The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
PureEdge AI-Driven Scheduling for Failover Management in Edge Computing Systems
Abstract
This paper presents a comparative analysis of three scheduling methods- round-robin, energy-aware, and pure AI-based -focusing on failover time, switch time, request handling efficiency, energy efficiency, and load balancing in edge computing environments. The PureEdge AI-based method leverages machine learning to dynamically optimize scheduling decisions based on energy availability and system states. The simulation results demonstrate the effectiveness of the proposed AI-based method in reducing failure over time, balancing load distribution, and improving energy efficiency.
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ACR [7], DTFR [19], and PureEdge [20] rely primarily on simulation-based validation.
“ACR [7], DTFR [19], and PureEdge [20], rely primarily on simulation-based validation”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [20]
The ODT system's failover is comparable to PureEdge (approximately 1.8 s).
“comparable to PureEdge (≈ 1.8 s) [20]”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [20]
PureEdge balances failover time against energy consumption.
“PureEdge [20] balances failover time against energy consumption”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [20]
Citations
Cites
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733 · cited 1×
“Edge computing has emerged as a critical paradigm for processing data closer to end users, reducing latency, and enhancing real-time decision making [1], [2].”
Edge Computing and Cloud Computing for Internet of Things: A Review
Andriulo, F. C., Fiore, M., Mongiello, M., Traversa, E., & Zizzo, V. (2024) · Informatics · doi:10.3390/informatics11040071 · Cited by 249
Nayak, S., Patgiri, R., Waikhom, L., & Ahmed, A. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.10.016 · Cited by 93
A comprehensive survey of energy-efficient computing to enable sustainable massive IoT networks
Alsharif, M. H., Kelechi, A. H., Jahid, A., Kannadasan, R., Singla, M. K., Gupta, J., & Geem, Z. W. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.01.067 · Cited by 82
Energy-Aware Resource Scheduling for Serverless Edge Computing
Aslanpour, M. S., Toosi, A. N., Cheema, M. A., & Gaire, R. (2022) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid54584.2022.00028 · Cited by 61
Gill, S. S., Tuli, S., Toosi, A. N., Cuadrado, F., Garraghan, P., Rami Bahsoon, A., Lutfiyya, H., Sakellariou, R., RANA, O. F., Dustdar, S., et al. (2020) · Journal of Systems and Software · doi:10.1016/j.jss.2020.110596 · Cited by 56
Energy efficient task scheduling for heterogeneous multicore processors in edge computing
Liu, Y., Qu, H., Chen, S., & Feng, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-92604-6 · Cited by 38
A survey of fault tolerant consensus in wireless networks
Zou, Y., Li, Y., Jing, G., Zhang, R., Xie, Z., Li, H., & Yu, D. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100202 · Cited by 32
In-depth analysis of fault tolerant approaches integrated with load balancing and task scheduling
Mushtaq, S. U., Sheikh, S., Idrees, S. M., & Malla, P. A. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-024-01798-5 · Cited by 26
Sahu, D. K., Nidhi, N., Chaturvedi, R. K., Prakash, S., Yang, T., Rathore, R. S., Wang, L., Tahir, S., & Bakhsh, S. T. (2025) · Scientific Reports · doi:10.1038/s41598-025-88197-9 · Cited by 24
Energy-Aware Scheduling in Edge Computing Based on Energy Internet
Zhang, Q., Lin, X., Hao, Y., & Cao, J. (2020) · IEEE Access · doi:10.1109/access.2020.3044932 · Cited by 22
Kim, D., & Rizal, S. (2024) · ICT Express · doi:10.1016/j.icte.2024.10.008 · Cited by 20
Edge Server Selection with Round-Robin-Based Task Processing in Multiserver Mobile Edge Computing
Hasan, K. F., & Yazıcı, M. A. (2025) · Sensors · doi:10.3390/s25113443 · Cited by 13
Survey on Secure Scientific Workflow Scheduling in Cloud Environments
Saeed, H. A., Al-Janabi, S. T. F., Yassen, E. T., & Aldhaibani, O. A. (2025) · Future Internet · doi:10.3390/fi17020051 · Cited by 10
PureNFT: A Blockchain-Based Ticketing System with Lightweight AI for Scalping Prevention
Haryadi, G. A., Zainudin, A., Lee, J., & Kim, D. (2025) · International Conference on Consumer Electronics · doi:10.1109/icce63647.2025.10930216 · Cited by 4
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Neagu, M., Serban, C. M., Hangan, A., & Sebestyen, G. (2026) · Future Internet · doi:10.3390/fi18030137 · Cited by 3
Metadata
- Authors
- Dong-Seong, K., & Syamsul, R.
- Affiliations
- Kumoh National Institute of Technology (KR)
- Kumoh National Institute of Technology,ICT Convergence Research Center,Gumi,South Korea
- Venue
- 2025 International Conference on Mobile, Military, Maritime IT Convergence (ICMIC)
- Publisher
- IEEE
- Published
- 2025-08-27
- Pages
- 133-136
- OpenAlex
- W4416799691
- Cited by
- 1
- References
- 15
- Citations by year
- 2026: 1
- FWCI
- 1.24
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Mobile, Military, Maritime IT Convergence (ICMIC) (publishedVersion)
- Funders
- MSIT(Ministry of Science and ICT), Korea
- National Research Foundation of Korea(NRF)
- Ministry of Education, Science and Technology (2018RIA6AIA03024003,50)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- edge computing, task scheduling, energy-aware scheduling, round robin scheduling, load balancing, energy efficiency, machine learning for scheduling
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