Adaptive Task Migration Strategy Based on Health Status and Resource Utilization Rate DOI

Yunxin Zhuang,

Zerui Zhen,

Fanqin Zhou

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 395 - 405

Published: Jan. 1, 2025

Language: Английский

Edge computing in future wireless networks: A comprehensive evaluation and vision for 6G and beyond DOI Creative Commons
Mustafa Ergen, Bilal Saoud, Ibraheem Shayea

et al.

ICT Express, Journal Year: 2024, Volume and Issue: 10(5), P. 1151 - 1173

Published: Aug. 17, 2024

Language: Английский

Citations

3
Secure workflow scheduling algorithm utilizing hybrid optimization in mobile edge computing environments DOI
Dileep Kumar Sajnani, Xiaoping Li,

Abdul Rasheed Mahesar

et al.

Computer Communications, Journal Year: 2024, Volume and Issue: 226-227, P. 107929 - 107929

Published: Aug. 23, 2024

Language: Английский

Citations

3
Mobile-aware placement: optimizing energy consumption and delay of data stream applications in edge computing DOI
Pengcheng Jin, Wu Yi, Jingyu Huang

et al.

Published: Jan. 9, 2025

With the rapid development of 5G and IoT, traditional cloud computing faces limitations due to increasing demands. Edge addresses these issues by offloading tasks nearby nodes, thereby improving efficiency user experience for latency-sensitive applications such as live video autonomous driving. However, balancing energy consumption latency in heterogeneous edge devices remains a challenge, particularly previous studies often overlook mobility. In this work, we tackle problem data stream processing (DSP) task placement within dynamic mobility scenarios, addressing challenges posed frequent movement. Additionally, introduce reinforcement learning approach enhance adaptability DSP tasks, allowing system effectively adjust changing patterns. Through simulation experiments, it is verified that compared with method, algorithm proposed paper optimizes delay while considering attributes, improves utilization rate each node.

Language: Английский

Citations

0
Research on computing task scheduling method for distributed heterogeneous parallel systems DOI Creative Commons

Xianzhi Cao,

Chong Chen, Shiwei Li

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 15, 2025

Abstract With the explosive growth of terminal devices, scheduling massive parallel task streams has become a core challenge for distributed platforms. For computing resource providers, enhancing reliability, shortening response times, and reducing costs are significant challenges, particularly in achieving energy efficiency through to realize green computing. This paper investigates heterogeneous flow problem minimize system consumption under time constraints. First, set independent tasks capable computation on terminals, is performed according computational capabilities each terminal. The modeled as mixed-integer nonlinear programming using Directed Acyclic Graph input model. Then, dynamic method based heuristic reinforcement learning algorithms proposed schedule flows. Furthermore, redundancy applied certain reliability analysis enhance fault tolerance improve service quality. Experimental results show that our can achieve improvements, by 14.3% compared existing approaches two practical workflow instances.

Language: Английский

Citations

0
Adaptive Task Migration Strategy Based on Health Status and Resource Utilization Rate DOI

Yunxin Zhuang,

Zerui Zhen,

Fanqin Zhou

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 395 - 405

Published: Jan. 1, 2025

Language: Английский

Citations

0