Whale Optimized Distributed Computing Data Lake for Energy Storage
DOI:
https://doi.org/10.69996/jcai.2024022Keywords:
Whale Optimization, Distributed Computing, Seahorse Optimization, Classification, Resource Allocation, Lake ArchitectureAbstract
This paper presents the Whale Seahorse Optimization Distributed Computing (WSODS)
algorithm, a novel approach that combines the Whale Optimization Algorithm (WOA) and Seahorse
Optimization Algorithm (SOA) within a distributed computing framework. WSODS aims to address
complex optimization challenges across various domains, including power storage systems and data lake
architectures. The algorithm's performance was evaluated based on key metrics such as data processing
time, system throughput, resource utilization, and scalability. The evaluation results indicate that WSODS
significantly enhances system performance. In the context of power storage systems, WSODS improves
energy efficiency, storage capacity, charging and discharging rates, and round-trip efficiency. For data
lake architectures, WSODS achieves lower data processing times, higher system throughput, and
competitive resource utilization while demonstrating good scalability with increasing data loads. The
evaluation results indicate that WSODS significantly enhances system performance. In the context of
power storage systems, WSODS improves energy efficiency from 92% to 98%, storage capacity from 480
MWh to 500 MWh, charging rate from 45 MW to 55 MW, discharging rate from 55 MW to 64 MW, and
round-trip efficiency from 88% to 94% over 100 iterations. For data lake architectures, WSODS achieves
lower data processing times (450 seconds compared to 600 seconds for GA), higher system throughput
(75 MB/s compared to 55 MB/s for GA), and competitive resource utilization (80% CPU and 65%
memory), while demonstrating good scalability with processing time for a 2x data load at 920 seconds
compared to 1250 seconds for GA. These findings suggest that WSODS is a versatile and robust
optimization tool capable of driving advancements in energy efficiency, data analytics, and distributed
computing. Further research and real-world applications are recommended to fully explore its potential
and capabilities.
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