eISSN: 3087-4289 editor@aimjournals.com
All Journals
Submit your article

International Journal of Modern Computer Science and IT Innovations

Open Access Peer Review International
Open Access

Survey of Secure Workload Scheduling and Management in Cloud Computing Systems

DOI https://doi.org/10.55640/ijmcsit-v03i07-04
pdf
Vivek Sharma 1 ,
4 System Analyst, MITS Deemed University, India

Abstract

Cloud computing provides scalable, on-demand computing resources for a wide range of applications, making efficient workload scheduling and management essential for optimizing resource utilization, reducing execution time, and ensuring Quality of Service (QoS) and Service Level Agreement (SLA) compliance. However, the distributed and multi-tenant nature of cloud environments introduces security challenges such as unauthorized access, data breaches, malicious workloads, and resource misuse. This survey presents a comprehensive review of secure workload scheduling and management techniques in cloud computing systems. It discusses the fundamentals of workload scheduling, including cloud architectures, service models, deployment models, and workload management concepts. The survey reviews secure scheduling approaches, including static and dynamic scheduling, heuristic and metaheuristic algorithms, Artificial Intelligence (AI)- and Machine Learning (ML)-based techniques, and load balancing strategies. It also examines key security mechanisms such as authentication, authorization, encryption, trust management, workload isolation, and intrusion detection for protecting cloud workloads. Furthermore, the survey summarizes recent research contributions, identifies existing limitations, and highlights open challenges, including scalability, multi-cloud security, real-time scheduling, edge–cloud integration, green cloud computing, and Zero-Trust security. Finally, it outlines future research directions for developing intelligent, secure, and efficient workload scheduling frameworks in modern cloud computing environments.

Keywords

Cloud Computing, Secure Workload Scheduling, Management

References

J. B. Mehta, “Autonomous Workload Right-Sizing for Multi-Cloud Cost Optimization,” in 2026 International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET), Cairo, Egypt: IEEE, Apr. 2026, pp. 1–11. doi: 10.1109/ICAISET66439.2026.11541899.
S. K. Madishetty and G. N. M. V. Kumar, “Intelligent Surgical Tables: Real-Time Patient Monitoring and Autonomous Adjustment Using IoT, Cloud Computing, and Cybersecurity,” J. Comput. Anal. Appl., vol. 31, no. 02, pp. 803–809, Feb, Feb. 2023, doi: 10.48047/jocaaa.2023.31.02.50.
T. Shah, “Cloud-Based Data Warehousing for Marketing Agility: Lessons from FinTech Migrations to Snowflake and AWS,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 4, no. 4, March, pp. 642–652, Mar. 2024, doi: 10.48175/IJARSCT-16000B.
Mohit Mittal, “The Great Migration: Understanding the Cloud Revolution in IT,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 10, no. 6, pp. 2222–2228, Dec. 2024, doi: 10.32628/CSEIT2410612423.
S. Jain and D. Jain, “Artifact Comparison Analyzer: Evaluating Microservice Build Metrics for Performance and Efficiency Improvements,” in 2026 IEEE International Conference on AI Engineering and Innovations (AIEI), IEEE, Mar. 2026, pp. 1–6. doi: 10.1109/AIEI69164.2026.11497468.
R. Vasikarla, S. Kakkar, B. Makkena, and A. Kakkar, “An Enhanced Artificial Intelligence Framework for Cloud Resource Usage Anomaly Detection in Site Reliability Engineering,” in 2026 2nd International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), IEEE, Feb. 2026, pp. 1418–1424. doi: 10.1109/IC3ECSBHI67834.2026.11468993.
S. K. Malaraju and S. K. Madishetty, “AI-Augmented Compiler Optimization for Energyefficient Software Execution on Embedded Systems,” in 2025 14th International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, Uttar Pradesh, India: IEEE, 2025, pp. 1–6, November. doi: 10.1109/SMART66937.2025.11389313.
S. K. Anumula, N. Talluru, R. Gangavarapu, S. Gupta, and K. Tatavarthy, “Quantum-Inspired Computing: Classical Approaches to Machine Learning,” Quantum-Inspired Neural Networks.
D. Kanellopoulos and V. Sharma, “Dynamic Load Balancing Techniques in the IoT: A Review,” Symmetry (Basel)., vol. 14, no. 12, p. 2554, Dec. 2022, doi: 10.3390/sym14122554.
R. K. Kanneganti, “Security and Compliance Issues in Cloud-Based Deployments of Content and Workflow Management Systems,” Eastasouth J. Inf. Syst. Comput. Sci., vol. 2, no. 01, pp. 131–138, Aug. 2024, doi: 10.58812/esiscs.v2i01.1122.
A. Vafamehr, “Energy-aware cloud computing,” Electr. J., vol. 31, 2018, doi: 10.1016/j.tej.2018.01.009.
N. Adik, “The Rise of Open and Free Networks: A Community-Driven Paradigm for Decentralized Connectivity,” in 2025 IEEE First International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability (ICINVENTS), IEEE, Nov. 2025, pp. 1–9. doi: 10.1109/ICINVENTS64613.2025.11402224.
A. Warrier, “Hybrid Cloud iPaaS for Healthcare Digital Transformation: Bridging On-Premises and Cloud-Based Health Information Systems,” Int. Sci. J. Eng. Manag., vol. 2, no. 1, p. 7, 2023.
V. Sharma, “Cloud-Native 5G Deployments: Kubernetes and Microservices in Telco Networks,” Int. J. Innov. Res. Eng. Multidiscip. Phys. Sci., vol. 10, no. 3, pp. 1–8, May 2022, doi: 10.37082/IJIRMPS.v10.i3.232706.
C. Chen, Y. Li, G. Cao, and J. Zhang, “Research on Dynamic Scheduling Model of Plant Protection UAV Based on Levy Simulated Annealing Algorithm,” Sustainability, vol. 15, no. 3, p. 1772, Jan. 2023, doi: 10.3390/su15031772.
M. Kari, “A Comprehensive Performance Study of Hashing Algorithms: MD5, SHA-256, and SHA-512 using Java,” Int. J. Curr. Eng. Technol., vol. 13, no. 6, pp. 1–8, 2023.
S. D. Veeravalli, S. B. Venkata, A. Ashirova, R. MS, S. A. D. Poovaiah, and D. Turaev, “Breaking the Swarm Paradigm: Single Candidate Optimization for Cloud Task Allocation,” in 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3), Bhubaneswar, India: IEEE, 2025, pp. 1–7, July.
R. U. L. A. Yeruva, D. Singh, S. Suddala, and N. Bhatt, “Augmented Data Management: Enhancing Cache Performance and Cybersecurity through AI and Mobile Integration,” J. Comput. Mech. Manag., 2026.
Y. Muni, “Understanding the Evolution of Internet Protocols: An In-Depth Review of IPV4 and IPV6: A Comparative Review of Transition Challenges and Solutions,” Int. J. Adv. Res. Comput. Sci., vol. 16, no. 4, pp. 109–117, Aug. 2025, doi: 10.26483/ijarcs.v16i4.7307.
K. R. Kiran, “API Integration Barriers in Case-Based Process Management: A Review of Interoperability and Real-Time Response Constraints Outline,” Qual. Res., vol. 25, no. 3, pp. 21–48, 2025, doi: https://doi.org/10.5281/zenodo.16782616.
A. Joon, B. K. R. Janumpally, A. Gogineni, and P. Chatterjee, “Efficient Large-Scale Intrusion Identification and Prevention in Distributed Cloud Networks Using Artificial Intelligence,” in 2025 5th International Conference on Intelligent Technologies (CONIT), HUBBALI, India: IEEE, Jun. 2025, pp. 1–8. doi: 10.1109/CONIT65521.2025.11167760.
H. P. Cyril, N. D. Bhandarwar, S. Kumara, and S. Mathur, “Securing Containerised Applications Using Kubernetes-Based Orchestration in Software Development,” in 2026 International Conference on Intelligent Computing, Networks, and Security (IC-ICNS), IEEE, Mar. 2026, pp. 1–6. doi: 10.1109/IC-ICNS68863.2026.11537887.
V. Chaturvedi, “Modern Software Development with Java , Spring Boot , and Python : A Survey of Frameworks and Best Practices,” vol. 3, no. 4, pp. 188–197, 2023, doi: 10.56472/25832646/JETA-V3I8P121.
P. C. Pradnya H Desai, Parikshit Mahalle, Intelligent Access Control Schemes for the Internet of Everything: A Survey of Techniques, Challenges, and Future Directions. Springer Nature Switzerland, 2026.
J. B. Mehta, “An Autonomous Dependency And Failure-Propagation Modeling System For Security-Critical Distributed Enterprise Cloud Computing Environments,” 202641001713, 2026
R. Azmeera, “The Effects of Increased Software Errors on Software Product Functionality,” University of the Cumberlands ProQuest, 2025.
V. Gudur, C. Upadhyay, S. Sudarsan, R. Dandigam, A. Patel, and S. Tiwari, “Trust Deviation Metrics (TDM) for AI-Driven Cloud Orchestration,” in Proceedings of the 2026 9th International Conference on Information and Computer Technologies, New York, NY, USA: ACM, Mar. 2026, pp. 539–544. doi: 10.1145/3803291.3803293.
M. Mittal, “Federated Learning: An Intrusion Detection Privacy-Preserving Approach to Decentralized AI Model Training for IOT Security,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 07, no. 01, Jan. 2018, doi: 10.15662/IJAREEIE.2018.0701001.
S. Tyagi and A. Tyagi, “Evaluating the Performance with Deep Learning Techniques in Cybersecurity for Effective Threat Detection,” 2026, pp. 437–452. doi: 10.1007/978-981-96-8687-2_31.
S. C. -, “Risk Management in Advanced Persistent Threats (APTs) for Critical Infrastructure in the Utility Industry,” Int. J. Multidiscip. Res., vol. 3, no. 4, Aug. 2021, doi: 10.36948/ijfmr.2021.v03i04.34396.
P. K. Boyapati and S. T. R. Kandula, “High-Performance Distributed Deep Learning Using Adaptive Parallelism and Dynamic Workload Scheduling,” in 2026 14th International Symposium on Digital Forensics and Security (ISDFS), Boston, MA, USA: IEEE, Mar. 2026, pp. 01–06. doi: 10.1109/ISDFS69419.2026.11459016.
K. Rajammal and M. Chinnadurai, “Dynamic load balancing in cloud computing using predictive graph networks and adaptive neural scheduling,” Sci. Rep., vol. 15, no. 1, p. 22181, Jul. 2025, doi: 10.1038/s41598-025-97494-2.
D. S, E. Daniel, D. S, and R. V.K, “Deep learning-based workload prediction and resource provisioning for mobile edge-cloud computing in healthcare applications,” Sustain. Comput. Informatics Syst., vol. 47, p. 101176, Sep. 2025, doi: 10.1016/j.suscom.2025.101176.
L. Li and X. Gao, “Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction,” Appl. Sci., vol. 15, no. 5, p. 2347, Feb. 2025, doi: 10.3390/app15052347.
H. Chaudhary, G. Sharma, D. K. Nishad, and S. Khalid, “AI-enhanced modelling of queueing and scheduling systems in cloud computing,” Discov. Appl. Sci., vol. 7, no. 4, p. 276, Mar. 2025, doi: 10.1007/s42452-025-06755-2.
P. Nehra and N. Kesswani, “A workload prediction model for reducing service level agreement violations in cloud data centers,” Decis. Anal. J., vol. 11, p. 100463, Jun. 2024, doi: 10.1016/j.dajour.2024.100463.
V. R. Pillareddy and G. R. Karri, “MONWS: Multi-Objective Normalization Workflow Scheduling for Cloud Computing,” Appl. Sci., vol. 13, no. 2, p. 1101, Jan. 2023, doi: 10.3390/app13021101.

Similar Articles

1-10 of 58

You may also start an advanced similarity search for this article.