Research on Ship Data Big Data Parallel Scheduling Algorithm Based on Cloud Computing

• Published in: Journal of coastal research Vol. 94; no. sp1; pp. 535 - 539
• Main Authors: Li, Xin, Guo, Jingjing
• Format: Journal Article
• Language: English
• Published: Fort Lauderdale Coastal Education and Research Foundation 01.06.2019; Allen Press Publishing; Allen Press Inc
• Subjects: Accuracy; Adaptation; Algorithms; Big Data; Center of gravity; Cloud computing; Coastal engineering; Coastal inlets; Coastal research; Coasts; Computer centers; Data; Data management; Data processing; Energy consumption; Energy management; Fault diagnosis; Genetic algorithms; Gravity; Industrial development; Infrastructure; Optimization; parallel scheduling; Schedules; Scheduling; SECTION III. SHIPPING INDUSTRY; ship; Ships; Software services; Strategy; Sustainable development; Technical services
• ISSN: 0749-0208; 1551-5036
• DOI: 10.2112/SI94-106.1

Li, X., and Guo, J.-J., 2019. Research on ship data big data parallel scheduling algorithm based on cloud computing. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 535–539. Coconut Creek (Florida), ISSN 0749-0208. The traditional big data parallel scheduling algorithm has a small number of ships and the scheduling accuracy is very poor. In order to solve this problem, a new ship big data parallel scheduling algorithm based on cloud computing is studied, and the computing framework of the algorithm is constructed. The computing framework includes infrastructure services, software services and platform services. The scheduling process is discussed. The horizontal and vertical coordinates of each ship in the dimensional network are scheduled. A set of energy-aware dual-adaptation dynamic genetic scheduling strategy is constructed. The scheduling strategy is based on the traditional genetic algorithm, and sets the dual optimization targets with weights, which are the execution time of the task and the energy consumption of the infrastructure. By adjusting the optimization weight, the optimization center of gravity can be controlled. The scheduling strategy also fully considers the dynamics of the cloud computing environment and the traditional algorithm. The experimental results show that the cloud computing-based ship big data parallel scheduling algorithm can simultaneously schedule multiple ships, and the scheduling accuracy is very high. The algorithm has broad market development space and is worthy of promotion.