Comparing the performance between Virtual Machines and Containers using deep learning credit models

Abstract

The cloud computing paradigm utilizes neutralization to isolate data, workloads and network bandwidth in an elastic mode. The providers hence have historically used Virtual Machines (VM) to perform this workload isolation. However these virtual machines require huge resources and are costly to set-up and deploy. Containerization has of late been used as a different approach for neutralization albeit as a Software as a service approach. Docker containerization has been one of the most popular approaches and its a solution where containers all share the same resources and operating system as opposed to virtual machines which use hyper-visor technology to abstract hardware and operating systems. The study uses different cloud vendors to compare docker containers for response time, download time, CPU processing time as well as memory usage against virtual machines. The study did a performance comparison for Virtual Machines and Docker Containers in various cloud providers namely AWS, Google Cloud as well as Microsoft Azure cloud platforms. The dataset used included deep learning big data downloaded from Kaggle website and classified for loan defaulting with Keras and tensorflow python implementation frameworks. The comparisons do prove that docker containers are faster than KVMs and Xen virtual machines. The study also proves that by using Kubernetes framework for scaling the containers, the performance of the docker containers improves compared to the docker and bare metal as well as cloud frameworks.