{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:56:03Z","timestamp":1761897363355,"version":"3.37.0"},"reference-count":35,"publisher":"Science Research Society","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J INFORM SYSTEMS ENG"],"published-print":{"date-parts":[[2020,7,30]]},"DOI":"10.29333\/jisem\/8429","type":"journal-article","created":{"date-parts":[[2020,7,30]],"date-time":"2020-07-30T12:04:18Z","timestamp":1596110658000},"page":"em0121","source":"Crossref","is-referenced-by-count":4,"title":["Scheduling in Cloud and Fog Architecture: Identification of Limitations and Suggestion of Improvement Perspectives"],"prefix":"10.52783","volume":"5","author":[{"given":"Celestino","family":"Barros","sequence":"first","affiliation":[]},{"given":"V\u00edtor","family":"Rocio","sequence":"additional","affiliation":[]},{"given":"Andr\u00e9","family":"Sousa","sequence":"additional","affiliation":[]},{"given":"Hugo","family":"Paredes","sequence":"additional","affiliation":[]}],"member":"30593","reference":[{"key":"162206","doi-asserted-by":"crossref","unstructured":"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 \u201816, IEEE, pp. 1-5, https:\/\/doi.org\/10.1109\/ICT.2016.7500362","DOI":"10.1109\/ICT.2016.7500362"},{"key":"162207","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.23919\/CISTI49556.2020.9141156"},{"key":"162208","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/MCC.2017.27"},{"key":"162209","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ISCC.2015.7405527"},{"key":"162210","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/JIOT.2016.2565516"},{"key":"162211","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/GLOCOM.2017.8255037"},{"key":"162212","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.future.2012.05.023"},{"key":"162213","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/CloudNet.2015.7335282"},{"key":"162214","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.jss.2019.04.058"},{"key":"162215","unstructured":"Harzing, A. W. (2020). Publish or perish. Available at: https:\/\/harzing.com\/resources\/publish-or-perish (Accessed: 06 July 2020)."},{"key":"162216","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/PERCOMW.2016.7457059"},{"key":"162217","unstructured":"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)."},{"key":"162218","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.imu.2017.02.005"},{"key":"162219","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/MIC.2017.26"},{"key":"162220","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ACCESS.2017.2756826"},{"key":"162221","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1002\/spe.2392"},{"key":"162222","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.4102\/ijmla.v2i1.5"},{"key":"162223","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/VTCSpring.2015.7146129"},{"key":"162224","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.compeleceng.2018.04.001"},{"key":"162225","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1080\/17517575.2017.1304579"},{"key":"162226","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/TCC.2015.2485206"},{"key":"162227","doi-asserted-by":"crossref","unstructured":"Seddik, Y. and Hanz\u00e1lek, Z. (2017). Match-up scheduling of mixedcriticality jobs: \u2018Maximizing the probability of execution jobs. European Journal of Operational Research, 262(1), 46-59. https:\/\/doi.org\/10.1016\/j.ejor.2017.03.054","DOI":"10.1016\/j.ejor.2017.03.054"},{"key":"162228","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.future.2015.03.014"},{"key":"162229","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.pmcj.2015.07.005"},{"key":"162230","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1186\/s13677-018-0105-8"},{"key":"162231","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1007\/s10586-014-0420-x"},{"key":"162232","doi-asserted-by":"crossref","unstructured":"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 \u201817, IEEE. https:\/\/doi.org\/10.1109\/ICFEC.2017.12","DOI":"10.1109\/ICFEC.2017.12"},{"key":"162233","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1007\/s11042-018-7051-9"},{"key":"162234","unstructured":"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)."},{"key":"162235","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.jpdc.2018.04.004"},{"key":"162236","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1007\/s00500-014-1506-3"},{"key":"162237","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/JIOT.2018.2823000"},{"key":"162238","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/TSC.2015.2511002"},{"key":"162239","doi-asserted-by":"crossref","unstructured":"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 \u201815, IEEE, pp. 122-126.","DOI":"10.4108\/eai.19-8-2015.2260126"},{"key":"162240","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/CloudCom.2015.27"}],"container-title":["Journal of Information Systems Engineering and Management"],"original-title":[],"link":[{"URL":"https:\/\/www.jisem-journal.com\/download\/scheduling-in-cloud-and-fog-architecture-identification-of-limitations-and-suggestion-of-improvement-8429.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T18:31:42Z","timestamp":1738693902000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jisem-journal.com\/article\/scheduling-in-cloud-and-fog-architecture-identification-of-limitations-and-suggestion-of-improvement-8429"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,30]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020]]}},"alternative-id":["8429"],"URL":"https:\/\/doi.org\/10.29333\/jisem\/8429","relation":{},"ISSN":["2468-4376"],"issn-type":[{"type":"electronic","value":"2468-4376"}],"subject":[],"published":{"date-parts":[[2020,7,30]]}}}