Evaluation of Docker containers based on hardware utilization

Preeth, E N; Mulerickal, Fr. Jaison Paul; Paul, Biju; Sastri, Yedhu

doi:10.1109/iccc.2015.7432984

Cited by 66 publications

(27 citation statements)

References 1 publication

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“…Similarly, results from sysbench, an open-source benchmarking utility originally created by MySQL AB, found that the time needed to calculate 10,000 primes in either environment was equivalent [Figure 3b]. When combined with evidence from other studies,[21] these results demonstrate that Docker has a minimal overhead for these components.…”

Section: Approachmentioning

confidence: 53%

Use of application containers and workflows for genomic data analysis

Schulz

Durant

Siddon

et al. 2016

Journal of Pathology Informatics

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Background:The rapid acquisition of biological data and development of computationally intensive analyses has led to a need for novel approaches to software deployment. In particular, the complexity of common analytic tools for genomics makes them difficult to deploy and decreases the reproducibility of computational experiments.Methods:Recent technologies that allow for application virtualization, such as Docker, allow developers and bioinformaticians to isolate these applications and deploy secure, scalable platforms that have the potential to dramatically increase the efficiency of big data processing.Results:While limitations exist, this study demonstrates a successful implementation of a pipeline with several discrete software applications for the analysis of next-generation sequencing (NGS) data.Conclusions:With this approach, we significantly reduced the amount of time needed to perform clonal analysis from NGS data in acute myeloid leukemia.

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Section: Approachmentioning

confidence: 53%

Use of application containers and workflows for genomic data analysis

Schulz

Durant

Siddon

et al. 2016

Journal of Pathology Informatics

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“…Finally, the monitoring of services deployed in containers is present in the works [13] and [14]. In the work published in [13] the authors present a framework called PyMon that uses the Docker management API to obtain statistics of resources used by containers.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike [13], the present study uses libraries to monitor processes inside the containers, thus allowing the effective monitoring of a container that performs a multiservice or multi-process environment. The work presented in [14] brings an assessment of the use of Docker containers versus the use of Virtual Machines. To verify the QoS parameters to be compared for evaluation, the authors monitored the CPU usage by the installed Docker process, not verifying the parameters of the containers that are being executed or even of the processes internal to the containers.…”

Section: Related Workmentioning

confidence: 99%

“…In summary, works such as CLAMBS model [11] enables efficient monitoring of services in a multicloud environment but lacks capability to monitor microservices at the edge. Current works on microservice monitoring [5] usually focus on single layer monitoring, i.e., microservices in the cloud [14] or microservices at the edge [13]. Our proposed work differs from the current approaches by presenting an advanced monitoring solution that can be used to monitor microservices deployed in Osmotic Computing environment i.e.…”

Section: Related Workmentioning

confidence: 99%

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Osmotic Monitoring of Microservices between the Edge and Cloud

Souza

Cacho

Noor

et al. 2018

2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference On

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Osmotic computing is a new IoT application programming paradigm thats driven by the significant increase in resource capacity/capability at the network edge, along with support for data transfer protocols that enable such resources to interact more seamlessly with Cloud-based services. Much of the difficulty in QoS and performance monitoring of IoT applications in an Osmotic computing environment is due to the massive scale and heterogeneity (IoT + Edge + Cloud) of computing environments. To, this end, this work presents an integrated monitoring system for monitoring IoT applications decomposed as microservices and executed in an Osmotic computing environment. A real-world smart parking IoT application is used for an experimental evaluation and for demonstrating the effectiveness of the proposed approach. Through rigorous experimental evaluation, we validate the Osmotic monitoring system ability to holistically identify variation in CPU, memory, and network latency of microservices deployed across Cloud and Edge layers.

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“…Docker is an open platform to build, deploy and run distributed applications using Docker engine and Docker hub [44]. Docker engine is a portable, lightweight runtime and packaging tool while Docker Hub is a cloud service for sharing applications and automating workflows.…”

Section: Introduction To Docker Containersmentioning

confidence: 99%

Containerized IoT Solution for Efficient Mobile Vertical Handover

Gaur¹

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Internet of Things (IoT) is ubiquitous, which includes objects communicating through heterogenous networks. One of the challenges in mobile IoT is vertical handover decision (VHD) between heterogenous networks for seamless connectivity. Conventional VHD approach is based on received signal strength (RSS), which is limited to only RSS Quality and hence inefficient to decide best network for vertical handover. This thesis proposes multi-criteria based VHD (MCVHD) algorithm for efficient VHD between Wi-Fi, Radio and Satellite network. The experiment results show that MCVHD outperforms the conventional RSS Quality based VHD by minimizing handover failures, unnecessary handovers, handover time and cost of service by selecting best available network using multi-criteria parameters. The proposed IoT solution also employs Docker container based microservices architecture. The results show that the Docker container produces negligible resource overhead and can be used on resource constrained IoT devices like Raspberry Pi 3, for efficiently managing IoT application and services.iii

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Evaluation of Docker containers based on hardware utilization

Cited by 66 publications

References 1 publication

Use of application containers and workflows for genomic data analysis

Use of application containers and workflows for genomic data analysis

Osmotic Monitoring of Microservices between the Edge and Cloud

Containerized IoT Solution for Efficient Mobile Vertical Handover

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