Deploying Microservice Based Applications with Kubernetes: Experiments and Lessons Learned

Vayghan, Leila Abdollahi; Saied, Mohamed Aymen; Toeroe, Maria; Khendek, Ferhat

doi:10.1109/cloud.2018.00148

Cited by 67 publications

(23 citation statements)

References 4 publications

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“…To avoid conflicts, each pod is assigned a unique IP address, enabling applications to use different ports under the same IP address. Kubernetes offers availability and quality checks for containers in order to heal failed containers in pods through its automatic failure recovery actions [35]. As presented in [36], compared to Docker Swarm, which is another container orchestration platform [37], Kubernetes is more powerful and provides immense scalability and automation at the same time.…”

Section: Container Orchestration-kubernetesmentioning

confidence: 99%

Architectural Concept and Evaluation of a Framework for the Efficient Automation of Computational Scientific Workflows: An Energy Systems Analysis Example

et al. 2019

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Scientists and engineers involved in the design of complex system solutions use computational workflows for their evaluations. Along with growing system complexity, the complexity of these workflows also increases. Without integration tools, scientists and engineers are often highly concerned with how to integrate software tools and model sets, which hinders their original research or engineering aims. Therefore, a new framework for streamlining the creation and usage of automated computational workflows is introduced in the present article. It uses state-of-the-art technologies for automation (e.g., container-automation) and coordination (e.g., distributed message oriented middleware), and a microservice-based architecture for novel distributed process execution and coordination. It also supports co-simulations as part of larger workflows including additional auxiliary computational tasks, e.g., forecasting or data transformation. Using Apache NiFi, an easy-to-use web interface is provided to create, run and control workflows without the need to be concerned with the underlying computing infrastructure. Initial framework testing via the implementation of a real-world workflow underpins promising performance in the realms of parallelizability, low overheads and reliable coordination.

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Section: Container Orchestration-kubernetesmentioning

confidence: 99%

Architectural Concept and Evaluation of a Framework for the Efficient Automation of Computational Scientific Workflows: An Energy Systems Analysis Example

et al. 2019

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“…Hence, it is necessary to appropriately set the Kubernetes parameters related to fault detection and recovery to meet the requirements of mission-critical IoT services [ 17 ]. To study fault recovery in container infrastructure, References [ 18 , 19 ] measured and analyzed the fault detection and recovery performance under various conditions in the Kubernetes environment. First, in Reference [ 18 ], the fault detection and recovery function were tested using the basic Kubernetes function when the case of node failure and application fault occurred.…”

Section: Introductionmentioning

confidence: 99%

“…To study fault recovery in container infrastructure, References [ 18 , 19 ] measured and analyzed the fault detection and recovery performance under various conditions in the Kubernetes environment. First, in Reference [ 18 ], the fault detection and recovery function were tested using the basic Kubernetes function when the case of node failure and application fault occurred. However, the authors of Reference [ 18 ] measured and presented only fault detection and recovery based on default parameters.…”

Section: Introductionmentioning

confidence: 99%

“…First, in Reference [ 18 ], the fault detection and recovery function were tested using the basic Kubernetes function when the case of node failure and application fault occurred. However, the authors of Reference [ 18 ] measured and presented only fault detection and recovery based on default parameters. In addition, since the focus is only on functional tests for failure recovery at the application level, the improvement of the node’s fault detection method is not considered.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [ 19 ], based on the result of Reference [ 18 ], when a node fault occurs, the fault detection time and the fault recovery time were measured. However, in the case of node fault, the performance was measured considering only the fault detection time (reaction time) at the application level, such as load balancer.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design and Implementation of Fast Fault Detection in Cloud Infrastructure for Containerized IoT Services

Yang

Kim

2020

Sensors

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The container-based cloud is used in various service infrastructures as it is lighter and more portable than a virtual machine (VM)-based infrastructure and is configurable in both bare-metal and VM environments. The Internet-of-Things (IoT) cloud-computing infrastructure is also evolving from a VM-based to a container-based infrastructure. In IoT clouds, the service availability of the cloud infrastructure is more important for mission-critical IoT services, such as real-time health monitoring, vehicle-to-vehicle (V2V) communication, and industrial IoT, than for general computing services. However, in the container environment that runs on a VM, the current fault detection method only considers the container’s infra, thus limiting the level of availability necessary for the performance of mission-critical IoT cloud services. Therefore, in a container environment running on a VM, fault detection and recovery methods that consider both the VM and container levels are necessary. In this study, we analyze the fault-detection architecture in a container environment and designed and implemented a Fast Fault Detection Manager (FFDM) architecture using OpenStack and Kubernetes for realizing fast fault detection. Through performance measurements, we verified that the FFDM can improve the fault detection time by more than three times over the existing method.

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A lightweight virtualization model to enable edge computing in deeply embedded systems

et al. 2021

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Edge computing paradigm enables moving Internet of Things (IoT) applications from the Cloud to the edge of the network. Modern software engineering approaches are adhering to microservices to enable the deployment of such applications on edge devices. Microservices consist of the disaggregation of an application into smaller pieces that operate independently. Recent works have explored microservices packaged into containers and advocate that containers result in a reduced footprint and avoid the unwanted overhead caused by traditional virtualization. However, containers cannot be used in many deeply embedded systems (DES) due to an underlying operating system's (OSs) requirement. DES are edge devices with minimal resources regarding storage, memory, and processing power. Thus, they cannot afford large and sophisticated OSs. This article presents the Hellfire hypervisor, a lightweight virtualization implementation that enables separation and improves security in IoT applications on DES. Our proposal simplifies the traditional hypervisor approach and reaches devices where the existing techniques fail. The results show that the proposed model has a small footprint of 23 KB while keeping a low average virtualization overhead of 0.62% for multiple virtual machines execution.

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Deploying Microservice Based Applications with Kubernetes: Experiments and Lessons Learned

Cited by 67 publications

References 4 publications

Architectural Concept and Evaluation of a Framework for the Efficient Automation of Computational Scientific Workflows: An Energy Systems Analysis Example

Architectural Concept and Evaluation of a Framework for the Efficient Automation of Computational Scientific Workflows: An Energy Systems Analysis Example

Design and Implementation of Fast Fault Detection in Cloud Infrastructure for Containerized IoT Services

A lightweight virtualization model to enable edge computing in deeply embedded systems

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