Evolving geospatial applications: from silos and desktops to Microservices and DevOps

• Main Author: Gao, Bing
• Other Authors: Coady, Yvonne
• Format: Others
• Language: English; en
• Published: 2019
• Subjects: Kubernetes; Docker container; DevOps; Microservices; Satellite Imagery Processing; Cloud
• Online Access: http://hdl.handle.net/1828/10802

The evolution of software applications from single desktops to sophisticated cloud-based systems is challenging. In particular, applications that involve massive data sets, such as geospatial applications and data science applications are challenging for domain experts who are suddenly constructing these sophisticated code bases. Relatively new software practices, such as Microservice infrastructure and DevOps, give us an opportunity to improve development, maintenance and efficiency for the entire software lifecycle. Microservices and DevOps have become adopted by software developers in the past few years, as they have relieved many of the burdens associated with software evolution. Microservices is an architectural style that structures an application as a collection of services. DevOps is a set of practices that automates the processes between software development and IT teams, in order to build, test, and release software faster and increase reliability. Combined with lightweight virtualization solutions, such as containers, this technology will not only improve response rates in cloud-based solutions but also drastically improve the efficiency of software development. This thesis studies two applications that apply Microservices and DevOps within a domain-specific application. The advantages and disadvantages of Microservices architecture and DevOps are evaluated through the design and development on two different platforms---a batch-based cloud system, and a general purpose cloud environment. === Graduate