Digital Real‐Time Data Processing with Embedded Systems

Bosse, Stefan; Lehmhus, Dirk

doi:10.1002/9783527679249.ch11

Cited by 1 publication

(1 citation statement)

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“…Studies by Bosse and Lehmhus provide a model for data collection using a structural monitoring and tactile sensing system to obtain data from the lowest system levels. It allows an IT team to assess and monitor the technical condition of the equipment with great accuracy [18]. This research is crucial for our study, because in order to form new metrics and find infrastructure issues' causal relationships, and create an optimal SLO/SLA definition, it is necessary to collect data from all possible levels of web project applications.…”

Section: Literature Reviewmentioning

confidence: 99%

User-Engagement Score and SLIs/SLOs/SLAs Measurements Correlation of E-Business Projects Through Big Data Analysis

et al. 2020

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The Covid-19 crisis lockdown caused rapid transformation to remote working/learning modes and the need for e-commerce-, web-education-related projects development, and maintenance. However, an increase in internet traffic has a direct impact on infrastructure and software performance. We study the problem of accurate and quick web-project infrastructure issues/bottleneck/overload identification. The research aims to achieve and ensure the reliability and availability of a commerce/educational web project by providing system observability and Site Reliability Engineering (SRE) methods. In this research, we propose methods for technical condition assessment by applying the correlation of user-engagement score and Service Level Indicators (SLIs)/Service Level Objectives (SLOs)/Service Level Agreements (SLAs) measurements to identify user satisfaction types along with the infrastructure state. Our solution helps to improve content quality and, mainly, detect abnormal system behavior and poor infrastructure conditions. A straightforward interpretation of potential performance bottlenecks and vulnerabilities is achieved with the developed contingency table and correlation matrix for that purpose. We identify big data and system logs and metrics as the central sources that have performance issues during web-project usage. Throughout the analysis of an educational platform dataset, we found the main features of web-project content that have high user-engagement and provide value to services’ customers. According to our study, the usage and correlation of SLOs/SLAs with other critical metrics, such as user satisfaction or engagement improves early indication of potential system issues and avoids having users face them. These findings correspond to the concepts of SRE that focus on maintaining high service availability.

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Section: Literature Reviewmentioning

confidence: 99%