Performance Evaluation of Docker Container and Virtual Machine

Potdar, Amit M; Narayan, D. G.; Kengond, Shivaraj; Mulla, Mohammed Moin

doi:10.1016/j.procs.2020.04.152

Cited by 112 publications

(42 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…None of the selected studies proposes dedicated techniques and tools to address those reasons behind performance issues due to frequent communication. However, a few studies (e.g., [41][42][43][44][45]) discuss the performance of the Docker containers and VMs for the implementation of microservices. Salah et al experimented and found that the performance of 31/50 the services that are deployed on Amazon EC2 container is worse than services that are deployed on Amazon EC2 VMs [41].…”

Section: Challengesmentioning

confidence: 99%

A Systematic Mapping Study on Microservices Architecture in DevOps

Waseem

Liang

Shahin

2020

Journal of Systems and Software

104

View full text Add to dashboard Cite

Section: Challengesmentioning

confidence: 99%

A Systematic Mapping Study on Microservices Architecture in DevOps

Waseem

Liang

Shahin

2020

Journal of Systems and Software

104

View full text Add to dashboard Cite

“…Docker-container-based hosts had the fastest boot time and the best overall performance, with the exception of network bandwidth. Potdar et al [26] evaluated the performance of Docker containers and VMs using standard benchmark tools, such as Sysbench, Phoronix, and Apache benchmark, in terms of CPU performance, memory throughput, storage read/write performance, load test, and operation speed measurement. Ventre et al [27] investigated the performance of the instantiation process of micro-virtualized network functions for open-source virtual infrastructure managers, such as OpenStack Nova and Nomad, on the Xen virtualization platform.…”

Section: Introductionmentioning

confidence: 99%

Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud

2021

View full text Add to dashboard Cite

The pervasive use of cloud computing has led to many concerns, such as performance challenges in communication- and computation-intensive services on virtual cloud resources. Most evaluations of the infrastructural overhead are based on standard benchmarks. Therefore, the impact of communication issues and infrastructure services on the performance of parallel MPI-based computations remains unclear. This paper presents the performance analysis of communication- and computation-intensive software based on the discrete element method, which is deployed as a service (SaaS) on the OpenStack cloud. The performance measured on KVM-based virtual machines and Docker containers of the OpenStack cloud is compared with that obtained by using native hardware. The improved mapping of computations to multicore resources reduced the internode MPI communication by 34.4% and increased the parallel efficiency from 0.67 to 0.78, which shows the importance of communication issues. Increasing the number of parallel processes, the overhead of the cloud infrastructure increased to 13.7% and 11.2% of the software execution time on native hardware in the case of the Docker containers and KVM-based virtual machines of the OpenStack cloud, respectively. The observed overhead was mainly caused by OpenStack service processes that increased the load imbalance of parallel MPI-based SaaS.

show abstract

“…Several cases have made performance comparisons between dockers and virtual machines [5] [6], the result of this comparison is that Docker is able to provide more efficient output on the performance of CPU performance. Docker also has better scalability than VMs.…”

Section: Docker Environmentmentioning

confidence: 99%

The Implementation of Big Data Technology in Virtual Machines for Mapping 2019-nCoV Pandemic on the Students of Information Technology

Surateno¹,

Atmadji²

2021

Proceedings of the First International Conference on Social Science, Humanity, and Public Health (ICOSHIP 2020)

View full text Add to dashboard Cite

The spread of Covid-19 has influenced changes in human behavior and paradigm including in teaching and learning process. The teaching and learning process has now been replaced by E-Learning models which offer flexibility for both teachers and students. Current learning process can be accessed by students from different places and not necessarily gather in one room. However, this raises a new problem, namely the difficulty of tracking the scattered students when they return to campus. One way to overcome this problem is to use a big data approach combined with a virtual machine to recognize and detect these students. In this approach, the results of tracking students with server computer resources are up to 15% smaller than using non virtual machines. This is a new approach to data processing.

show abstract

Performance Evaluation of Docker Container and Virtual Machine

Cited by 112 publications

References 4 publications

A Systematic Mapping Study on Microservices Architecture in DevOps

A Systematic Mapping Study on Microservices Architecture in DevOps

Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud

The Implementation of Big Data Technology in Virtual Machines for Mapping 2019-nCoV Pandemic on the Students of Information Technology

Contact Info

Product

Resources

About