Development of an orchestration aid system for gridded crop growth simulations using Kubernetes

Abstract

Spatial simulations of crop growth under climate change have been limited to researchers who have access to the resources for high performance computing. The objective of this study was to develop an orchestration aid system for concurrent gridded simulations of crop growth, which would support the design of climate change adaptation options on crop production without expertise in distributed computing. The orchestration aid system was designed to help a user build a set of virtualized cluster computers using a simple input file, which would require little expertise in distributed computing, rather than manual configuration. The orchestration aid system, which was referred to as GROWLERS-kube, was implemented to launch multiple sets of gridded simulations using pods or containers managed by Kubernetes. As a case study, GROWLER-kube was executed using 16 Raspberry Pi 4 computers to perform 120 sets of the gridded simulations under diverse crop management options, including varying planting date and cultivar, for the period from 2001 to 2010. The wall time or the elapsed time for the given sets of the gridded simulation differed by configuration of virtualized cluster computers, such as the number of pods used for server and client nodes, although the total number of physical nodes were identical. For example, the wall time difference between virtualized cluster computer sets was about 28.9% when 15 worker nodes were used. In particular, the acceleration of the gridded simulations was at maximum using a large number of the virtualized cluster computers with a small number of nodes. It was found that the spatial distribution of planting dates and cultivars was similar to that of a previous study based on field experiments mostly in regions where rice is usually grown. These results suggest that GROWLERS-kube would facilitate the spatial assessment of climate change impact on crop production without considerable effort and expertise in distributed computing, which would aid a researcher to focus on the design of adaptation strategies.