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

Cloud 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.

[1]  Ramakrishnan Rajamony,et al.  An updated performance comparison of virtual machines and Linux containers , 2015, 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

[2]  Michael Shuey,et al.  Containers in Research: Initial Experiences with Lightweight Infrastructure , 2016, XSEDE.

[3]  S. Viswanadha Raju,et al.  Analysis of a Network IO Bottleneck in Big Data Environments Based on Docker Containers , 2016, Big Data Res..

[4]  César A. F. De Rose,et al.  Performance Evaluation of Container-Based Virtualization for High Performance Computing Environments , 2013, 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[5]  René Peinl,et al.  Docker Cluster Management for the Cloud - Survey Results and Own Solution , 2016, Journal of Grid Computing.

[6]  Nam Thoai,et al.  Using Docker in high performance computing applications , 2016, 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE).

[7]  Nam Thoai,et al.  Provision of Docker and InfiniBand in High Performance Computing , 2016, 2016 International Conference on Advanced Computing and Applications (ACOMP).

[8]  Jayaprakash Kar,et al.  Mitigating Threats and Security Metrics in Cloud Computing , 2016, J. Inf. Process. Syst..

[9]  Richard O. Sinnott,et al.  A performance comparison of container-based technologies for the Cloud , 2017, Future Gener. Comput. Syst..

[10]  Miika Komu,et al.  Hypervisors vs. Lightweight Virtualization: A Performance Comparison , 2015, 2015 IEEE International Conference on Cloud Engineering.

[11]  John Paul Martin,et al.  System Performance Evaluation of Para Virtualization, Container Virtualization, and Full Virtualization Using Xen, OpenVZ, and XenServer , 2014, 2014 Fourth International Conference on Advances in Computing and Communications.

[12]  Carlos de Alfonso,et al.  Container-based virtual elastic clusters , 2017, J. Syst. Softw..

[13]  Changhoon Lee,et al.  A Security Protection Framework for Cloud Computing , 2016, J. Inf. Process. Syst..

[14]  Jun-Ho Huh,et al.  Design and test bed experiments of server operation system using virtualization technology , 2016, Human-centric Computing and Information Sciences.

[15]  Heon-Chang Yu,et al.  A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments , 2017, Human-centric Computing and Information Sciences.

[16]  D. Jacobsen,et al.  Contain This, Unleashing Docker for HPC , 2015 .

[17]  Weicheng Huang,et al.  Building a Virtual HPC Cluster with Auto Scaling by the Docker , 2015, ArXiv.

[18]  Augusto Ciuffoletti,et al.  Automated Deployment of a Microservice-based Monitoring Infrastructure , 2015, Cloud Forward.

[19]  Mohamed Jemni,et al.  LXCloud-CR: Towards LinuX Containers Distributed Hash Table based Checkpoint-Restart , 2018, J. Parallel Distributed Comput..