A Review on Task Offloading using Meta-Heuristic Algorithms on Fog Computing DOI
Zulfiqar Ali Khan, Izzatdin Abdul Aziz, Nurul Aida Osman

et al.

Published: Dec. 13, 2023

Language: Английский

A novel energy-based task scheduling in fog computing environment: an improved artificial rabbits optimization approach DOI
Reyhane Ghafari, N. Mansouri

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(6), P. 8413 - 8458

Published: April 10, 2024

Language: Английский

Citations

9
Independent task scheduling algorithms in fog environments from users’ and service providers’ perspectives: a systematic review DOI
Abdulrahman K. Al-Qadhi, Rohaya Latip, Raymond Chiong

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 28, 2025

Language: Английский

Citations

1
D3QN-based Multi-Priority Computation Offloading for Time-Sensitive and Interference-Limited Industrial Wireless Networks DOI
Chi Xu, Peifeng Zhang, Haibin Yu

et al.

IEEE Transactions on Vehicular Technology, Journal Year: 2024, Volume and Issue: 73(9), P. 13682 - 13693

Published: April 11, 2024

Industrial wireless networks (IWNs) are generally time-sensitive and interference-limited to guarantee real-time reliability for critical industrial tasks. However, the highconcurrent access of heterogeneous tasks poses great challenges IWNs which resource-limited. By employing multi-access edge computing (MEC) enhance capability, this paper proposes a multi-priority computation offloading scheme realize end-edge orchestrated based on deep reinforcement learning. Specifically, we study general scenario that multiple end devices offload MEC-enhanced base stations cooperatively accomplish complex work. fully considering different task deadlines, capabilities, maximum transmit power peak co-channel interference power, formulate an overall delay minimization problem with respect decisions, ratios powers. Due non-convexity problem, reformulate it by Markov decision process design priority-driven reward, where priorities assigned according deadline requirements. To approximate optimum solution in explosive state space, employ double dueling architectures basis Q-network (namely D3QN), propose D3QN-based (D3QN-MPCOS). Extensive experiments performed validate suitability superiority D3QN-MPCOS IWNs, eight benchmark schemes compared. The results show can converge higher reward smaller than other schemes, satisfy requirements under constraints.

Language: Английский

Citations

4
IPAQ: a multi-objective global optimal and time-aware task scheduling algorithm for fog computing environments DOI
Man Qi, Xiaochun Wu, Keke Li

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(2)

Published: Jan. 8, 2025

Language: Английский

Citations

0
Deep reinforcement learning-based scheduling in distributed systems: a critical review DOI

Zahra Jalali Khalil Abadi,

N. Mansouri, Mohammad Masoud Javidi

et al.

Knowledge and Information Systems, Journal Year: 2024, Volume and Issue: 66(10), P. 5709 - 5782

Published: June 26, 2024

Language: Английский

Citations

3
Reinforcement learning-based solution for resource management in fog computing: A comprehensive survey DOI
Reyhane Ghafari, N. Mansouri

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127214 - 127214

Published: March 1, 2025

Language: Английский

Citations

0
Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment DOI Creative Commons

Muhammad Saad Sheikh,

Rabia Noor Enam,

Rehan Qureshi

et al.

Frontiers in Computer Science, Journal Year: 2023, Volume and Issue: 5

Published: Dec. 14, 2023

Fog Computing has emerged as a pivotal technology for enabling low-latency, context-aware, and efficient computing at the edge of network. Effective task scheduling plays vital role in optimizing performance fog systems. Traditional algorithms, primarily designed centralized cloud environments, often fail to cater dynamic, heterogeneous, resource-constrained nature nodes. To overcome these limitations, we introduce sophisticated machine learning-driven methodology that adapts allocation ever-changing environment's conditions. Our approach amalgamates K-Means clustering algorithm enhanced with fuzzy logic, robust unsupervised learning technique, efficiently group nodes based on their resource characteristics workload patterns. The proposed method combines capabilities K-means adaptability logic dynamically allocate tasks By leveraging techniques, demonstrate how can be intelligently allocated nodes, resulting reducing execution time, response time network usage. Through extensive experiments, showcase effectiveness our dynamic environments. Clustering proves time-effective identifying groups jobs per virtual (VM) efficiently. model evaluate approach, have utilized iFogSim. simulation results affirm showcasing significant enhancements reduction, minimized utilization, improved when compared existing non-machine methods within iFogSim framework.

Language: Английский

Citations

8
Fuzzy Reinforcement Learning Algorithm for Efficient Task Scheduling in Fog-Cloud IoT-Based Systems DOI
Reyhane Ghafari, N. Mansouri

Journal of Grid Computing, Journal Year: 2024, Volume and Issue: 22(4)

Published: Sept. 23, 2024

Language: Английский

Citations

2
Meta-Heuristic Scheduling: A Review on Swarm Intelligence and Hybrid Meta-Heuristics Algorithms for Cloud Computing DOI

Samah Jomah,

S. Aji

Operations Research Forum, Journal Year: 2024, Volume and Issue: 5(4)

Published: Oct. 8, 2024

Language: Английский

Citations

2
A Grouping-Based Multi-task Scheduling Strategy with Deadline Constraint on Heterogeneous Edge Computing DOI
Xiaoyong Tang,

Wenbiao Cao,

Tan Deng

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 468 - 483

Published: Jan. 1, 2024

Language: Английский

Citations

0