Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud

Metzger, Florian; Hoβfeld, Tobias; Bauer, André; Kounev, Samuel; Heegaard, Poul E.

doi:10.1109/jproc.2019.2901578

Cited by 111 publications

(56 citation statements)

References 37 publications

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“…According to [35], the Poisson distribution for modeling the traffic of an IoT application to the Cloud is a good approximation for the scalability analysis. Thus, to simplify, we assume that the arrivals of the IoT traffic in a session follow a Poisson distribution.…”

Section: Evaluation Of the Tcfs Packaging (Vnf And Anf)mentioning

confidence: 99%

A Cost-Effective Approach for End-to-End QoS Management in NFV-Enabled IoT Platforms

Ouedraogo¹,

Medjiah²,

Chassot³

et al. 2021

IEEE Internet Things J.

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The systematic scaling approach is used in the literature as the only option to meet QoS requirements in response to the traffic load increase in IoT platforms. Such a solution reduces the load on the congested nodes and leads to an increase in the provisioning costs that, when measured at the scale of billions of devices, may hinder acceptability. Our objective is to enhance the existing approaches using and extending the emerging concept of Virtualized Network Functions (VNF), promoting End-to-End IoT traffic control. We start from the observation that, within Cloud-enabled platforms, the allocated resources are not fully used, making it possible to deploy traffic control network functions (NFs). Instead of systematically scaling a congested node, we dynamically deploy, on under-loaded nodes, additional NFs that exploit the available computing resources to differentiate the traffic processing level and to apply a QoS-oriented policy. The considered NFs extend the notion of VNF defined within Network Function Virtualization (NFV), to take advantage of component-based software design. To enable a possible use of the implemented functions, we formulate a multi-objective optimization problem for the efficient planning of adequate NFs and scaling actions, according to the considered multi-constrained context. The planner, called QoS4NIP (QoS for NFVenabled IoT Platforms), is based on a Genetic Algorithm (GA). The GA metaheuristic relies on biologically inspired operators such as mutation, crossover, and selection. QoS4NIP uses the GA to generate solutions to the identified multi-objective optimization problem by making a series of improvements in an iterative process. The experiments conducted on a realistic case study show that QoS4NIP outperforms autoscalingbased approaches (65% financial cost saved).

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Section: Evaluation Of the Tcfs Packaging (Vnf And Anf)mentioning

confidence: 99%

A Cost-Effective Approach for End-to-End QoS Management in NFV-Enabled IoT Platforms

Ouedraogo¹,

Medjiah²,

Chassot³

et al. 2021

IEEE Internet Things J.

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“…A server can process any packet with a size up to the Maximum Transmission Unit (MTU). We assume that all packets are MTU-sized packets [20], [21] and the service follows a deterministic distribution [22], [23] with a rate 1 s . Each data source can generate different QoS-levels of network traffic at different time slots, and a maximum of k packets can be processed at each service time using c servers.…”

Section: A System Modelmentioning

confidence: 99%

Concurrent Traffic Queuing Game in Smart Home

Attia

Nguyen

Cheriet

2019

2019 15th International Conference on Network and Service Management (CNSM)

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Smart home gateway has to process different types of network traffic generated from several devices in an optimal way to meet their QoS requirements. However, the fluctuation of network traffic distributions results in packets concurrency. Current QoS-aware scheduling methods in the smart home networks do not consider concurrent traffic in their scheduling solutions. This paper presents an analytic model for a QoS-aware scheduling optimization of concurrent smart home network traffic with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffics in smart home network, and propose an innovative queuing design based on the auction economic model of game theory to provide a fair multiple access over different communication channels/ports. Our experiments show the proposed solution achieves an improvement of 14% of packets that meet their required delay and 57% of delay for different number of concurrent flows in the system.

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“…Based on such architecture, LoRaWAN provides connectivity to a massive amount of IoT devices deployed over a vast area by employing a control access mechanism with less complexity at the cost of low throughput [15]. One key characteristic of LoRaWAN resides in its dynamic networking features, such as an IoT device's ability to continually get in and out of the network and to send data with different rate patterns in accordance with application requirements or events [16].…”

Section: Introductionmentioning

confidence: 99%

LoRaWAN Gateway Placement Model for Dynamic Internet of Things Scenarios

Matni

Moraes

Oliveira

et al. 2020

Sensors

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Extended Range Wide Area Network (LoRaWAN) has recently gained a lot of attention from the industrial and research community for dynamic Internet of Things (IoT) applications. IoT devices broadcast messages for neighbor gateways that deliver the message to the application server through an IP network. Hence, it is required to deploy LoRaWAN gateways, i.e., network planning, and optimization, in an environment while considering Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) along with Quality of Service (QoS) requirements. In this article, we introduced a LoRaWAN gateway placement model for dynamic IoT applications called DPLACE. It divides the IoT devices into groups with some degree of similarity between them to allow for the placement of LoRaWAN gateways that can serve these devices in the best possible way. Specifically, DPLACE computes the number of LoRaWAN gateways based on the Gap statistics method. Afterward, DPLACE uses K-Means and Fuzzy C-means algorithms to calculate the LoRaWAN gateway placement. The simulations’ results proved the benefits of DPLACE compared to state-of-the-art LoRaWAN gateway placement models in terms of OPEX, CAPEX, and QoS.

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Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud

Cited by 111 publications

References 37 publications

A Cost-Effective Approach for End-to-End QoS Management in NFV-Enabled IoT Platforms

A Cost-Effective Approach for End-to-End QoS Management in NFV-Enabled IoT Platforms

Concurrent Traffic Queuing Game in Smart Home

LoRaWAN Gateway Placement Model for Dynamic Internet of Things Scenarios

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