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A case study of spark resource configuration and management for image processing applications

The world population is expected to reach an estimated 9.8 billion by 2050, necessitating substantial increases in food production. Achieving such increases will require large-scale application of computer informatics within the agricultural sector. In particular, application of informatics to crop breeding has the potential to greatly enhance our ability to develop new varieties quickly and economically. Achieving this potential, however, will require capabilities for analyzing huge volumes of data acquired from various field-deployed image acquisition technologies. Although numerous frameworks for big data processing have been developed, there are relatively few published case studies that describe user experiences with these frameworks in particular application science domains. 
 
In this paper, we describe our efforts to apply Apache Spark to three applications of initial interest within the Plant Phenotyping and Imaging Research Centre (P2IRC) at the University of Saskatchewan. We find that default Spark parameter settings do not work well for these applications. We carry out extensive performance experiments to investigate the impact of alternative Spark parameter settings, both for applications run individually and in scenarios with multiple concurrently executing applications. We find that optimizing Spark parameter settings is challenging, but can yield substantial performance improvements, particularly with concurrent applications, provided that the dataset characteristics are considered. This is a first step towards insights regarding Spark parameter tuning on these classes of applications that may be more generally applicable to broader ranges of applications.