Deadline Performance and Tenant Fairness Under Security Screening in Cloud Workload Scheduling
Article Sidebar
Main Article Content
Abstract
Cloud applications share execution capacity while imposing different deadlines and security screening requirements. A scheduler can therefore face congestion even when aggregate capacity appears sufficient, particularly when a large tenant reaches its concurrency limit. This study investigates interaction through a reproducible discrete event simulation of screened cloud jobs. Four nonpreemptive policies are compared: first in first out, shortest job first, earliest deadline first, and a deadline policy with a bounded penalty for tenants that have already consumed more virtual service. The primary experiment contains 240 runs covering three arrival rates, 20 random seeds, and 1,200 jobs per run. An additional 80 runs remove the restrictive tenant concurrency limit at the highest arrival rate. With eight workers and a four-worker tenant limit, shortest job first has a mean deadline miss rate of 5.26% at 1.5 jobs per simulated second, compared with 44.57% for first in first out and 23.97% for earliest deadline first. The service balance candidate reaches 28.91%, showing that the added fairness penalty does not improve deadline performance in this workload. Increasing the tenant limit from four to eight reduces the earliest deadline miss rate to 1.41%. These findings identify tenant capacity fragmentation and job length variation as stronger influences than the tested fairness adjustment. The results are generated from synthetic workloads and do not measure production cloud performance or resistance to cyberattacks. The contribution is an executable comparative study of scheduling under a mandatory modeled screening stage, with explicit workload assumptions and a tenant capacity sensitivity analysis.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
[1] P. Chakraborty et al., “An Intelligent Cybersecurity Framework for Data Protection in Cloud Computing Environments,” Spectr. Eng. Sci., vol. 2, no. 3, pp. 657–677, 2024, doi: https://thesesjournal.com/index.php/1/article/view/3573.
[2] S. Rose, O. Borchert, S. Mitchell, and S. Connelly, “Zero trust architecture,” NIST Spec. Publ., vol. 800, no. 207, pp. 1–52, 2020, doi: https://doi.org/10.6028/NIST.SP.800-207.
[3] R. Chandramouli and Z. Butcher, “A Zero Trust Architecture Model for Access Control in Cloud-Native Applications in Multi-Cloud Environments,” National Institute of Standards and Technology, 2023. doi: https://doi.org/10.6028/NIST.SP.800-207Ahttps://doi.org/10.6028/NIST.SP.800-207A.
[4] E. Barker, “Recommendation for Key Management: Part 1—General,” National Institute of Standards and Technology, 2020. doi: https://doi.org/10.6028/NIST.SP.800-57pt1r5.
[5] S. N. Hasan et al., “The influence of artificial intelligence on data system security,” Int. J. Comput. Exp. Sci. Eng., vol. 11, no. 3, 2025, doi: https://doi.org/10.22399/ijcesen.3476.
[6] J. Kaur et al., “Comparative Analysis of Transformer and LSTM Architectures for Cybersecurity Threat Detection Using Machine Learning,” EAI Endorsed Trans. AI Robot., vol. 4, no. 1, 2025, doi: https://doi.org/10.4108/airo.9759.
[7] J. Kaur et al., “Advanced Cyber Threats and Cybersecurity Innovation-Strategic Approaches and Emerging Solutions,” J. Comput. Sci. Technol. Stud., vol. 5, no. 3, pp. 112–121, 2023, doi: https://doi.org/10.32996/jcsts.2023.5.3.9.
[8] M. A. R. Chy et al., “Understanding the Relationship Between Data Governance and Business Analytics Success: A Case Study of Global Corporations,” J. Inf. Syst. Eng. Manag., vol. 9, no. 4s, 2024, doi: https://doi.org/10.52783/jisem.v9i4s.14807.
[9] F. Mahmud et al., “Big data and cloud computing in IT project management: A framework for enhancing performance and decision-making,” Fuel Cells Bull., vol. 2023, no. 9, pp. 1–18, 2023, doi: https://doi.org/10.52710/fcb.166.
[10] F. Mahmud, M. A. Goffer, H. Rahman, and G. T. Alam, “The Role of Cloud-Based Management Information Systems in Enhancing IT Project Governance and Stakeholder Collaboration,” 2025, doi: https://doi.org/10.63332/joph.v5i4.974.
[11] S. M. Orthi et al., “DataOps-oriented big data governance for automated decision pipelines,” in In 2025 1st International Conference on Advancement in Futuristic Technologies (ICAFT) (pp. 1–8). IEEE, 2025, pp. 1–8. doi: https://doi.org/10.1109/ICAFT66710.2025.11452860.
[12] P. Chakraborty et al., “Trustworthy data lakehouse design using federated learning and blockchain,” in 2025 1st International Conference on Advancement in Futuristic Technologies (ICAFT), 2025, pp. 1–8. doi: https://doi.org/10.1109/ICAFT66710.2025.11453041.
[13] M. K. Tuhin et al., “Federated Machine Learning for Privacy-Preserving Cyber Threat Intelligence in Global Cloud-Integrated MIS Platforms,” Eastasouth J. Inf. Syst. Comput. Sci., vol. 2, no. 2, pp. 284–293, 2024, doi: https://doi.org/10.58812/esiscs.v2i02.1123.
[14] N. Das et al., “AI-enhanced privacy preservation using homomorphic federated models,” in In 2025 1st International Conference on Advancement in Futuristic Technologies (ICAFT) (pp. 1–8). IEEE, 2025, pp. 1–8. doi: https://doi.org/10.1109/ICAFT66710.2025.11453096.
[15] S. M. Orthi et al., “Federated learning with privacy-preserving big data analytics for distributed healthcare systems,” J. Comput. Sci. Technol. Stud., vol. 7, no. 8, pp. 269–281, 2025, doi: https://doi.org/10.32996/jcsts.2025.7.8.31.
[16] S. N. Hasan et al., “Enhancing Cybersecurity Threat Detection and Response Through Big Data Analytics in Management Information Systems,” 2023, doi: https://doi.org/10.52710/fcb.137.
[17] M. D. Hossain, M. S. Sikder, M. S. Uddin, R. M. Ahsan, B. Uddin, and T. Hossen, “Cognitive Cyber Defense: AI–MIS Integration through Big Data and Cloud Frameworks for Next-Generation Digital Resilience,” Eastasouth J. Inf. Syst. Comput. Sci., vol. 1, no. 2, pp. 140–152, 2023, doi: https://doi.org/10.58812/esiscs.v1i02.764.
[18] M. A. Goffer et al., “AI-Enhanced Cyber Threat Detection and Response Advancing National Security in Critical Infrastructure,” J. Posthumanism, vol. 5, no. 3 SE-, pp. 1667–1689, Apr. 2025, doi: https://doi.org/10.63332/joph.v5i3.965.
[19] M. A. Goffer et al., “Cybersecurity and Supply Chain Integrity: Evaluating the Economic Consequences of Vulnerabilities in US Infrastructure,” J. Manag. World, vol. 2, pp. 233–243, 2025, doi: https://doi.org/10.53935/jomw.v2024i4.907.
[20] M. S. Uddin, M. S. Sikder, M. M. Anwar, and F. Hossain, “AI-Driven Cybersecurity and Big Data-Enabled MIS Frameworks: Strengthening Supply Chain Integrity, Energy Resilience, and Critical Infrastructure Protection,” J. Comput. Sci. Technol. Stud., vol. 7, no. 9, pp. 223–232, 2025, doi: https://doi.org/10.32996/jcsts.2025.7.9.26.
[21] S. Sultana et al., “AI-Augmented Big Data Analytics for Real-Time Cyber Attack Detection and Proactive Threat Mitigation,” Int. J. Comput. Exp. Sci. Eng., vol. 11, no. 3 SE-Research Article, Jul. 2025, doi: https://doi.org/10.22399/ijcesen.3564.
[22] M. D. Hossain, M. S. Uddin, M. S. Sikder, T. Hossen, B. Uddin, and R. M. Ahsan, “Green and Secure Data Centers: Balancing Energy Efficiency with Advanced Cybersecurity Measures,” J. Comput. Sci. Technol. Stud., vol. 6, no. 5, pp. 300–315, 2024, doi: https://doi.org/10.32996/jcsts.2024.6.5.24.
[23] F. Mahmud et al., “AI-Driven Cybersecurity in IT Project Management: Enhancing Threat Detection and Risk Mitigation,” J. Posthumanism, vol. 5, no. 4 SE-, pp. 23–44, Apr. 2025, doi: https://doi.org/10.63332/joph.v5i4.974.
[24] S. M. Orthi et al., “AI-Driven Cyber Threat Intelligence as a Management Information System: Integrating Cybersecurity Governance and IT Project Management for Organizational Resilience,” Eastasouth J. Inf. Syst. Comput. Sci., vol. 1, no. 2, pp. 194–213, 2023, doi: https://doi.org/10.58812/esiscs.v1i02.873.
[25] A. Shan-A-Alahi, M. Mustafizur, K. M. R. Hossan, A. Al Zaiem, and M. M. Rahman, “Cybersecurity Training and Its Influence on Employee Behavior in Business Environments,” Comput. Fraud Secur., vol. 2024, no. 12, 2024, doi: https://doi.org/10.52710/cfs.689.
[26] M. A. Siam et al., “Cybersecure Intelligent Sensor Framework for Smart Buildings: AI-Based Intrusion Detection and Resilience Against IoT Attacks,” Sensors, vol. 25, no. 24, p. 7680, 2025, doi: https://doi.org/10.3390/s25247680.
[27] M. A. Siam et al., “AI-Driven Cyber Threat Intelligence Systems: A National Framework for Proactive Defense Against Evolving Digital Warfare,” Int. J. Comput. Exp. Sci. Eng., vol. 11, no. 3, 2025, doi: https://doi.org/10.22399/ijcesen.3793.
[28] J. Hassan et al., “Blockchain Integration in Management Information Systems: A Decentralized Approach to Strengthening Cybersecurity and Data Integrity,” 2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, pp. 1–7, 2025. doi: https://doi.org/10.1109/ICECET63943.2025.11472020.