Blocked Job Scheduling and Redundant Computing Resource Allocation in Edge Computing Systems
Peng, Pei Rui, Yun Xu, Tianheng Zou, Yulong Chen, Xianfu Jiang, Xiaoyang Zarakovitis, Charilaos C. Guizani, Mohsen
Peng, Pei
Rui, Yun
Xu, Tianheng
Zou, Yulong
Chen, Xianfu
Jiang, Xiaoyang
Zarakovitis, Charilaos C.
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Edge computing is near end users and provides them with fast computing services. Compared to the cloud, edge provides services with low communication delays but can only service limited users due to constrained storage and computing resources. Thus, allocating more computing resources to some jobs will block the execution of others, and the edge sends them either to the cloud or back to the users. This paper focuses on minimizing the average system time by exploring blocked job scheduling and redundant computing resource allocation in the cloud-edge-user system. Since edge nodes often operate in highly unpredictable environments, we adopt the replication redundancy to use the resources to shorten the job execution time. First, we propose an approximate model to evaluate the average system time theoretically. Second, we analyze the optimal scheduling for the blocked jobs and the optimal resource allocation for the redundant computing resources. Finally, we propose an algorithm combining blocked job scheduling and redundant computing resource allocation. Simulation results show that the proposed model approximates the cloud-edge-user system well, and the combined algorithm significantly outperforms the other algorithms under different service time distributions.
Citation
P. Peng et al., "Blocked Job Scheduling and Redundant Computing Resource Allocation in Edge Computing Systems," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2025.3530478
Source
IEEE Internet of Things Journal
Conference
Keywords
Computational modeling, Cloud computing, Resource management, Delays, Internet of Things, Redundancy, Scheduling, Optimal scheduling, Approximation algorithms, Heuristic algorithms
Subjects
Source
Publisher
IEEE