Simulation-based Queueing Models for Performance Analysis of IoT Applications

Bouloukakis, Georgios; Moscholios, Ioannis D.; Georgantas, Nikolaos; Issarny, Valérie

doi:10.1109/csndsp.2018.8471798

Cited by 7 publications

(5 citation statements)

References 20 publications

(24 reference statements)

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“…This paper expands upon our previous work [24] that provides a summary of queueing models that represent the aforementioned QoS constraints of IoT applications. In this paper, we include two queueing systems that model the processing or transmission of diverse IoT messages (e.g., video data vs. temperature data) and the prioritization of urgent data in critical IoT applications (e.g., structural fire scenarios).…”

Section: Introductionmentioning

confidence: 78%

Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models

et al. 2021

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Numerous middleware application programming interfaces (APIs) and protocols were introduced in the literature in order to facilitate the application development of the Internet of Things (IoT). Such applications are built on reliable or even unreliable protocols that may implement different quality-of-service (QoS) delivery modes. The exploitation of these protocols, APIs and QoS modes, can satisfy QoS requirements in critical IoT applications (e.g., emergency response operations). To study QoS in IoT applications, it is essential to leverage a performance analysis methodology. Queueing-network models offer a modeling and analysis framework that can be adopted for the IoT interactions of QoS representation through either analytical or simulation models. In this paper, various types of queueing models are presented that can be used for the representation of various QoS settings of IoT interactions. In particular, we propose queueing models to represent message-drop probabilities, intermittent mobile connectivity, message availability or validity, the prioritization of important information, and the processing or transmission of messages. Our simulation models demonstrate the significant effect on delivery success rates and response times when QoS settings are varied.

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Section: Introductionmentioning

confidence: 78%

Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models

et al. 2021

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“…As above mentioned, refs. [ 37 , 38 ] are the most related works that presented the use of queuing models for comprehensive performance assessment of data transactions and services in IoT infrastructures. We propose to use a queuing network-based message exchange architecture to comprehend the exact performance behaviors and evaluation of the data transactions.…”

Section: Related Workmentioning

confidence: 99%

“…Bouloukakis et al in a recent work, ref. [ 37 ] presented several queuing models to represent QoS settings of IoT interactions. Nevertheless, the models are for different purposes of performance analysis without a proper consideration of system/network architecture.…”

Section: Introductionmentioning

confidence: 99%

IoT Sensor Networks in Smart Buildings: A Performance Assessment Using Queuing Models

Santos

Soares

Nguyen

et al. 2021

Sensors

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Smart buildings in big cities are now equipped with an internet of things (IoT) infrastructure to constantly monitor different aspects of people’s daily lives via IoT devices and sensor networks. The malfunction and low quality of service (QoS) of such devices and networks can severely cause property damage and perhaps loss of life. Therefore, it is important to quantify different metrics related to the operational performance of the systems that make up such computational architecture even in advance of the building construction. Previous studies used analytical models considering different aspects to assess the performance of building monitoring systems. However, some critical points are still missing in the literature, such as (i) analyzing the capacity of computational resources adequate to the data demand, (ii) representing the number of cores per machine, and (iii) the clustering of sensors by location. This work proposes a queuing network based message exchange architecture to evaluate the performance of an intelligent building infrastructure associated with multiple processing layers: edge and fog. We consider an architecture of a building that has several floors and several rooms in each of them, where all rooms are equipped with sensors and an edge device. A comprehensive sensitivity analysis of the model was performed using the Design of Experiments (DoE) method to identify bottlenecks in the proposal. A series of case studies were conducted based on the DoE results. The DoE results allowed us to conclude, for example, that the number of cores can have more impact on the response time than the number of nodes. Simulations of scenarios defined through DoE allow observing the behavior of the following metrics: average response time, resource utilization rate, flow rate, discard rate, and the number of messages in the system. Three scenarios were explored: (i) scenario A (varying the number of cores), (ii) scenario B (varying the number of fog nodes), and (iii) scenario C (varying the nodes and cores simultaneously). Depending on the number of resources (nodes or cores), the system can become so overloaded that no new requests are supported. The queuing network based message exchange architecture and the analyses carried out can help system designers optimize their computational architectures before building construction.

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“…A multi-level simulator was employed, and performance evaluation was conducted. Bouloukakis et al [18] designed queueing network models of IoT applications, and simulated the models for performance evaluation and optimization. Sonmez et al [19] built a simulator tool upon CloudSim for performance evaluation of edge computing systems.…”

Section: Related Work a Measurement-based Experimental Approachesmentioning

confidence: 99%

A Simulation-Based Optimization Approach for Reliability-Aware Service Composition in Edge Computing

2020

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With the prevalence of Internet of Things (IoT), edge computing has emerged as a novel computing model for optimizing traditional cloud computing systems by moving part of the computational tasks to the edge of the network for better performance and security. With the technique of services computing, edge computing systems can accommodate the application requirements with more agility and flexibility. In large-scale edge computing systems, service composition as one of the most important problems in services computing suffers from several new challenges, i.e., complex layered architecture, failures and recoveries always in the lifecycle, and search space explosion. In this paper, we make an attempt at addressing these challenges by designing a simulation-based optimization approach for reliability-aware service composition. Composite stochastic Petri net models are proposed for formulating the dynamics of multi-layered edge computing systems, and their corresponding quantitative analysis is conducted. To solve the state explosion problem in large-scale systems or complex service processes, time scale decomposition technique is applied to improving the efficiency of model solving. Additionally, simulation schemes are designed for performance evaluation and optimization, and ordinal optimization technique is introduced to significantly reduce the size of the search space. Finally, we conduct experiments based on real-life data, and the empirical results validate the efficacy of the approach.

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Simulation-based Queueing Models for Performance Analysis of IoT Applications

Cited by 7 publications

References 20 publications

Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models

Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models

IoT Sensor Networks in Smart Buildings: A Performance Assessment Using Queuing Models

A Simulation-Based Optimization Approach for Reliability-Aware Service Composition in Edge Computing

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