Optimization enabled deep learning methods for Container-based Cloud Computing Environment

Abstract

Cloud applications are now evolving towards a more granular micro-service paradigm, where fewer and simpler working elements are joined for providing end-to-end services, in response to the demand for agile development and administration. The increase in the utilization of container have gained more insight and helped in ensuring maximized and large portability with minimized overhead and rapid deployment in the cloud platform. But the rapid growth of container technology has introduced phenomenal changes in the management and automation of containers in the cloud computing environment. In specific, container resource allocation is the most potential challenge realized from the dimension of cloud providers since it possesses a direct impact on system performance and resource management. In this paper, a comprehensive review on Optimization enabled deep learning methods for Container-based Cloud Computing Environment is presented with its merits and limitations. It has presented the possible number of deep learning models-based container scheduling process that helps in significant load prediction in the cloud platform. This study also outlines the prospective research areas that might be explored moving forward with this implementation research.