TY - JOUR

T1 - A cache-based approach toward improved scheduling in fog computing

AU - Khan, O.A.

AU - Malik, S.U.R.

AU - Baig, F.M.

AU - Islam, S.U.

AU - Pervaiz, H.

AU - Malik, H.

AU - Ahmed, S.H.

N1 - This is the peer reviewed version of the following article: Khan, OA, Malik, SUR, Baig, FM, et al. A cache-based approach toward improved scheduling in fog computing. Softw: Pract Exper. 2021; 51: 2360– 2372. https://doi.org/10.1002/spe.2824 which has been published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1002/spe.2824 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2021/12/31

Y1 - 2021/12/31

N2 - Fog computing is a promising technique to reduce the latency and power consumption issues of the Internet of Things (IoT) ecosystem by enabling storage and computational resource close to the end-user devices with additional benefits such as improved execution time and processing. However, with an increase in IoT devices, the resource allocation and job scheduling became a complicated and cumbersome task due to limited and heterogeneous resources along with the locality restriction in such computing environment. Therefore, this paper proposes a cache-based approach for efficient resource allocation in fog computing environment, while maintaining the quality of service. The proposed algorithm is realized using iFogSim simulator and a comprehensive comparison is presented with the traditional First Come First Served and Shortest Job First policies. The performance evaluation revealed that with the proposed scheme the execution time, latency, processing delays and power consumption decreased by 38%, 11.1%, 6%, and 17.8%, respectively, as compared to those of the traditional schemes. 

AB - Fog computing is a promising technique to reduce the latency and power consumption issues of the Internet of Things (IoT) ecosystem by enabling storage and computational resource close to the end-user devices with additional benefits such as improved execution time and processing. However, with an increase in IoT devices, the resource allocation and job scheduling became a complicated and cumbersome task due to limited and heterogeneous resources along with the locality restriction in such computing environment. Therefore, this paper proposes a cache-based approach for efficient resource allocation in fog computing environment, while maintaining the quality of service. The proposed algorithm is realized using iFogSim simulator and a comprehensive comparison is presented with the traditional First Come First Served and Shortest Job First policies. The performance evaluation revealed that with the proposed scheme the execution time, latency, processing delays and power consumption decreased by 38%, 11.1%, 6%, and 17.8%, respectively, as compared to those of the traditional schemes. 

KW - cache

KW - cloud computing

KW - fog computing

KW - IoT

KW - job scheduling

KW - QoE

KW - QoS

KW - Electric power utilization

KW - Fog

KW - Green computing

KW - Internet of things

KW - Quality of service

KW - Resource allocation

KW - Scheduling

KW - Comprehensive comparisons

KW - Computational resources

KW - Computing environments

KW - Efficient resource allocation

KW - First come first served

KW - Heterogeneous resources

KW - Internet of thing (IOT)

KW - Processing delay

KW - Fog computing

U2 - 10.1002/spe.2824

DO - 10.1002/spe.2824

M3 - Journal article

VL - 51

SP - 2360

EP - 2372

JO - Software: Practice and Experience

JF - Software: Practice and Experience

SN - 0038-0644

IS - 12

ER -