Research on Resource Scheduling Model of Ocean Cloud Platform Based on Competitive Genetic Algorithm

Abstract

High quality marine natural resource management cannot be achieved without the support of data and information. Given the unique nature of marine data, the processing of marine environmental data often involves long time series or large-scale processing work. For data processing work mainly focused on intensive computing, universal cloud platforms have prominent issues of low efficiency. Based on a comprehensive analysis of the native resource scheduling algorithms on the Hadoop platform and the characteristics of intensive computing in ocean data processing, this paper innovatively proposes a genetic algorithm task scheduling strategy based on a competitive model. The use of chaotic algorithm mechanism as the basis for genetic algorithm population initialization ensures that the solution space obtained during each initialization can be uniformly distributed, effectively solving the problem of genetic algorithm solving speed being greatly affected by the initialization population and population evolution measurement. In addition, in order to speed up rate of convergence, competition mechanism is introduced and an adaptive evolution model based on population competition is proposed. Through the actual verification and comparison of the built models, it is proved that the improved algorithm is superior to the traditional genetic algorithm in rate of convergence and stability of the convergence results, and has greatly improved the ability and efficiency of improving the resource scheduling of the ocean cloud platform.

Keywords

• ocean data processing
• cloud computing
• cloud services
• cloudsim
• task scheduling