https://doi.org/10.21203/rs.3.rs-2559005/v1
Downloads
Published
How to Cite
Issue
Section
