A Performance Comparison of Cloud-Based Container Orchestration Tools

Pan, Yao; Chen, Ian; Brasileiro, Francisco; Jayaputera, Glenn; Sinnott, Richard

doi:10.1109/icbk.2019.00033

Cited by 25 publications

(9 citation statements)

References 7 publications

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“…Docker supports various orchestration tools such as Mesos [20], Docker Swarm 10 , and Kubernetes 11 . Kubernetes is used extensively in comparison with other tools as indicated by [28] due to its compatibility with any operating system, auto-scaling feature for large deployments and better resource management facilities. Developers in this category have queries about using Kubernetes in Docker (id: 39995335, title: "access docker container in kubernetes"), and initialization of Kubernetes in Docker (id: 32955684, title: "How to pass a parameter to docker with kubernetes").…”

Section: 110mentioning

confidence: 99%

Challenges in Docker Development

Haque¹,

Iwaya²,

Babar³

2020

Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background: Docker technology has been increasingly used among software developers in a multitude of projects. This growing interest is due to the fact that Docker technology supports a convenient process for creating and building containers, promoting close cooperation between developer and operations teams, and enabling continuous software delivery. As a fast-growing technology, it is important to identify the Docker-related topics that are most popular as well as existing challenges and difficulties that developers face. Aims: This paper presents a large-scale empirical study identifying practitioners' perspectives on Docker technology by mining posts from the Stack Overflow (SoF) community. Method: A dataset of 113, 922 Docker-related posts was created based on a set of relevant tags and contents. The dataset was cleaned and prepared. Topic modelling was conducted using Latent Dirichlet Allocation (LDA), allowing the identification of dominant topics in the domain. Results: Our results show that most developers use SoF to ask about a broad spectrum of Docker topics including framework development, application deployment, continuous integration, web-server configuration and many more. We determined that 30 topics that developers discuss can be grouped into 13 main categories. Most of the posts belong to categories of application development, configuration, and networking. On the other hand, we find that the posts on monitoring status, transferring data, and authenticating users are more popular among developers compared to the other topics. Specifically, developers face challenges in web browser issues, networking error and memory management. Besides, there is a lack of experts in this domain. Conclusion: Our research findings will guide future work on the development of new tools and techniques, helping the community to focus efforts and understand existing trade-offs on Docker topics. CCS CONCEPTS • General and reference → Empirical studies; • Computing methodologies → Information extraction.

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Section: 110mentioning

confidence: 99%

Challenges in Docker Development

Haque¹,

Iwaya²,

Babar³

2020

Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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“…Nevertheless, there is still a potential for Docker and Kubernetes to be independent of each other. Another orchestration platform that can be an alternative to Kubernetes is Docker Swarm which carries out hold the same responsibility toward the containerized application with simpler implementation 41 . The complexity of these two container orchestrators has been proven with the assessment of the cluster provisioning time and failover time in which Docker Swarm exhibits less duration for both tests compared to Kubernetes 42 …”

Section: Microservice Autoscalingmentioning

confidence: 99%

A review of microservices autoscaling with formal verification perspective

2022

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The process of scaling microservices is a challenging task, especially in maintaining optimum resource provisioning while respecting QoS constraints and SLA.Many research works have proposed autoscaling approaches for microservices, however, less likely concerned with the correctness guarantee of the proposed algorithms. Hence, it is significant to gather and summarize these approaches to foster future innovation. Meanwhile, a few reviews have been published concerning microservices from different aspects. Therefore, our review complements the existing by focusing on autoscaling with verification perspectives. This study highlights the recent contributions in three inter-related main topics that were published within the year 2017 to 2022, namely, microservice, verification, and autoscaling. Due to limited resources on verification for microservice autoscaling, we widen the perspective by considering the verification for autoscaling in cloud-based systems. Based on our findings, we found that the formal method is not a new thing in verifying the autoscaling policies in cloud-based systems, and one recent study that implements the formal method in the microservices area has been identified. Apart from the autoscaling techniques, we have also determined several factors that have been a concern in scaling the microservices as well as the relatable metrics. Meanwhile, from a verification perspective, we identified that probabilistic model checking is the common formal verification technique used to verify microservices and cloud autoscaling. Finally, we recommend open challenges from two perspectives which highlight the verification for existing microservice autoscaling and verification for ML-based microservice autoscaling.

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“…Both tackle similar problems, i.e., orchestrating a set of containerized applications; however, the technologies target different application domains. In short, Docker Swarm Mode is more appropriate when orchestrating simpler service stacks where high-availability, fault-tolerancy and automatic scalability are not of highest priority and Kubernetes in case of more complex situations Pan et al (2019). While Docker provides an open standard for encapsulating and distributing containerized applications, the complexity can increase quite fast when hundreds or thousands of containers need to be managed.…”

Section: Service Orchestrationmentioning

confidence: 99%

AVUBDI: A Versatile Usable Big Data Infrastructure and Its Monitoring Approaches for Process Industry

et al. 2021

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The amount of sensors in process industry is continuously increasing as they are getting faster, better and cheaper. Due to the rising amount of available data, the processing of generated data has to be automatized in a computationally efficient manner. Such a solution should also be easily implementable and reproducible independently of the details of the application domain. This paper provides a suitable and versatile usable infrastructure that deals with Big Data in the process industry on various platforms using efficient, fast and modern technologies for data gathering, processing, storing and visualization. Contrary to prior work, we provide an easy-to-use, easily reproducible, adaptable and configurable Big Data management solution with a detailed implementation description that does not require expert or domain-specific knowledge. In addition to the infrastructure implementation, we focus on monitoring both infrastructure inputs and outputs, including incoming data of processes and model predictions and performances, thus allowing for early interventions and actions if problems occur.

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A Performance Comparison of Cloud-Based Container Orchestration Tools

Cited by 25 publications

References 7 publications

Challenges in Docker Development

Challenges in Docker Development

A review of microservices autoscaling with formal verification perspective

AVUBDI: A Versatile Usable Big Data Infrastructure and Its Monitoring Approaches for Process Industry

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