Journal of Information Systems Engineering and Management

Scheduling in Cloud and Fog Architecture: Identification of Limitations and Suggestion of Improvement Perspectives
Celestino Barros 1 * , Vítor Rocio 2, André Sousa 3, Hugo Paredes 4
More Detail
1 Faculty of Science and Technology of University of Cabo Verde, Praia, CAPE VERDE
2 INESC TEC and Open University of Portugal, Lisbon, PORTUGAL
3 Critical TechWorks, Porto, PORTUGAL
4 INESCT TEC and University of Trás-os-Montes and Alto Douro, Vila Real, PORTUGAL
* Corresponding Author
Research Article

Journal of Information Systems Engineering and Management, 2020 - Volume 5 Issue 3, Article No: em0121
https://doi.org/10.29333/jisem/8429

Published Online: 30 Jul 2020

Views: 458 | Downloads: 384

How to cite this article
APA 6th edition
In-text citation: (Barros et al., 2020)
Reference: Barros, C., Rocio, V., Sousa, A., & Paredes, H. (2020). Scheduling in Cloud and Fog Architecture: Identification of Limitations and Suggestion of Improvement Perspectives. Journal of Information Systems Engineering and Management, 5(3), em0121. https://doi.org/10.29333/jisem/8429
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Barros C, Rocio V, Sousa A, Paredes H. Scheduling in Cloud and Fog Architecture: Identification of Limitations and Suggestion of Improvement Perspectives. J INFORM SYSTEMS ENG. 2020;5(3):em0121. https://doi.org/10.29333/jisem/8429
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Barros C, Rocio V, Sousa A, Paredes H. Scheduling in Cloud and Fog Architecture: Identification of Limitations and Suggestion of Improvement Perspectives. J INFORM SYSTEMS ENG. 2020;5(3), em0121. https://doi.org/10.29333/jisem/8429
Chicago
In-text citation: (Barros et al., 2020)
Reference: Barros, Celestino, Vítor Rocio, André Sousa, and Hugo Paredes. "Scheduling in Cloud and Fog Architecture: Identification of Limitations and Suggestion of Improvement Perspectives". Journal of Information Systems Engineering and Management 2020 5 no. 3 (2020): em0121. https://doi.org/10.29333/jisem/8429
Harvard
In-text citation: (Barros et al., 2020)
Reference: Barros, C., Rocio, V., Sousa, A., and Paredes, H. (2020). Scheduling in Cloud and Fog Architecture: Identification of Limitations and Suggestion of Improvement Perspectives. Journal of Information Systems Engineering and Management, 5(3), em0121. https://doi.org/10.29333/jisem/8429
MLA
In-text citation: (Barros et al., 2020)
Reference: Barros, Celestino et al. "Scheduling in Cloud and Fog Architecture: Identification of Limitations and Suggestion of Improvement Perspectives". Journal of Information Systems Engineering and Management, vol. 5, no. 3, 2020, em0121. https://doi.org/10.29333/jisem/8429
ABSTRACT
Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that can lead us to the question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences.
In this paper, we conducted a systematic review of the literature on the main task by: scheduling algorithms in the existing cloud and fog architecture; studying and discussing their limitations, and we explored and suggested some perspectives for improvement.
KEYWORDS
REFERENCES
  • Aazam, M., Hilaire, M. St., Lung, Ch. and Lambadaris, I. (2016). MeFoRE: Resource Estimation QoE based at Fog to Enhance QoS in IoT. In: Proc. of the 23rd International Conference on Telecommunications, ICT ‘16, IEEE, pp. 1-5, https://doi.org/10.1109/ICT.2016.7500362
  • Barros, C., Rocio, V., Sousa, A. and Paredes, H. (2020). Survey on Job Scheduling in Cloud-Fog Architecture. 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), Sevilla, Spain, pp. 1-7. https://doi.org/10.23919/CISTI49556.2020.9141156
  • Bittencourt, L. F., Diaz-Montes, J., Buyya, R., Rana, O. F. and Parashar, M. (2017). Mobility-Aware Computing in Fog Application Scheduling. IEEE Cloud Computing, 4(2), 26-35, https://doi.org/10.1109/MCC.2017.27
  • Cardellini, V., Grassi, V., Presti, F. L. and Nardelli, M. (2015). On QoS-Aware Scheduling of Data Stream Applications over Fog Computing Infrastructures. IEEE Symposium on Computers and Communication (ISCC), pp. 271-276, https://doi.org/10.1109/ISCC.2015.7405527
  • Deng, R., Luan, T. H., Lu, R., Liang, H. and Lai, C. (2016). Optimal Allocation Workload in Fog-Cloud Computing Towards Balanced Delay and Power Consumption. IEEE Internet Things J., X(X), 1171-1181, https://doi.org/10.1109/JIOT.2016.2565516
  • Fan, J., Wei, X., Wang, T., Lan, T. and Subramaniam, S. (2017). Deadline-aware task scheduling in a Tiered IoT Infrastructure. GLOBECOM 2017 - 2017 IEEE Global Communications Conference, Singapore, pp. 1-7, https://doi.org/10.1109/GLOCOM.2017.8255037
  • Fernando, N., Loke, S. W. and Rahayu, W. (2013). Mobile cloud computing: The survey. Future Generation Computer Systems, 29(1), 84-106, https://doi.org/10.1016/j.future.2012.05.023
  • Ghouma, H. and Jaseemuddin, M. (2015). Context aware resource allocation and scheduling for mobile cloud. 2015 IEEE 4th International Conference on Cloud Networking (CloudNet), Niagara Falls, ON, pp. 67-70, https://doi.org/10.1109/CloudNet.2015.7335282
  • Gill, S. S., Garraghan, P. and Buyya, R. (2019). ROUTER: Fog enabled cloud based intelligent resource management approach for smart home IoT devices. Journal of Systems and Software, 154, 125-138, https://doi.org/10.1016/j.jss.2019.04.058
  • Harzing, A. W. (2020). Publish or perish. Available at: https://harzing.com/resources/publish-or-perish (Accessed: 06 July 2020).
  • Intharawijitr, K., Iida, K. and Koga, H. (2016). Analysis of Fog Model considering Computing and Communication Latency in 5G Cellular Networks. IEEE International Conference on Pervasive Computing and Communication Workshops (Workshops Percom), pp. 1-4. https://doi.org/10.1109/PERCOMW.2016.7457059
  • Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Join Tecnical Report, Keele University RT / SE-0401. Available at: http://www.it.hiof.no/~haraldh/misc/2016-08-22-smat/Kitchenham-Systematic-Review-2004.pdf (Accessed: 6 July 2020).
  • Lawanyashri, M., Balusamy, B. and Subha, S. (2017). Energy-Aware fruitfly hybrid optimization for load balancing in cloud environments is EHR applications. Informatics Med. Unlocked, 8(March), 42-50, https://doi.org/10.1016/j.imu.2017.02.005
  • Li, Q., Novak, E., Yi, S. and Hao, Z. (2017). Challenges and Software Architecture for Fog Computing. IEEE Internet Computing, 21(2), 44-53. https://doi.org/10.1109/MIC.2017.26
  • Li, T., Liu, Y., Gao, A. L. and Liu, A. (2017). A for Cooperative - based Smart Sensing Tasks in Fog-Computing. IEEE, Access, 5, 21296-21311. https://doi.org/10.1109/ACCESS.2017.2756826
  • Mahmud, M. R., Afrin, M., Razzaque, M. A., Hassan, M. M., Alelaiwi, A. and Alrubaian, M. (2016). Maximizing Quality of Experience through Context-Aware Mobile Application Scheduling in Cloudlet Infrastructure. Software: Practice and Experience, 46(11), 1525-1545. https://doi.org/10.1002/spe.2392
  • Musumba, G. W. and Nyongesa, H. O. (2013). Context awareness in mobile computing: a review. International Journal of Machine Learning and Applications, 2(1), 1-5, https://doi.org/10.4102/ijmla.v2i1.5
  • Oueis, J., Strinati, E. C. and Barbarossa, S. (2015). The Fog Balancing: Load Cell Distribution for Small Cloud Computing. IEEE 81st Vehicular Technology Conference (VTC Spring), Glasgow, pp. 1-6. https://doi.org/10.1109 / VTCSpring.2015.7146129
  • Sahoo, P. K. and Dehury, C. K. (2018). Efficient data and CPU-intensive job scheduling algorithms for healthcare cloud. Computters and Electrical Engineering, 68(March), 119-139. https://doi.org/10.1016/j.compeleceng.2018.04.001
  • Salim, B., Sherali, Z. and Abdelhamid, M. (2018). Fog computing job scheduling optimization based on bees swarm. Enterprise Information Systems, 12(4), 373-397. https://doi.org/10.1080/17517575.2017.1304579
  • Sarkar, S., Chatterjee, S. and Misra, S. (2018). Assessment of the Suitability of Fog Computing in the Context of Internet of Things. IEEE Transactions on Cloud Computing, 6(1), 46-59. https://doi.org/10.1109/TCC.2015.2485206
  • Seddik, Y. and Hanzálek, Z. (2017). Match-up scheduling of mixedcriticality jobs: ‘Maximizing the probability of execution jobs. European Journal of Operational Research, 262(1), 46-59. https://doi.org/10.1016/j.ejor.2017.03.054
  • Sheikhalishahi, M., Grandinetti, L., Guerriero, F., Wallace, R. M. and Vazquez-Poletti, J. L. (2015). Multi-dimensional job scheduling. Future Generation Computer Systems, 54, 123-131. https://doi.org/10.1016/j.future.2015.03.014
  • Shi, T., Yang, M., Li, X., Law, Q. and Jiang, Y. (2016). An energy-efficient scheduling scheme for time-constrained tasks in the local mobile clouds. Pervasive and Mobile Computing, 27, 90-105. https://doi.org/10.1016/j.pmcj.2015.07.005
  • Shinde, S. K. and Gawali, M. B. (2018). Task scheduling and resource allocation in the cloud using heuristic approach. Journal Cloud Computing, 7, 4. https://doi.org/10.1186/s13677-018-0105-8
  • Shojafar, M., Javanmardi, S. and Abolfazli, S. (2015). FUGE: The joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and the genetic method. Cluster Computing, 18, 829-844. https://doi.org/10.1007/s10586-014-0420-x
  • Skarlat, O., Nardelli, M., Schulte, S. and Dustdar, S. (2017). Towards QoS-aware Service Placement Fog. In: Procedure of the First IEEE International Conference on Fog and Edge Computing, ICFEC ‘17, IEEE. https://doi.org/10.1109/ICFEC.2017.12
  • Stavrinides, G. L. and Karatza, H. D. (2019). A hybrid approach to real-time scheduling IoT workflows in fog and cloud environments. Multimedia Tools and Applications, 78, 24639-24655. https://doi.org/10.1007/s11042-018-7051-9
  • Swaroop, P. (2019). Cost Based Job Scheduling In Fog Computing (PhD thesis), DTU, India, Available at: http://dspace.dtu.ac.in:8080/jspui/handle/repository/16722 (Accessed: 6 July 2020).
  • Tiwary, M., Puthal, D., Sahoo, K. S., Sahoo, B. and Yang, L. T. (2018). Response time for optimization cloudlets in Mobile Computing Edge. Journal of Parallel and Distributed Computing, 119, 81-91. https://doi.org/10.1016/j.jpdc.2018.04.004
  • Wang, X., Wang, Y. and Cui, Y. (2016). An energy-aware bi-level optimization model for multi-job scheduling problems under cloud. Soft Comput., 20(1), 303-317. https://doi.org/10.1007/s00500-014-1506-3
  • Yang, Y., Zhao, S., Zhang, W., Chen, Y., Luo, X. and Wang, J. (2018). DEBTS: Delay Balanced Energy Task Scheduling in Homogeneous Fog Networks. IEEE Internet of Things Journal, 5(3), 2094-2106. https://doi.org/10.1109/JIOT.2018.2823000
  • Zhou, B., Dastjerdi, A. V., Calheiros, R. N., Srirama, S. N. and Buyya, R. (2017). mcloud: The Context-Aware Offloading Framework for Heterogeneous Mobile Cloud. IEEE Transactions on Services Computing, 10(5), 797-810. https://doi.org/10.1109/TSC.2015.2511002
  • Zhou, X., Sun, M., Wang, Y. and Wu, X. (2015). The New QoE-driven Video Cache Allocation Scheme for Mobile Cloud Server. In: Procedure of the 11th Conference on International Heterogeneous Networking for Quality, Reliability, Security and Robustness, QSHINE ‘15, IEEE, pp. 122-126.
  • Zhu, C., Li, X., Leung, V., Hu, X. and Yang, T. L. (2015). Towards Integration of Wireless Sensor Networks and Cloud Computing. IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), IEEE, Singapore, pp. 62-69. https://doi.org/10.1109/CloudCom.2015.27
LICENSE
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.