Abstract
Cloud computing is a base platform for the distribution of large volumes of data and high-performance image processing on the Web. Despite wide applications in Web-based services and their many benefits, geo-spatial applications based on cloud computing technology are still developing. Auto-scaling realizes automatic scalability, i.e., the scale-out and scale-in processing of virtual servers in a cloud computing environment. This study investigates the applicability of auto-scaling to geo-based image processing algorithms by comparing the performance of a single virtual server and multiple auto-scaled virtual servers under identical experimental conditions. In this study, the cloud computing environment is built with OpenStack, and four algorithms from the Orfeo toolbox are used for practical geo-based image processing experiments. The auto-scaling results from all experimental performance tests demonstrate applicable significance with respect to cloud utilization concerning response time. Auto-scaling contributes to the development of web-based satellite image application services using cloud-based technologies.
Highlights
Cloud computing is considered an important paradigm in Information Technology (IT).The key features for cloud computing capabilities can be summarized as: on-demand self-service, broad network access, resource pooling, measured service and rapid elasticity
Elasticity, which refers to the extent to which resources provisioned by service providers change in relation to the changing user demand, allows service providers to maintain a high level of performance quality for application services
The purpose of this study is to investigate the applicability of auto-scaling schemes to geo-based image processing services through performance tests with respect to some practical remote sensing algorithms, in an OpenStack cloud computing environment
Summary
The key features for cloud computing capabilities can be summarized as: on-demand self-service, broad network access, resource pooling, measured service and rapid elasticity. The cloud service model uses virtual servers for managing, scheduling, communicating, networking and auto-scaling as the common layer to fulfill a cloud computing scheme [1]. Cloud-based application cases have been reviewed and we considered unresolved issues related to cloud platforms [2]. A technical review of auto-scaling for elastic cloud-based applications was provided, and the Gartner group describes auto-scaling as an automatic expansion or contraction of system capacity, and indicated that such a capacity is a commonly desired feature in cloud infrastructure as a service and platform as a service offering [4]. Auto-scaling refers to the significant capability of a cloud computing environment to utilize virtualized computing resources automatically
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