Geo-distributed efficient deployment of containers with Kubernetes

Rossi, Fabiana; Cardellini, Valeria; Presti, Francesco Lo; Nardelli, Matteo

doi:10.1016/j.comcom.2020.04.061

Cited by 103 publications

(66 citation statements)

References 15 publications

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“…Kubernetes [32] is an open-source variant of Google orchestrator Borg [33]. All workloads end in the domain of one cluster [32,33,34]. Kubernetes is a promising solution for geo-distributed and EC environments due to its extensibility and existing tooling, but by design, Kubernetes operate in a completely different environment.…”

Section: B Platform Modelmentioning

confidence: 99%

“…On the other hand, some solutions show the Kubernetes can run in geo-distributed and EC environments. For example, Rossi et al [34] focuses on adapting Kubernetes for geo-distributed workloads using a reinforcement learning (RL) solution, to learn a suitable scaling policy from experience. Like every other machine learning implementation, this could be potentially slow due to the required model training.…”

Section: B Platform Modelmentioning

confidence: 99%

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Towards Edge Computing as a Service: Dynamic Formation of the Micro Data-Centers

et al. 2021

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Edge computing brings cloud services closer to the edge of the network, where data originates, and dramatically reduces the network latency of the cloud. It is a bridge linking clouds and users making the foundation for novel interconnected applications. However, edge computing still faces many challenges like remote configuration, well-defined native applications model, and limited node capacity. It lacks geo-organization and a clear separation of concerns. As such edge computing is hard to be offered as a service for future real-time user-centric applications. This paper presents the dynamic organization of geodistributed edge nodes into micro data-centers to cover any arbitrary area and expand capacity, availability, and reliability. A cloud organization is used as an influence with adaptations for a different environment, and a model for edge applications utilizing these adaptations is presented. It is argued that the presented model can be integrated into existing solutions or used as a base for the development of future systems. Furthermore, a clear separation of concerns is given for the proposed model. With the separation of concerns setup, edge-native applications model, and a unified node organization, we are moving towards the idea of edge computing as a service, like any other utility in cloud computing.INDEX TERMS cloud computing, distributed systems, edge computing, formal specifications, infrastructure as software, platform

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Section: B Platform Modelmentioning

confidence: 99%

Section: B Platform Modelmentioning

confidence: 99%

Towards Edge Computing as a Service: Dynamic Formation of the Micro Data-Centers

et al. 2021

View full text Add to dashboard Cite

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“…If location-aware is selected, the selection of node is based on minimizing the latency with the target location of the pod. Paper [28] proposed an orchestration tool (ge-kube) for container-based applications in the geo-distribution of the Fog computing environment. Ge-kube relies on Kubernetes and provides two logical components: elasticity manager and placement manager.…”

Section: Related Workmentioning

confidence: 99%

ElasticFog: Elastic Resource Provisioning in Container-Based Fog Computing

et al. 2020

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The recent increase in the number of Internet of Things (IoT) devices has led to the generation of a large amount of data. These data are generally processed by cloud servers because of their high scalability and ability to provide resources on demand. However, processing large amounts of data in the cloud is an impractical solution for the strict requirements of IoT services, such as low latency and high bandwidth. Fog computing, which brings computational resources closer to the IoT devices, has emerged as a suitable solution to mitigate these problems. Resource provisioning and application orchestration are two of the key challenges when running IoT applications in a Fog computing environment. In this paper, we present ElasticFog, which runs on top of the Kubernetes platform and enables real-time elastic resource provisioning for containerized applications in Fog computing. Specifically, ElasticFog collects network traffic information in real time and allocates computational resources proportionally to the distribution of network traffic. The experimental results prove that ElasticFog achieves a significant improvement in terms of throughput and network latency compared with the default mechanism in Kubernetes. INDEX TERMS container, Fog computing, Internet of Things (IoT), Kubernetes, resource provisioning.

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“…MSA-based cloud applications are network sensitive because different functions interact with each other. The simplest optimal microservices placement is that microservices having frequent interactions are placed on the same node or next to each other as much as possible [36], [37]. To apply this methodology, we also define a parameter, node interaction denoted as h int z,n in Eq.…”

Section: A Greedy-based Heuristic Algorithm For Microservices Placementmentioning

confidence: 99%

Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

2020

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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.

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Geo-distributed efficient deployment of containers with Kubernetes

Cited by 103 publications

References 15 publications

Towards Edge Computing as a Service: Dynamic Formation of the Micro Data-Centers

Towards Edge Computing as a Service: Dynamic Formation of the Micro Data-Centers

ElasticFog: Elastic Resource Provisioning in Container-Based Fog Computing

Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

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