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Leveraging energy-efficient load balancing algorithms in fog computing - 2022


Research paper on Leveraging energy-efficient load balancing algorithms in fog computing

Research Area:  Fog Computing

Abstract:

Cloud computing and smart gadgets are the need of smart world these days. This often leads to latency and irregular connectivity issues in many situations. In order to overcome this issue, an emerging technique of fog computing is used for cloud and smart devices. A decentralized computing infrastructure in which all the elements, that is, storage, compute, data and the applications in use, are passed in an efficient and logical place between cloud and the data source, is called Fog computing. The cloud computing and services are generally extended by fog computing, which brings the power and advantages of data creation and data analysis at the network edge. Real-time location based services and applications with mobility support are enabled due to the physical proximity of users and high speed internet connection to the cloud. Fog computing is promoted with leveraging load balancing techniques so as to balance the load which is done in two ways, that is, static load balancing and dynamic load balancing. In this paper, different load balancing algorithms are discussed and their comparative analysis has been carried out. Round Robin load balancing is the simplest and easiest load balancing technique to be implemented in fog computing environments. The major problem of Source IP Hash load balancing algorithm is that each change can redirect to anyone with a different server, and thus, is least preferred in fog networks. The mechanisms to make energy efficient load balancing are also considered as the part of this paper.

Keywords:  
Energy-efficient
Load balancing algorithms
Fog computing
Cloud

Author(s) Name:   Simar Preet Singh, Rajesh Kumar, Anju Sharma, Anand Nayyar

Journal name:  Concurrency and Computation: Practice and Experience

Conferrence name:  

Publisher name:  Wiley

DOI:  10.1002/cpe.5913

Volume Information:  Volume 34, Issue 13