Dynamic Traffic Engineering for Cooperative Fog-Cloud Environment: Trade-Off Analysis of Cost and Utilization Under Different Load Conditions DOI
Md. Rahinur Rahman, Mirza Mohd Shahriar Maswood

Published: Jan. 1, 2023

Fog computing has become an attractive method for different IoT (Internet of Things) applications that require low latency and location awareness. It provides by bringing computational power to the edge or nearer traffic generators’ a network works as perfect complement cloud computing. Though there are many advantages fog computing, due limitations resources (CPU processing capacity, bandwidth, memory, backup) nodes, framework combating these is highly desired. In this work, we formulate optimization model cooperative environment dealing with dynamic traffic. We analyzed how arrival rates impact bandwidth costs, link utilization, server resource utilization. By adopting techniques rates, utilization layer higher than resources, shown in paper. also figured out blocking within acceptable range (0-15%). Finally, identified driving factors associated blocking, case, shortages (network resources), which responsible generating our network.

Language: Английский

A dynamic weight–assignment load balancing approach for workflow scheduling in edge-cloud computing using ameliorated moth flame and rock hyrax optimization algorithms DOI
Mustafa Ibrahim Khaleel

Future Generation Computer Systems, Journal Year: 2024, Volume and Issue: 155, P. 465 - 485

Published: Feb. 27, 2024

Language: Английский

Citations

9
Design Cloud Computing to Monitor and Controller for High Voltage Networks 400 KV DOI Creative Commons
H. Khalil, Laith Ali Abdul-Rahaim,

Shamam Alwash

et al.

Cognitive Robotics, Journal Year: 2025, Volume and Issue: 5, P. 192 - 200

Published: Jan. 1, 2025

Language: Английский

Citations

0
Integration of Smart Grid with Industry 5.0: Applications, Challenges and Solutions DOI Creative Commons

Sunawar Khan,

Tehseen Mazhar, Tariq Shahzad

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: unknown, P. 100031 - 100031

Published: Dec. 1, 2024

Language: Английский

Citations

3
NS-OWACC: nature-inspired strategies for optimizing workload allocation in cloud computing DOI
Miaolei Deng, Umer Nauman, Zhang Yu-hong

et al.

Computing, Journal Year: 2024, Volume and Issue: 107(1)

Published: Nov. 27, 2024

Language: Английский

Citations

1
RWRR: Remind Weighted Rounding Robin for Load Balancing in Fog Computing DOI

Samah Ali,

Raaid Alubady

Published: Nov. 23, 2023

The healthcare environment is one of the applications that require real-time monitoring to immediately process. Fog computing works in a and offers connected devices for processing data with low latency compared cloud model. Load balancing an important term fog avoids situations overload underload nodes. Many Quality Service (QoS) metrics such as cost, response time, throughput, resource utilization, performance can be improved by load balancing. In this paper, we proposed mechanism called Remind Weighted Round Robin (RWRR) algorithm enhance QoS tasks appropriate node based on capabilities will assigned algorithm. applied order system environment. Results demonstrate it enhances overall 20.05%, average time 120.25ms when related work.

Language: Английский

Citations

3
Optimizing Resource Allocation for Energy Efficiency in Fog Cloud Computing Environments DOI
Mandeep Kaur, Rajni Aron,

Shriya Seth

et al.

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), Journal Year: 2024, Volume and Issue: unknown, P. 538 - 542

Published: April 6, 2024

Fog computing has become the primary paradigm for IoT applications as it meets low-latency needs of growing number applications. However, servers can get overwhelmed due to high demand fog resources in several complements cloud computing, only processes user requests near them. Distributing tasks evenly across all nodes layer helps achieve optimal task processing. Load balancing fog-cloud environment aids diminishing energy use. In this article, architecture named "EcoFogLoad Architecture" been proposed balance workload among layer. Along with this, "Energy-Efficient Workload Optimization (EEWO)" algorithm optimize use at terms cost, time delay and consumption. iFogSim used execute obtain experimental results. The results approach are compared those other existing algorithms. facilitates resource utilization, reducing latency improving service quality. article concludes by presenting potential avenues future research.

Language: Английский

Citations

0
Hybrid Metaheuristic Algorithms for Resource Allocation in Fog Computing Environments DOI
Pranav Kumar,

Harpreet S. Bhatia,

Anurag Shrivastava

et al.

Published: Feb. 21, 2024

This study aims to investigate the feasibility concerns of metaheuristic algorithms involving a hybridisation among GPSO, Adventure, ACO-GA and FDE; for asset allocation with regard fog computing context. These evaluations were based on joining speed, arrangement quality, flexibility strength in wide testing. Comparative analysis was performed, execution their related works within field. It is inferred that demonstrates fantastic meeting speed; code needs 450 cycles do this well has high greatness or fitness zero. 92ocuments GPSO FDE are closely proximate, showing competitive coalescence along design optimization. Adventure programs feature slightly less intense encounters but present dynamic exploration-exploitation prospects. In case if adaptability, trumps score 92% highlighting its resilience larger datasets. Stability reveals have little deviation folds, stability. The discoveries emphasize nuanced qualities each algorithm, giving profitable bits knowledge professionals computing. results contribute progressing talk allotment, directing future research towards refinement application hybrid calculations energetic situations.

Language: Английский

Citations

0
Efficient Load Balancing Algorithms for Edge Computing in IoT Environments DOI

Ankita Nainwal,

Muntather Almusawi,

Saloni Bansal

et al.

Published: May 9, 2024

Language: Английский

Citations

0
Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture DOI Open Access

Prasanna Kumar Kannughatta Ranganna,

Siddesh Gaddadevara Matt,

Chin‐Ling Chen

et al.

Computers, materials & continua/Computers, materials & continua (Print), Journal Year: 2024, Volume and Issue: 80(2), P. 2557 - 2578

Published: Jan. 1, 2024

In recent decades, fog computing has played a vital role in executing parallel computational tasks, specifically, scientific workflow tasks. cloud data centers, takes more time to run applications. Therefore, it is essential develop effective models for Virtual Machine (VM) allocation and task scheduling environments. Effective scheduling, VM migration, allocation, altogether optimize the use of resources across different nodes. This process ensures that tasks are executed with minimal energy consumption, which reduces chances resource bottlenecks. this manuscript, proposed framework comprises two phases: (i) using fractional selectivity approach (ii) by proposing an algorithm name Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO). The FSCPSO integrates concepts chaos theory fitness sharing effectively balance both global exploration local exploitation. enables wide range solutions leads total cost makespan, comparison other traditional optimization algorithms. algorithm's performance analyzed six evaluation measures namely, Load Balancing Level (LBL), Average Resource Utilization (ARU), cost, response time. relation conventional algorithms, achieves higher LBL 39.12%, ARU 58.15%, 1175, makespan 85.87 ms, particularly when evaluated 50

Language: Английский

Citations

0
Improved deep network‐based load predictor and optimal load balancing in cloud‐fog services DOI
Shubham Singh, Amit Kumar Mishra, Siddhartha Kumar Arjaria

et al.

Concurrency and Computation Practice and Experience, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 4, 2024

Summary Cloud computing is commonly utilized in remote contexts to handle user demands for resources and services. Each assignment has unique processing needs that are determined by the time it takes complete. However, if load balancing not properly managed, effectiveness of may suffer dramatically. Consequently, cloud service providers have emphasize rapid precise as well proper resource supply. This paper proposes a novel enhanced deep network‐based predictor cloud‐fog In prior, workload predicted using network called Multiple Layers Assisted LSTM (MLA‐LSTM) model considers capacity virtual machine (VM) task input predicts target label underload, overload equally balanced. According this prediction, optimal performed through hybrid optimization named Osprey Pelican Optimization Algorithm (OAPOA) while taking into account several parameters such makespan, execution cost, consumption, server load. Additionally, process known migration carried out, which machines with tasks assigned underload tasks. applied optimally via OAPOA strategy under consideration constraints including cost efficiency.

Language: Английский

Citations

0