A Comparison of QoS-Based Architecture Solutions for IoT/Edge Computing Environment

Niang, Ibrahima

doi:10.1007/978-3-030-53440-0_37

Cited by 3 publications

(7 citation statements)

References 23 publications

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“…It applied SDN to high-performance computing and performed efficiently. In [17], the authors proposed a method for real-time IoT applications with QoS requirements in edge computing environments. This method revealed a novel research view about QoS dynamic management.…”

Section: Related Workmentioning

confidence: 99%

“…FC reduces the service delay by processing the requests on Fog nodes instead of forwarding them to the Cloud data center from the core network. To better support QoS requirements, preprocessing and filtering are done in Fog to reduce sending requests to the Cloud as well as the network traffic and network congestion between the edge and the central Cloud [13][14][15][16][17][18]. If requests require permanent storage or extensive analyses, they are sent to the Cloud.…”

Section: Introductioncmentioning

confidence: 99%

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Delay Reduction in MTC Using SDN Based Offloading in Fog Computing

Arefian

Khayyambashi

Movahhedinia

2022

Preprint

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Fog computing (FC) brings a Cloud close to the users, and improves the quality of service and delay services. In this article, the converged FC and Software-Defined-Networking (SDN) have been proposed to implement complicated mechanisms of resource management. SDN has suited the practical standard for FC systems. The priority and differential flow space allocation has been applied to arrange this framework for the heterogeneous request Machine-Type-Communications. The delay-sensitive flows are assigned to a configuration of priority queues on each Fog. Due to limited resources in the Fog, a promising solution is offloading flows to other Fogs a decision-based SDN controller. The flow-based Fog nodes have been modeled according to the queueing theory, where polling priority algorithms have been applied to service the flows and to reduce the starvation problem in a multi-queueing model. It is observed that the proposed mechanism notably reduces the computation and transmission delay and the count of the drops.

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Section: Related Workmentioning

confidence: 99%

Section: Introductioncmentioning

confidence: 99%

Delay Reduction in MTC Using SDN Based Offloading in Fog Computing

Arefian

Khayyambashi

Movahhedinia

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It applied SDN to high-performance computing and performed efficiently. In [ 13 ], the authors proposed a method for real-time IoT applications with QoS requirements in edge computing environments. This method revealed a novel research view about QoS dynamic management.…”

Section: Related Workmentioning

confidence: 99%

“…This is an important factor to consider when designing the system's performance. The probability of all computing resources being idle is defined as follows, and is expressed as p 0 in the Eq (13) according to [53].…”

Section: Queueing Model and Schedulingmentioning

confidence: 99%

“…Fog computing (FC) can help organizations reduce latency issues. Cisco proposed FC in 2012, which reduces the service delay and the network traffic and network congestion by processing the requests on Fog nodes instead of forwarding them to the Cloud data center from the core network [9][10][11][12][13][14]. If requests require permanent storage or extensive analyses, they are sent to the Cloud.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Delay reduction in MTC using SDN based offloading in Fog computing

2023

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Fog computing (FC) brings a Cloud close to users and improves the quality of service and delay services. In this article, the convergence of FC and Software-Defined-Networking (SDN) has been proposed to implement complicated mechanisms of resource management. SDN has suited the practical standard for FC systems. The priority and differential flow space allocation have been applied to arrange this framework for the heterogeneous request in Machine-Type-Communications. The delay-sensitive flows are assigned to a configuration of priority queues on each Fog. Due to limited resources in the Fog, a promising solution is offloading flows to other Fogs through a decision-based SDN controller. The flow-based Fog nodes have been modeled according to the queueing theory, where polling priority algorithms have been applied to service the flows and to reduce the starvation problem in a multi-queueing model. It is observed that the percentage of delay-sensitive processed flows, the network consumption, and the average service time in the proposed mechanism are improved by about 80%, 65%, and 60%, respectively, compared to traditional Cloud computing. Therefore, the delay reductions based on the types of flows and task offloading is proposed.

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Delay reduction in MTC via SDN based offloading in a Fog Computing environment with the assistance of volunteer nodes

Arefian,

Khayyambashi,

Movahhedinia

2023

Preprint

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This article discusses the integration of Distributed Fog Computing (FC) and Software-Defined Networking (SDN) for efficient resource management in Machine Type Communications (MTC). FC brings Cloud resources closer to the user, enhancing service quality and reducing delays. Some MTC devices have powerful processors that can be used as volunteer nodes to process lightweight requests, thereby increasing the network's distributed processing capabilities. The paper proposes a framework that uses priority and differential flow space allocation to handle heterogeneous requests in MTC and assign delay-sensitive flows to priority queues on each Fog node. To address the limited resources available on individual Fog nodes, the article recommends offloading flows to other Fog nodes and volunteer nodes through a decision-based SDN controller. The article models flow-based Fog nodes using queueing theory, employing priority polling algorithms to service the flows and alleviate the issue of resource starvation in a multi-queueing environment. It is observed that the percentage of delay-sensitive processed flows, the network consumption, and the average service time in the proposed mechanism are improved by about85%, 68%, and 62%, respectively, compared to traditional Cloud computing. Therefore, the delay reductions based on the types of flows and task offloading is proposed.

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A Comparison of QoS-Based Architecture Solutions for IoT/Edge Computing Environment

Cited by 3 publications

References 23 publications

Delay Reduction in MTC Using SDN Based Offloading in Fog Computing

Delay Reduction in MTC Using SDN Based Offloading in Fog Computing

Delay reduction in MTC using SDN based offloading in Fog computing

Delay reduction in MTC via SDN based offloading in a Fog Computing environment with the assistance of volunteer nodes

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