Several fields of science have traditionally demanded large-scale workflow support, which requires thousands of central processing unit (CPU) cores. In order to support such large-scale scientific workflows, large-capacity cluster systems such as supercomputers are widely used. However, as users require a diversity of software packages and configurations, a system administrator has some trouble in making a service environment in real time. In this paper, we present a container-based cluster management platform and introduce an implementation case to minimize performance reduction and dynamically provide a distributed computing environment desired by users. This paper offers the following contributions. First, a container-based virtualization technology is assimilated with a resource and job management system to expand applicability to support large-scale scientific workflows. Second, an implementation case in which docker and HTCondor are interlocked is introduced. Lastly, docker and native performance comparison results using two widely known benchmark tools and Monte-Carlo simulation implemented using various programming languages are presented.