Performance Evaluation of Container Orchestration Tools in Edge Computing Environments

Čilić, Ivan; Krivic, Petar; Žarko, Ivana Podnar; Kušek, Mario

doi:10.3390/s23084008

Cited by 11 publications

(9 citation statements)

References 60 publications

(23 reference statements)

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“…Edge computing and edge platforms are two of the most confusing and misused terms today, with edge computing platform even more confusing. Edge computing platforms are horizontal pieces of software that are designed to enable (i) the automated deployment, (ii) update, and (iii) management of distributed applications [114]. Many operations are quickly turning to edge technology because of its relevant advantages over the legacy cloud solutions.…”

Section: A Choice Modeling-based Dt For Edge Computing Platformsmentioning

confidence: 99%

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

Hlophe,

Maharaj

2023

Applied Sciences

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As a result of the new telecommunication ecosystem landscape, wireless communication has become an interdisciplinary field whose future is shaped by several interacting dimensions. These interacting dimensions, which form the cyber–physical convergence, closely link the technological perspective to its social, economic, and cognitive sciences counterparts. Beyond the current operational framework of the Internet of Things (IoT), network devices will be equipped with capabilities for learning, thinking, and understanding so that they can autonomously make decisions and take appropriate actions. Through this autonomous operation, wireless networking will be ushered into a paradigm that is primarily inspired by the efficient and effective use of (i) AI strategies, (ii) big data analytics, as well as (iii) cognition. This is the Cognitive Internet of People Processes Data and Things (CIoPPD&T), which can be defined in terms of the cyber–physical convergence. In this article, through the discussion of how the cyber–physical convergence and the interacting dynamics of the socio-technical ecosystem are enablers of digital twins (DTs), the network DT (NDT) is discussed in the context of 6G networks. Then, the design and realization of edge computing-based NDTs are discussed, which culminate with the vehicle-to-edge (V2E) use cases.

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Section: A Choice Modeling-based Dt For Edge Computing Platformsmentioning

confidence: 99%

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

Hlophe,

Maharaj

2023

Applied Sciences

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“…We observe that among the open-source frameworks, Kubernetes is the best choice. Even compared with fully-managed services, Kubernetes is the clear winner [15,16,18,45].…”

Section: Kubernetes Vs Other Container Orchestration Frameworkmentioning

confidence: 99%

“…Notably, the authors in [15,46] show that Kubernetes can help stabilize the Information Technology (IT) administration and Serverless Computing efficiently, i.e., it helps stabilize the containerized applications and systems. Čilić et al [45] analyze the performance of container orchestration frameworks for Edge Computing. Their analysis shows that Kubernetes and its derivatives are highly efficient in container orchestration across the resources of edge network compared with other orchestration frameworks.…”

Section: Kubernetes Vs Other Container Orchestration Frameworkmentioning

confidence: 99%

Toward Optimal Load Prediction and Customizable Autoscaling Scheme for Kubernetes

Mondal

Kabir

et al. 2023

Mathematics

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Most enterprise customers now choose to divide a large monolithic service into large numbers of loosely-coupled, specialized microservices, which can be developed and deployed separately. Docker, as a light-weight virtualization technology, has been widely adopted to support diverse microservices. At the moment, Kubernetes is a portable, extensible, and open-source orchestration platform for managing these containerized microservice applications. To adapt to frequently changing user requests, it offers an automated scaling method, Horizontal Pod Autoscaler (HPA), that can scale itself based on the system’s current workload. The native reactive auto-scaling method, however, is unable to foresee the system workload scenario in the future to complete proactive scaling, leading to QoS (quality of service) violations, long tail latency, and insufficient server resource usage. In this paper, we suggest a new proactive scaling scheme based on deep learning approaches to make up for HPA’s inadequacies as the default autoscaler in Kubernetes. After meticulous experimental evaluation and comparative analysis, we use the Gated Recurrent Unit (GRU) model with higher prediction accuracy and efficiency as the prediction model, supplemented by a stability window mechanism to improve the accuracy and stability of the prediction model. Finally, with the third-party custom autoscaling framework, Custom Pod Autoscaler (CPA), we packaged our custom autoscaling algorithm into a framework and deployed the framework into the real Kubernetes cluster. Comprehensive experiment results prove the feasibility of our autoscaling scheme, which significantly outperforms the existing Horizontal Pod Autoscaler (HPA) approach.

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“…To accomplish the metaanalysis, bibliometric analysis was applied with the aim of contributing to the knowledge in the field of Container orchestration research. Bibliometric analysis is a scientific computer-assisted review methodology that can identify core research or authors, as well as their relationships [9]. By covering all the publications related to a given topic or field.…”

Section: B Search Strategy and Selection Processmentioning

confidence: 99%

“…In the pursuit of identifying the most appropriate orchestration tool amidst a diverse array of options, a comprehensive comparison has been conducted in existing literature, spanning orchestrators including Amazon ECS, Microsoft Azure Kubernetes Service (AKS), Google Kubernetes Engine (GKE), OpenShift, Docker Swarm, and Apache Mesos and Nomad. This comprehensive evaluation has discerned the strengths and distinct characteristics of each orchestrator to provide an understanding of their relevance and suitability within the broader spectrum [9]. Through this exploration, tools such as Kubernetes, Docker Swarm, and Apache Mesos emerged as popular tools based on their relevance to streamlining deployment, scaling processes, and optimizing fog computing environments.…”

Section: Suitability Of Orchestration Tools For Iot Devices In Fog Co...mentioning

confidence: 99%

Orchestration Tools For Efficient Deployment of IoT Applications In Fog Computing: A Systematic Review

Mthembu,

Ezeji,

Adigun

2023

iCARTi

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Internet of Things (IoT) is the developing technology that enables devices to communicate without human interaction. IoT utilizes cloud computing services to collect and process data for IoT devices and to manage the device remotely. Cloud computing is not efficient enough to handle the fast stream of data produced by the IoT, therefore scaling up IoT applications to meet demands of high peak becomes easier and highly automated in fog computing. Containers are mostly used as virtualization solutions for IoT in fog computing. It enables the execution of small microservices to large applications. However, the rise of many lightweight containers has resulted in new application architectures and fundamentally changing how applications are deployed and visualized. Due to this change, container orchestration tools were proposed. These tools allow users to coordinate and manage containers. However, container orchestration tools need to meet the requirements of IoT applications and constraints imposed on the nodes in fog. This paper presents a systematic literature review on the selection of orchestration tools for the efficient deployment of IoT applications in fog computing. Moreover, the performance of IoT applications must be considered by applying different metrics. This paper aims to propose potential research directions to address identified gaps in the selection of orchestration tools.

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Performance Evaluation of Container Orchestration Tools in Edge Computing Environments

Cited by 11 publications

References 60 publications

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

Toward Optimal Load Prediction and Customizable Autoscaling Scheme for Kubernetes

Orchestration Tools For Efficient Deployment of IoT Applications In Fog Computing: A Systematic Review

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