Hybrid Gradient Descent Golden Eagle Optimization (HGDGEO) Algorithm-Based Efficient Heterogeneous Resource Scheduling for Big Data Processing on Clouds

Kumar, N. Jagadish; Balasubramanian, C.

doi:10.1007/s11277-023-10182-0

Cited by 12 publications

(3 citation statements)

References 28 publications

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“…Ilango R et al [43] proposed S2NA-GEO combined with a neural network learning algorithm. Later, a model for the uncertainty associated with renewable energy based on GEO was developed to relate the negative effects of variations in RES output for electric vehicles and intelligent charging [44].…”

Section: Related Work On Geomentioning

confidence: 99%

A Nonlinear Convex Decreasing Weights Golden Eagle Optimizer Technique Based on a Global Optimization Strategy

Deng,

Zhang,

et al. 2023

Applied Sciences

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A novel approach called the nonlinear convex decreasing weights golden eagle optimization technique based on a global optimization strategy is proposed to overcome the limitations of the original golden eagle algorithm, which include slow convergence and low search accuracy. To enhance the diversity of the golden eagle, the algorithm is initialized with the Arnold chaotic map. Furthermore, nonlinear convex weight reduction is incorporated into the position update formula of the golden eagle, improving the algorithm’s ability to perform both local and global searches. Additionally, a final global optimization strategy is introduced, allowing the golden eagle to position itself in the best possible location. The effectiveness of the enhanced algorithm is evaluated through simulations using 12 benchmark test functions, demonstrating improved optimization performance. The algorithm is also tested using the CEC2021 test set to assess its performance against other algorithms. Several statistical tests are conducted to compare the efficacy of each method, with the enhanced algorithm consistently outperforming the others. To further validate the algorithm, it is applied to the cognitive radio spectrum allocation problem after discretization, and the results are compared to those obtained using traditional methods. The results indicate the successful operation of the updated algorithm. The effectiveness of the algorithm is further evaluated through five engineering design tasks, which provide additional evidence of its efficacy.

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Section: Related Work On Geomentioning

confidence: 99%

A Nonlinear Convex Decreasing Weights Golden Eagle Optimizer Technique Based on a Global Optimization Strategy

Deng,

Zhang,

et al. 2023

Applied Sciences

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“…Among these approaches, biologically inspired optimization techniques have gained traction for their ability to tackle complex optimization problems by drawing inspiration from natural processes and phenomena 9 . Biologically inspired optimization techniques encompass a wide range of algorithms, including genetic algorithms, swarm intelligence, and artificial immune systems, which mimic the behavior of biological organisms and systems to solve optimization problems in diverse domains, including cloud computing and distributed systems 10–12 . These techniques leverage principles of evolution, cooperation, and adaptation to optimize data storage and processing in dynamic and heterogeneous computing environments.…”

Section: Introductionmentioning

confidence: 99%

“…Continued exploration of optimization algorithms can lead to breakthroughs that drive the future of data storage and processing technologies. Importantly, the efficient management of data has far‐reaching implications for society, impacting areas, such as healthcare, finance, transportation, and environmental monitoring 10 . By optimizing data storage and processing systems, we can unlock insights that drive positive societal outcomes and address pressing global challenges.…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence with a Chaotic Leader and a Salp algorithm: HDFS optimization for reduced latency and enhanced availability

Jagadish Kumar,

Dhinakaran,

Naresh Kumar

et al. 2024

Concurrency and Computation

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SummaryThe Hadoop distributed file system (HDFS) effectively manages data by segmenting it into blocks distributed across DataNodes in its cluster. While default block sizes in Hadoop 2.x and 1.x are 128 and 64 MB, respectively, they can be customized for larger files. HDFS ensures data reliability by replicating blocks across multiple DataNodes, but this can introduce high latency in cloud storage during heavy network traffic, particularly in big data processing. To address this, we introduce swarm intelligence with a chaotic leader and a salp (SI‐CLS) optimization algorithm. This algorithm reduces network traffic between racks in HDFS by optimizing block distribution. The SI‐CLS algorithm calculates a fitness value for each block, aiming to increase data availability and reduce latency. Performance metrics, including latency, data availability, and load balancing, indicate the effectiveness of SI‐CLS. Adopting this algorithm enhances HDFS performance, ensuring better data availability and lower latency, resulting in improved system reliability.

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MapReduce: A Big Data-Maintained Algorithm Empowering Big Data Processing for Enhanced Business Insights

Uprety,

Banarjee,

Kumar

et al. 2024

Lecture Notes in Networks and Systems

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Hybrid Gradient Descent Golden Eagle Optimization (HGDGEO) Algorithm-Based Efficient Heterogeneous Resource Scheduling for Big Data Processing on Clouds

Cited by 12 publications

References 28 publications

A Nonlinear Convex Decreasing Weights Golden Eagle Optimizer Technique Based on a Global Optimization Strategy

A Nonlinear Convex Decreasing Weights Golden Eagle Optimizer Technique Based on a Global Optimization Strategy

Swarm Intelligence with a Chaotic Leader and a Salp algorithm: HDFS optimization for reduced latency and enhanced availability

MapReduce: A Big Data-Maintained Algorithm Empowering Big Data Processing for Enhanced Business Insights

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