ElasticFog: Elastic Resource Provisioning in Container-Based Fog Computing

Nguyen, Nguyen Dinh; Phan, Linh-An; Park, Dae-Heon; Kim, Se-Han; Kim, Taejoon

doi:10.1109/access.2020.3029583

Cited by 39 publications

(14 citation statements)

References 27 publications

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“…Many open-source and commercial containerization technologies are available, like Docker, LinuX Container, OpenVZ, Rocket, and LXD [43]. A container image can be fetched from a container registry by the fog node to directly serve requests without preconfiguring nodes or forcing users to rely on the cloud [44]. The container images can be quickly deployed cross platforms because it contains all software needed for running the software code.…”

Section: Fc Software Featuresmentioning

confidence: 99%

Fog Computing Based on Machine Learning: A Review

Samann

Abdulazeez

Askar

2021

Int. J. Interact. Mob. Technol.

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<p>Internet of Things (IoT) systems usually produce massive amounts of data, while the number of devices connected to the internet might reach billions by now. Sending all this data over the internet will overhead the cloud and consume bandwidth. Fog computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications. This technology complements the cloud computing by providing processing power and storage to the edge of the network. However, it still suffers from performance and security issues. Thus, machine learning (ML) attracts attention for enabling FC to settle its issues. Lately, there has been a growing trend in utilizing ML to improve FC applications, like resource management, security, lessen latency and power usage. Also, intelligent FC was studied to address issues in industry 4.0, bioinformatics, blockchain and vehicular communication system. Due to the ML vital role in the FC paradigm, this work will shed light on recent studies utilized ML in a FC environment. Background knowledge about ML and FC also presented. This paper categorized the surveyed studies into three groups according to the aim of ML implementation. These studies were thoroughly reviewed and compared using sum-up tables. The results showed that not all studies used the same performance metric except those worked on security issues. In conclusion, the simulations of proposed ML models are not sufficient due to the heterogeneous nature of the FC paradigm.</p>

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Section: Fc Software Featuresmentioning

confidence: 99%

Fog Computing Based on Machine Learning: A Review

Samann

Abdulazeez

Askar

2021

Int. J. Interact. Mob. Technol.

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“…This approach is used to deploy container-based applications in a smart city. In the Fog computing environment, the paper [ 19 ] presented a framework that is based on Kubernetes. It collects the network traffic status to provide elastic resource provisioning of the container-based application among geographically distributed Fog nodes in real time.…”

Section: Related Workmentioning

confidence: 99%

Balanced Leader Distribution Algorithm in Kubernetes Clusters

Nguyen

Kim

2021

Sensors

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Container-based virtualization is becoming a de facto way to build and deploy applications because of its simplicity and convenience. Kubernetes is a well-known open-source project that provides an orchestration platform for containerized applications. An application in Kubernetes can contain multiple replicas to achieve high scalability and availability. Stateless applications have no requirement for persistent storage; however, stateful applications require persistent storage for each replica. Therefore, stateful applications usually require a strong consistency of data among replicas. To achieve this, the application often relies on a leader, which is responsible for maintaining consistency and coordinating tasks among replicas. This leads to a problem that the leader often has heavy loads due to its inherent design. In a Kubernetes cluster, having the leaders of multiple applications concentrated in a specific node may become a bottleneck within the system. In this paper, we propose a leader election algorithm that overcomes the bottleneck problem by evenly distributing the leaders throughout nodes in the cluster. We also conduct experiments to prove the correctness and effectiveness of our leader election algorithm compared with a default algorithm in Kubernetes.

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“…Nguyen et al [ 18 ] present ElasticFog, a mechanism that exploits different scheduling policies in the Kubernetes platform and enables real-time elastic resource provisioning for containerized applications in fog computing. It dynamically assigns resources to each fog node proportionate to network traffic to the application at each location.…”

Section: Related Workmentioning

confidence: 99%

QoS-Based Service-Time Scheduling in the IoT-Edge Cloud

Mutichiro

Tran

Kim

2021

Sensors

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In edge computing, scheduling heterogeneous workloads with diverse resource requirements is challenging. Besides limited resources, the servers may be overwhelmed with computational tasks, resulting in lengthy task queues and congestion occasioned by unusual network traffic patterns. Additionally, Internet of Things (IoT)/Edge applications have different characteristics coupled with performance requirements, which become determinants if most edge applications can both satisfy deadlines and each user’s QoS requirements. This study aims to address these restrictions by proposing a mechanism that improves the cluster resource utilization and Quality of Service (QoS) in an edge cloud cluster in terms of service time. Containerization can provide a way to improve the performance of the IoT-Edge cloud by factoring in task dependencies and heterogeneous application resource demands. In this paper, we propose STaSA, a service time aware scheduler for the edge environment. The algorithm automatically assigns requests onto different processing nodes and then schedules their execution under real-time constraints, thus minimizing the number of QoS violations. The effectiveness of our scheduling model is demonstrated through implementation on KubeEdge, a container orchestration platform based on Kubernetes. Experimental results show significantly fewer violations in QoS during scheduling and improved performance compared to the state of the art.

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ElasticFog: Elastic Resource Provisioning in Container-Based Fog Computing

Cited by 39 publications

References 27 publications

Fog Computing Based on Machine Learning: A Review

Fog Computing Based on Machine Learning: A Review

Balanced Leader Distribution Algorithm in Kubernetes Clusters

QoS-Based Service-Time Scheduling in the IoT-Edge Cloud

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