Exploring the support for high performance applications in the container runtime environment

Abstract

IntroductionCloud computing is being used for innumerable applications these days. The end-users vary from naive clients to expertised technicians. Cloud is a pool of resources shared among number of users [1]. Presently, in the world of cloud computing, it is the era of XaaS (Anything-as-a-Service) which means that the providers offer a wide variety of services [2,3]. One of the most recent services provided through the cloud is high performance computing (HPC) environments for the complex applications. Virtualization is the technology which enables users to share a single entity among a group of users. AbstractCloud computing is the driving power behind the current technological era. Virtualization is rightly referred to as the backbone of cloud computing. Impacts of virtualization employed in high performance computing (HPC) has been much reviewed by researchers. The overhead in the virtualization layer was one of the reasons which hindered its application in the HPC environment. Recent developments in virtualization, especially the OS container based virtualization provides a solution that employs a lightweight virtualization layer and promises lesser overhead. Containers are advantageous over virtual machines in terms of performance overhead which is a major concern in the case of both data intensive applications and compute intensive applications. Currently, several industries have adopted container technologies such as Docker. While Docker is widely used, it has certain pitfalls such as security issues. The recently introduced CoreOS Rkt container technology overcomes these shortcomings of Docker. There has not been much research on how the Rkt environment is suited for high performance applications. The differences in the stack of the Rkt containers suggest better support for high performance applications. High performance applications consist of CPU-intensive and data-intensive applications. The High Performance Linpack Library and the Graph500 are the commonly used computation intensive and data-intensive benchmark applications respectively. In this work, we explore the feasibility of this inter-operable Rkt container in high performance applications by running the HPL and Graph500 applications and compare its performance with the commonly used container technologies such as LXC and Docker containers. Martin et al. Hum. Cent. Comput. Inf. Sci. (2018) Hum. Cent. Comput. Inf. Sci. (2018) 8:1 Based on the position of the virtualization layer, virtualization can be of different types like full virtualization, paravirtualization and OS level virtualization. RESEARCHTraditional HPC clusters are composed of many separate dedicated servers called nodes and may be shared among different organizations. The requirements of each user or organization will be different, which demands the creation of customized environments without affecting others. This is not an easy task in traditional HPC systems. As a solution for this, virtualization was adopted for HPC. Virtualization materializes the task by creating s...

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“…Martin et al investigated the inter-operability of Rkt containers in high performance applications by running the HPL and Graph500 applications, then simulated the performance with commonly-used container technologies such as Docker and LXC containers [7]. They analyzed the performance results of data intensive and compute intensive practical applications in several scenarios [7].…”

“…Martin et al investigated the inter-operability of Rkt containers in high performance applications by running the HPL and Graph500 applications, then simulated the performance with commonly-used container technologies such as Docker and LXC containers [7]. They analyzed the performance results of data intensive and compute intensive practical applications in several scenarios [7]. Casalicchio et al explored the measurement of the Docker performance from the perspective of the host operating system and the virtualization environment by outputting a characterization of the CPU and the disk I/O overhead generated by containers [8].…”

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