State Based Load Balancing Algorithm for Smart Grid Energy Management in Fog Computing

2018 ◽  
pp. 220-232
Author(s):  
Muhammad Junaid Ali ◽  
Nadeem Javaid ◽  
Mubariz Rehman ◽  
Muhammad Usman Sharif ◽  
Muhammad KaleemUllah Khan ◽  
...  
Keyword(s):  
Load Balancing ◽  
Smart Grid ◽  
Energy Management ◽  
Fog Computing ◽  
Load Balancing Algorithm

scholarly journals A Random Walk based Load Balancing Algorithm for Fog Computing

2020 ◽  
Author(s):  
Roberto Beraldi ◽  
Claudia Canali ◽  
Riccardo Lancellotti ◽  
Gabriele Proietti Mattia
Keyword(s):  
Random Walk ◽  
Load Balancing ◽  
Fog Computing ◽  
Load Balancing Algorithm

Hill Climbing Load Balancing Algorithm on Fog Computing

2018 ◽  
pp. 238-251 ◽  
Author(s):  
Maheen Zahid ◽  
Nadeem Javaid ◽  
Kainat Ansar ◽  
Kanza Hassan ◽  
Muhammad KaleemUllah Khan ◽  
...  
Keyword(s):  
Load Balancing ◽  
Fog Computing ◽  
Hill Climbing ◽  
Load Balancing Algorithm

scholarly journals GWS—A Collaborative Load-Balancing Algorithm for Internet-of-Things

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2479 ◽  
Author(s):  
Hongyu Xiao ◽  
Zhenjiang Zhang ◽  
Zhangbing Zhou
Keyword(s):  
Load Balancing ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Nash Bargaining Solution ◽  
Nash Bargaining ◽  
Bargaining Solution ◽  
Time Sharing ◽  
Work Stealing ◽  
The Game Theory ◽  
Load Balancing Algorithm

This paper firstly replaces the first-come-first-service (FCFS) mechanism with the time-sharing (TS) mechanism in fog computing nodes (FCNs). Then a collaborative load-balancing algorithm for the TS mechanism is proposed for FCNs. The algorithm is a variant of a work-stealing scheduling algorithm, and is based on the Nash bargaining solution (NBS) for a cooperative game between FCNs. Pareto optimality is achieved through the collaborative working of FCNs to improve the performance of every FCN. Lastly the simulation results demonstrate that the game-theory based work-stealing algorithm (GWS) outperforms the classical work-stealing algorithm (CWS).

scholarly journals An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3500 ◽  
Author(s):  
Adia Khalid ◽  
Sheraz Aslam ◽  
Khursheed Aurangzeb ◽  
Syed Haider ◽  
Mahmood Ashraf ◽  
...  
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Fog Computing ◽  
Sharing Economy ◽  
Management Approach ◽  
Management Problem ◽  
Small Community ◽  
New Era ◽  
Computationally Intensive

An unprecedented opportunity is presented by smart grid technologies to shift the energy industry into the new era of availability, reliability and efficiency that will contribute to our economic and environmental health. Renewable energy sources play a significant role in making environments greener and generating electricity at a cheaper cost. The cloud/fog computing also contributes to tackling the computationally intensive tasks in a smart grid. This work proposes an energy efficient approach to solve the energy management problem in the fog based environment. We consider a small community that consists of multiple smart homes. A microgrid is installed at each residence for electricity generation. Moreover, it is connected with the fog server to share and store information. Smart energy consumers are able to share the details of excess energy with each other through the fog server. The proposed approach is validated through simulations in terms of cost and imported electricity alleviation.

A Load Balancing Algorithm for Fog Computing Environments

2020 ◽  
pp. 65-77 ◽  
Author(s):  
Eder Pereira ◽  
Ivânia A. Fischer ◽  
Roseclea D. Medina ◽  
Emmanuell D. Carreno ◽  
Edson Luiz Padoin
Keyword(s):  
Load Balancing ◽  
Fog Computing ◽  
Computing Environments ◽  
Load Balancing Algorithm

Research on fog Resource Scheduling based on Cloud-fog Collaboration Technology in the Electric Internet of Things

2021 ◽  
Vol 14 ◽  
Author(s):  
Youchan Zhu ◽  
Yingzi Wang ◽  
Weixuan Liang
Keyword(s):  
Internet Of Things ◽  
Load Balancing ◽  
Response Time ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Resource Scheduling ◽  
Previous Method ◽  
Collaboration Technology ◽  
Iot Devices ◽  
Load Balancing Algorithm

Background: With the further development of electric Internet of things (eIoT), IoT devices in the distributed network generate data with different frequencies and types. Objective: Fog platform is located between the smart collected terminal and cloud platform, and the resources of fog computing are limited, which affects the delay of service processing time and response time. Methods: In this paper, an algorithm of fog resource scheduling and load balancing is proposed. First, the fog devices divide the tasks into high or low priority. Then, the fog management nodes cluster the fog nodes through K-mean+ algorithm and implement the earliest deadline first dynamic (EDFD) task scheduling algorithm and De-REF neural network load balancing algorithm. Results: We use tools to simulate the environment, and the results show that this method has strong advantages in -30% response time, -50% scheduling time, delay, -50% load balancing rate and energy consumption, which provides a better guarantee for eIoT. Conclusion: Resource scheduling is important factor affecting system performance. This article mainly addresses the needs of eIoT in terminal network communication delay, connection failure, and resource shortage. And the new method of resource scheduling and load balancing is proposed, The evaluation was performed and proved that our proposed algorithm has better performance than the previous method, which brings new opportunities for the realization of eIoT.

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