Hybrid Spotted Hyena based Load Balancing algorithm (HSHLB)

In this paper, we delve into the core of our proposed dynamic load balancing mechanism, the Hybrid Spotted Hyena based Load Balancing algorithm (HSHLB). We begin by presenting a comprehensive overview of the Spotted Hyena Optimization Algorithm (SHOA) and the fundamental behaviors it encompasses, namely migration and attacking. We then introduce the Load Balancing Algorithm (LB) and outline its principles, emphasizing its distinct characteristics. Recognizing the strengths and weaknesses of both SHOA and LB, we embark on a journey to hybridize these two optimization techniques, resulting in the potent HSHLB. We elucidate the intricate process of combining these algorithms, elucidating how HSHLB harnesses their respective strengths while mitigating their limitations. This hybridization is pivotal to the overarching goal of achieving dynamic load balancing within cloud computing environments. As we progress through this section, we provide invaluable insights into the inner workings of HSHLB, offering readers a comprehensive understanding of its algorithmic steps, parameters, and intricacies. For clarity and enhanced comprehension, we incorporate pseudocode or flowcharts to illustrate the practical implementation of HSHLB. In sum, Section 3 lays the foundation for the practical application of HSHLB in achieving dynamic load balancing, setting the stage for subsequent sections where we delve into implementation details, results, and analysis. HSHLB emerges as a promising solution to the multifaceted challenges of load balancing in cloud computing, leveraging the unique strengths of SHOA and LB to optimize resource allocation and enhance Quality of Service (QoS).

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Publication Details

Journal
Mesopotamian Journal of Big Data
Published
2024-11-02
DOI
https://doi.org/10.58496/mjbd/2024/014
Citations
1
Primary Topic
Embedded Systems and FPGA Design
Type
article
Field-Weighted Citation Impact
0.33
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article

Hybrid Spotted Hyena based Load Balancing algorithm (HSHLB)

Mohammed Ayad Alkhafaji, Omar A. Hammood, Mustafa Mahmood Akawee, Raed Abdulkareem Hasan et al.
1 citations
Mesopotamian Journal of Big Data
Embedded Systems and FPGA Design
0.33
article

Hybrid Spotted Hyena based Load Balancing algorithm (HSHLB)

Mohammed Ayad Alkhafaji, Omar A. Hammood, Mustafa Mahmood Akawee, Raed Abdulkareem Hasan, Omar Ahmed, Enas F. Aziz, Aws Saad Showket, Husniyah Jasim, Khalil Yasin
article en
1 citations

Abstract

In this paper, we delve into the core of our proposed dynamic load balancing mechanism, the Hybrid Spotted Hyena based Load Balancing algorithm (HSHLB). We begin by presenting a comprehensive overview of the Spotted Hyena Optimization Algorithm (SHOA) and the fundamental behaviors it encompasses, namely migration and attacking. We then introduce the Load Balancing Algorithm (LB) and outline its principles, emphasizing its distinct characteristics. Recognizing the strengths and weaknesses of both SHOA and LB, we embark on a journey to hybridize these two optimization techniques, resulting in the potent HSHLB. We elucidate the intricate process of combining these algorithms, elucidating how HSHLB harnesses their respective strengths while mitigating their limitations. This hybridization is pivotal to the overarching goal of achieving dynamic load balancing within cloud computing environments. As we progress through this section, we provide invaluable insights into the inner workings of HSHLB, offering readers a comprehensive understanding of its algorithmic steps, parameters, and intricacies. For clarity and enhanced comprehension, we incorporate pseudocode or flowcharts to illustrate the practical implementation of HSHLB. In sum, Section 3 lays the foundation for the practical application of HSHLB in achieving dynamic load balancing, setting the stage for subsequent sections where we delve into implementation details, results, and analysis. HSHLB emerges as a promising solution to the multifaceted challenges of load balancing in cloud computing, leveraging the unique strengths of SHOA and LB to optimize resource allocation and enhance Quality of Service (QoS).

Mesopotamian Journal of Big DataVol. 2024
University of Baghdad (IQ), University Of Fallujah (IQ), Northern Technical University (IQ)
Openalex Percentile: Top 40%
Embedded Systems and FPGA Design
0.33
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