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http://dx.doi.org/10.9708/jksci.2021.26.04.113

Forecasting Chemical Tanker Freight Rate with ANN  

Lim, Sangseop (Div. of Navigation Convergence Studies, Korea Maritime and Ocean University)
Kim, Seokhun (Dept. of Electronic Commerce, PaiChai University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.4, 2021 , pp. 113-118 More about this Journal
Abstract
In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.
Keywords
Chemical Tanker; Shipping Freight rate; Artificial Neural Network; ARIMA; Forecasting;
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