Hill Climbing Load Balancing Algorithm on Fog Computing

Author(s):  
Maheen Zahid ◽  
Nadeem Javaid ◽  
Kainat Ansar ◽  
Kanza Hassan ◽  
Muhammad KaleemUllah Khan ◽  
...  
Author(s):  
Roberto Beraldi ◽  
Claudia Canali ◽  
Riccardo Lancellotti ◽  
Gabriele Proietti Mattia

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2479 ◽  
Author(s):  
Hongyu Xiao ◽  
Zhenjiang Zhang ◽  
Zhangbing Zhou

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).


Author(s):  
Eder Pereira ◽  
Ivânia A. Fischer ◽  
Roseclea D. Medina ◽  
Emmanuell D. Carreno ◽  
Edson Luiz Padoin

Author(s):  
Youchan Zhu ◽  
Yingzi Wang ◽  
Weixuan Liang

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.


Author(s):  
Muhammad Junaid Ali ◽  
Nadeem Javaid ◽  
Mubariz Rehman ◽  
Muhammad Usman Sharif ◽  
Muhammad KaleemUllah Khan ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document