A Modified Levy Flight Firefly-Based Approach to Optimize Turnaround Time in Fog Computing Environments DOI
Raj Mohan Singh, Geeta Sikka, Lalit Kumar Awasthi

et al.

IETE Journal of Research, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 11

Published: Aug. 11, 2024

The escalation of Internet Things (IoT) devices has led to increased data generation at the network edge that burdened cloud infrastructure in terms handling and processing data. This rapid adoption fog computing because its ability bring computation storage closer support for real-time applications services by reducing latency. One foremost challenges arena is minimizing turnaround time. research paper proposes a Modified Levy Flight Firefly Algorithm (MLFFA) optimize task scheduling environments. Specifically, objective minimize time tasks. Moreover, genetic operators like crossover mutation are also employed achieve an optimal balance between exploration exploitation. Experimental observations undertaken show proposed method improves average 55%, 22%, 13%, waiting 59%, 45%, 37%, energy consumption 19%, 7%, 4%, failure rate 50%, 28%, 7% compared existing studies, namely Load Balancing Optimization Strategy (LBOS), Technique Resource Allocation Management (TRAM), Fuzzy Golden Eagle (FGELB), respectively.

Language: Английский

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
Advances in Artificial Rabbits Optimization: A Comprehensive Review DOI

Ferzat Anka,

Nazim Agaoglu,

Sajjad Nematzadeh

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 7, 2024

Language: Английский

Citations

5
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
Multi‐Objective Workflow Scheduling in Cloud Using Archimedes Optimization Algorithm DOI Open Access
Shweta Kushwaha, Ravi Shankar Singh,

Krishna Prajapati

et al.

Concurrency and Computation Practice and Experience, Journal Year: 2025, Volume and Issue: 37(4-5)

Published: Feb. 10, 2025

ABSTRACT Cloud computing has changed the technology landscape for over a decade and led to an astounding growth in number of applications it may be used for. Consequently, there been significant spike demand improved algorithms schedule workflows efficiently. These were mostly concerned with heuristic, metaheuristic, hybrid approaches workflow scheduling that suffer from problem local optima entrapment. Due such heavy traffic on cloud resources, is still need less computationally complex approaches. In light this, this article proposes novel approach: multi‐objective Modified Local Escaping Archimedes Optimization (MLEAO) algorithm scheduling. This strategy involves initialization population through HEFT provide inclination towards solutions makespan while achieving cost‐efficient decision avoiding entrapment using escaping operation. To validate efficacy our approach, we conducted extensive experiments scientific as benchmarks. Through investigations, significantly makespan, cost, resource utilization, energy consumption. Moreover, effectiveness proposed approach also verified by performance metrics hypervolume, s‐metric, dominance relationships between state‐of‐the‐art

Language: Английский

Citations

0
Optimal Configuration Framework of Hybrid Renewable Energy Technologies-Based Hydrogen Energy Storage System Assessment using Enhanced Artificial Rabbit Algorithm DOI
Aykut Fatih Güven, Rizk M. Rizk‐Allah

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135408 - 135408

Published: March 1, 2025

Language: Английский

Citations

0
CLEMO: Cost, load, energy, and makespan-based optimized scheduler for internet of things applications in cloud-fog environment DOI
Ashutosh Kumar Singh,

Rohit Kumar Tiwari,

Sushil Kumar Saroj

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 124, P. 110377 - 110377

Published: April 25, 2025

Language: Английский

Citations

0
Energy Efficiency Analysis in IoT-Driven Computational Intelligence System using Meta-heuristic Optimization Algorithms DOI

Monika Ratnakar,

Ajay Kumar,

K. L. Ambashtha

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Abstract The rapid expansion of IoT devices across various domains has introduced significant energy consumption challenges, requiring innovative approaches for enhancing efficiency. This paper explores five well-known meta-heuristic optimization algorithms improving efficiency in IoT-driven computational intelligence systems. These key are: Gray Wolf Optimizer (GWO), Ant Colony Optimization (ACO), Particle Swarm (PSO), Genetic Algorithm (GA), and Artificial Bee (ABC). We have evaluated these their ability to minimize while maintaining optimal system performance. By analyzing energy-efficient strategies, the addresses critical issues such as dynamic workload management, resource constraints, communication overhead that are vital ecosystems characterized by limited resources. experimental results show GWO PSO outperformed others terms savings convergence speed, demonstrating potential sustainability networks. also discusses implications findings extending lifespan minimizing environmental impact, making a promising solution management.

Language: Английский

Citations

0
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
A Modified Levy Flight Firefly-Based Approach to Optimize Turnaround Time in Fog Computing Environments DOI
Raj Mohan Singh, Geeta Sikka, Lalit Kumar Awasthi

et al.

IETE Journal of Research, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 11

Published: Aug. 11, 2024

The escalation of Internet Things (IoT) devices has led to increased data generation at the network edge that burdened cloud infrastructure in terms handling and processing data. This rapid adoption fog computing because its ability bring computation storage closer support for real-time applications services by reducing latency. One foremost challenges arena is minimizing turnaround time. research paper proposes a Modified Levy Flight Firefly Algorithm (MLFFA) optimize task scheduling environments. Specifically, objective minimize time tasks. Moreover, genetic operators like crossover mutation are also employed achieve an optimal balance between exploration exploitation. Experimental observations undertaken show proposed method improves average 55%, 22%, 13%, waiting 59%, 45%, 37%, energy consumption 19%, 7%, 4%, failure rate 50%, 28%, 7% compared existing studies, namely Load Balancing Optimization Strategy (LBOS), Technique Resource Allocation Management (TRAM), Fuzzy Golden Eagle (FGELB), respectively.

Language: Английский

Citations

0