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

Martin, John P.; Kandasamy, A.; Chandrasekaran, K.

doi:10.1186/s13673-017-0124-3

Cited by 66 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Buzachis Et Al Investigated the Performance Of Four Networkmentioning

confidence: 99%

“…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].…”

Section: Buzachis Et Al Investigated the Performance Of Four Networkmentioning

confidence: 99%

“…Most studies have examined the performance of popular container cluster networking solutions in terms of different performance indicators such as throughput, latency, response times, bandwidth within different applications on local environments or public cloud infrastructures [2][3][4][5] However, few studies have focused on performance under load. The studies which have been carried out have generally been executed on HPC (High Performance Computing) platforms with applications which have different resource intensities [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new smart networking architecture for container network functions

2020

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

5G slices have challenging application demands from a wide variety of fields including high bandwidth, low latency and reliability. The requirements of the Container Network Functions which are used in telecommunications are different from any other cloud native IT applications as they are used for data plane packet processing functions, together with control, signalling and media processing which have critical processing requirements. This study aims to discover high performing container networking solution by considering traffic loads and application types. The behaviour of several container cluster networking solutions-Flannel, Weave, Libnetwork, Open Virtual Networking for Open vSwitch and Calico-are explored with regard to the most commonly-used Container Network Functions in the form of MongoDB and Web access. Evaluations show that none of the solutions provide a high throughput for all types of workload under optimum or heavy load situations. Hence, this research presents the view that traditional container networking implementation methods may not fulfill the Container Network Functions' networking performance requirements. This is because container networking performance changes dynamically, depending on traffic load and application types. To overcome this problem, a new smart container networking architecture is proposed which allows containers to use several container networking solutions dynamically in conjunction with container monitoring tools. Eventually, the primary implementation of proposed architecture has been performed and evaluated. This research shows that proposed smart architecture delivers promising results compared to traditional implementation methods, in case appropriate decision is made during dynamic interface selection process.

show abstract

Section: Buzachis Et Al Investigated the Performance Of Four Networkmentioning

confidence: 99%

Section: Buzachis Et Al Investigated the Performance Of Four Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new smart networking architecture for container network functions

2020

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…JupyTEP IDE extends the paradigm of a collaborative platform in the form of data and information exchange. Data sharing is resolved with the use of two types of volumes: User-and host-oriented [27]:…”

Section: Platform Infrastructurementioning

confidence: 99%

JupyTEP IDE as an Online Tool for Earth Observation Data Processing

Rapiński

Bednarczyk

Zinkiewicz³

2019

Remote Sensing

View full text Add to dashboard Cite

The paper describes a new tool called JupyTEP integrated development environment (IDE), which is an online integrated development environment for earth observation data processing available in the cloud. This work is a result of the project entitled “JupyTEP IDE—Jupyter-based IDE as an interactive and collaborative environment for the development of notebook style EO algorithms on network of exploitation platforms infrastructure” carried out in cooperation with European Space Agency. The main goal of this project was to provide a universal earth observation data processing tool to the community. JupyTEP IDE is an extension of Jupyter software ecosystem with customization of existing components for the needs of earth observation scientists and other professional and non-professional users. The approach is based on configuration, customization, adaptation, and extension of Jupyter, Jupyter Hub, and Docker components on earth observation data cloud infrastructure in the most flexible way; integration with accessible libraries and earth observation data tools (sentinel application platform (SNAP), geospatial data abstraction library (GDAL), etc.); adaptation of existing web processing service (WPS)-oriented earth observation services. The user-oriented product is based on a web-related user interface in the form of extended and modified Jupyter user interface (frontend) with customized layout, earth observation data processing extension, and a set of predefined notebooks, widgets, and tools. The final IDE is addressed to the remote sensing experts and other users who intend to develop Jupyter notebooks with the reuse of embedded tools, common WPS interfaces, and existing notebooks. The paper describes the background of the system, its architecture, and possible use cases.

show abstract

“…These applications, also known as cloud-native applications still harness the benefits of the cloud-native paradigm [4], [5]. For cloud-native applications, the adoption of containerization is gradually increasing since it has benefits in terms of flexibility and performance rather than virtual machines [6], [7]. Containers that are inherently agile offer the most feasible option to run microservices in cloud computing [8].…”

Section: Introductionmentioning

confidence: 99%

Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

2020

View full text Add to dashboard Cite

As cloud-native computing is becoming the de-facto paradigm in the cloud field, Microservices Architecture has attracted attention from industries and researchers for agility and efficiency. Moreover, with the popularity of the IoT in the context of edge computing, cloud-native applications that utilize geographically-distributed multiple resources are emerging. In line with this trend, there is an increasing demand for microservices placement that selectively use optimal resources. However, optimal microservices placement is a significant challenge because microservices are dynamic and complex, depending on diversified workloads. Besides, generalizing workloads' characteristics consisting of complex microservices is realistically challenging. Thus, microservices deployment with mathematically structured algorithms based on simulation is less practical. As an alternative, a microservices placement framework is required that can reflect the characteristics of workloads derived from empirical profiling. Therefore, in this research work, we propose a refinement framework for profiling-based microservices placement to identify and respond to workload characteristics in a practical way. To achieve this goal, we perform profiling experiments with selected workloads to derive delicate resource requirements. Then, we perform microservices placement with a greedy-based heuristic algorithm that considers application performance by using resource requirements derived from the profiled results. Finally, we verify the proposed concept by comparing the experimental results that use our work and those that don't.

show abstract

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

Cited by 66 publications

References 15 publications

A new smart networking architecture for container network functions

A new smart networking architecture for container network functions

JupyTEP IDE as an Online Tool for Earth Observation Data Processing

Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

Contact Info

Product

Resources

About