Research on Multi-Printer Intelligent Scheduling and State-Aware Methods for the Campus Unattended Cloud Printing Platform “Zhiyun Yiyin”

DOI: https://doi.org/10.62517/jbdc.202601132

Author(s)

Zijie Yi, Baohua Ning, Weiping Ning, Xiaomei Yang*, Ziguang Lu

Affiliation(s)

Guangxi Vocational Normal University, Nanning, Guangxi, China *Corresponding Author

Abstract

With the continuous advancement of smart campus development, campus printing services have placed higher demands on unattended operation, intelligent management, and high-concurrency processing. Traditional cloud printing systems still suffer from low scheduling efficiency and unbalanced resource utilization in multi-printer collaborative scheduling and device-state awareness. To address these issues, this paper investigates the campus-oriented unattended intelligent cloud printing platform Zhiyun Yiyin and proposes a multi-printer intelligent scheduling method that integrates print-task characteristics with device-state awareness. Specifically, the proposed method constructs a printer state-awareness model to obtain real-time operational status information and dynamically allocates print tasks according to task priority and device load, thereby improving overall system efficiency and stability. Based on the existing architecture of the campus unattended cloud printing platform, the functional modules for multi-printer intelligent scheduling and state awareness were implemented and experimentally validated. The results show that the proposed method outperforms traditional scheduling strategies in terms of print-task response time, device load balancing, and system stability. It can effectively satisfy high-concurrency printing demands in campus scenarios and demonstrates practical applicability.

Keywords

Unattended Campus Printing; Printer State Awareness; Multi-Printer Scheduling; Parallel Printing; Load Balancing

References

[1]Li Shi, Du Hongbo, Sun Jiaqi, et al. Print Terminal Based on “Internet + Sharing with Cloud”. Software, 2020, 41 (07): 72-75. [2]Lin Q, Liu J, Tretter D. Printing in a Digital Age. IEEE MultiMedia, 2010, DOI: 10.1109/MMUL.2010.84. [3]Hao Lijiang, Tian Luyun, Sun Peng, et al. Task Scheduling Algorithm for Intelligent Interpretation of Remote Sensing Data Based on Heterogeneous Platforms. Journal of Electronics & Information Technology, 2025, 47 (12): 4742-4753. [4]Suman Lata, Dheerendra Singh, Sukhpreet Singh. A Hybrid Approach for Cloud Load Balancing Optimization. Journal of Electrical Systems, 2024, 20 (9s): 1-10. [5]Athokpam Bikramjit Singh, Rio D’souza. A Hybrid Approach of Load Balancing in Cloud Computing by Optimization of Metaheuristic Techniques: An Execution Assessment. International Journal of Engineering Research in Electronics and Communication Engineering, 2022, 9 (11): 1-8. [6]Yousef Sanjalawe, Salam R. Al-Emari, Salam Fraihat, Sharif Naser Makhadmeh. AI-driven job scheduling in cloud computing: a comprehensive review. Artificial Intelligence Review, 2025, DOI: 10.1007/s10462-025-11208-8. [7]Yamari S., Benlahmar E. H. AI-Based Load Balancing in Cloud Computing: A Systematic Review and Future Directions. Mathematical Modeling and Computing, 2026, DOI: 10.23939/mmc2026.01.102. [8]Simaiya S, Lilhore U K, Sharma Y K, et al. A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques. Science & Technology - Other Topics, 2024, DOI: 10.1038/s41598-024-51466-0. [9]Li T., Ying S., Zhao Y., et al. Batch Jobs Load Balancing Scheduling in Cloud Computing Using Distributional Reinforcement Learning. IEEE Transactions on Parallel and Distributed Systems, 2023, DOI: 10.1109/TPDS.2023.3334519. [10]Albalawi N. Dynamic scheduling strategies for cloud-based load balancing in parallel and distributed systems. Journal of Cloud Computing, 2025, DOI: 10.1186/s13677-025-00757-6. [11]Wang J., Cheng W., Zhang W. AoI-Aware Resource Allocation for Smart Multi-QoS Provisioning. IEEE Systems Journal, 2024, DOI: 10.1109/JSYST.2024.3519536. [12]Wang Y., Liu Q., Zhang D., et al. Adaptive Resource Scheduling and Management Optimization in Digital Education with Compute First Networking. IEEE Transactions on Consumer Electronics, 2025, DOI: 10.1109/TCE.2025.3581902. [13]Chayon M. H. R., Shouvo N. H., Mamun M. A. A. An Efficient Scheduling Algorithm Based on Buffer Status and Queue Length to Satisfy the Real-time Users in 5G. 2024 27th International Conference on Computer and Information Technology (ICCIT), 2024, DOI: 10.1109/ICCIT64611.2024.11022462. [14]Sheikh Umar Mushtaq, Sophiya Sheikh, Sheikh Mohammad Idrees. Enhanced priority based task scheduling with integrated fault tolerance in distributed systems. International Journal of Cognitive Computing in Engineering, 2025, 6: 152-169.