Availability Evaluation and Sensitivity Analysis of a Mobile Backend‐as‐a‐service Platform

Costa, Igor Oliveira; Araújo, Jean; Dantas, Jamilson; Campos, Eliomar; Silva, Francisco Airton; Maciel, Paulo

doi:10.1002/qre.1927

Cited by 25 publications

(9 citation statements)

References 24 publications

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“…Sensitivity analysis is a measure of the effect of a given input data about the output data, aiming to outline the weak links of the computer systems, and from then on, seek to adopt a set of techniques that aim to improve these systems in different scenarios [ 45 ]. Some jobs use sensitivity analysis to provide the necessary security and forward the perspective of system administrators [ 46 , 47 ]. In this work, we have applied a sensitivity analysis with DoE.…”

Section: Sensitivity Analysis With Doementioning

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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Section: Sensitivity Analysis With Doementioning

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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“…Firebase is a BaaS (Backend-as-a-Service) platform from Google that provides several tools for the development of mobile and WEB applications. BaaS platforms are services that allow an application to connect to cloud services, making it possible to implement a database remotely and send notifications [9]. Firebase Realtime Database is a NoSQL database hosted in the cloud.…”

Section: Firebasementioning

confidence: 99%

Control System for Application in Residential Environment Using Bluetooth 5

Bianchetti¹,

Muñoz²

2020

IJETAE

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In the last few decades, there has been an increase in the use of digital devices by the people as a way of facilitate their day-today tasks. Several new devices have been developed for the control and automation of residential environments, thus emerging the smart homes concept that is strongly linked to the Internet of Things. Such devices are used for safety, decoration, child care, domestic management, among many other applications, and are implemented using many wireless communication technologies, data storage, sensors and actuators. In this scenario, Bluetooth low energy technology has played important role. Recently, Bluetooth 5.0 emerged with significant improvements, including twice the speed and four times the range compared to the previous generation. Also, it is possible to keep the Bluetooth on for longer time, since consumes less energy than generation 4.0. This enhanced characteristic has stimulated the use of this technology in home automation applications, and so, we explore this technology in this work. This work is aimed to develop a home automation system, consisting of a central hub and two peripheral modules, being them a Smart Power Plug and a LPG Sensor. The communication between the devices was developed using the Bluetooth ® 5.0 communication protocol and the data were stored in a real-time database of Firebase ® , which also communicates with an Android application. For this purpose, a NodeMCU ® development kit and some boards containing the CC2640R2F microcontroller were used. The results obtained so far, are quite promising to be applied in home automation applications.

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“…In [37] and [38] the availability characteristics of different private cloud infrastructures with a certain number of clusters based on Eucalyptus platform were explored under a variety of fault tolerance strategies such as standby replication mechanisms or software aging avoiding techniques. Costa et al [39] quantified availability for a mobile backend as a service platform (namely, MBaaS OpenMobster platform) linking a data storage system in a cloud DC to real-world mobile devices, which is in order to indentify the critical service component in the overall architecture design. Some other recent works explored reliability/availability related issues of a cloud DC system featured with a network inter-connection among distributed physical DC.…”

Section: Related Work a Reliability And Availability Quantificationmentioning

confidence: 99%

Reliability and Availability Evaluation for Cloud Data Center Networks Using Hierarchical Models

et al. 2019

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Modeling a cloud computing center is crucial to evaluate and predict its inner connectivity reliability and availability. Many previous studies on system availability/reliability assessment of virtualized systems consisting of singular servers in cloud data centers have been reported. In this paper, we propose a hierarchical modeling framework for the reliability and availability evaluation of tree-based data center networks. The hierarchical model consists of three layers, including 1) reliability graphs in the top layer to model the system network topology; 2) a fault-tree to model the architecture of the subsystems; and 3) stochastic reward nets to capture the behaviors and dependency of the components in the subsystems in detail. Two representative data center networks based on three-tier and fat-tree topologies are modeled and analyzed in a comprehensive manner. We specifically consider a number of case-studies to investigate the impact of networking and management on cloud computing centers. Furthermore, we perform various detailed analyses with regard to reliability and availability measures for the system models. The analysis results show that appropriate networking to optimize the distribution of nodes within the data center networks can enhance the reliability/availability. The conclusion of this paper can be used toward the practical management and the construction of cloud computing centers. INDEX TERMS Data center network (DCN), reliability, availability, hierarchical modeling, reliability graph (RG), fault tree (FT), stochastic reward net (SRN).

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Availability Evaluation and Sensitivity Analysis of a Mobile Backend‐as‐a‐service Platform

Cited by 25 publications

References 24 publications

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

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

Control System for Application in Residential Environment Using Bluetooth 5

Reliability and Availability Evaluation for Cloud Data Center Networks Using Hierarchical Models

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