Energy-Efficient and Secure Load Balancing Technique for SDN-Enabled Fog Computing

Jagdeep Singh, Parminder Singh (), El Mehdi Amhoud and Mustapha Hedabou
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Jagdeep Singh: School of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India
Parminder Singh: School of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India
El Mehdi Amhoud: School of Computer Science, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco
Mustapha Hedabou: School of Computer Science, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco

Sustainability, 2022, vol. 14, issue 19, 1-22

Abstract: The number of client applications on the fog computing layer is increasing due to advancements in the Internet of Things (IoT) paradigm. Fog computing plays a significant role in reducing latency and enhancing resource usage for IoT users’ tasks. Along with its various benefits, fog computing also faces several challenges, including challenges related to resource overloading, security, node placement, scheduling, and energy consumption. In fog computing, load balancing is a difficult challenge due to the increased number of IoT devices and requests, which requires an equal load distribution throughout all available resources. In this study, we proposed a secure and energy-aware fog computing architecture, and we implemented a load-balancing technique to improve the complete utilization of resources with an SDN-enabled fog environment. A deep belief network (DBN)-based intrusion detection method was also implemented as part of the proposed techniques to reduce workload communication delays in the fog layer. The simulation findings showed that the proposed technique provided an efficient method of load balancing in a fog environment, minimizing the average response time, average energy consumption, and communication delay by 15%, 23%, and 10%, respectively, as compared with other existing techniques.

Keywords: fog computing; load balancing; software defined network; resource management; intrusion detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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