Atdhe Buja, Zana Beqiri Luma


Data Security is a worldwide concern mostly for small medium enterprise (SMEs) and frameworks, approaches, methods are constantly evolving that has a connection with cloud computing, information systems, artificial intelligence, blockchain. Many developers, administrators or product teams running blind. Those are not knowing of problems with their application or do not have the information to fix the problems. The things which can go wrong with web and mobile applications or services is unlimited like dependency failures, resources, and crashes. Main argument is an evaluation of benefits by using Cloud as infrastructure and application on proactive monitoring called Azure Application Insights (AppInsight) towards target like web application, web API, PKI etc. The findings, demonstration of the study should reveal and support our main hypothesis that there is direct link between the proactive monitoring and the main factors that affects utilizing the cloud services. To address this need, in this paper, we introduce AppInsight, the best practice and a model of proactive approach to monitor different targets using Microsoft technology on Azure Cloud services. AppInsight – a model of proactive monitoring includes several functionalities: (1) identifying availability, (2) failures dependencies, (3) performance and (4) using telemetry data generates ad-hoc solution to fix potential failure of web application, web API etc. AppInsight a feature of Azure Monitor used to monitor live applications. AppInsight will automatically detect performance anomalies, and includes powerful analytics tools to help you diagnose issues. You will get a range of telemetry data of analytics of your target which is monitored by AppInsight. To evaluate this tool, we conduct an empirical evaluation by comparing data from actual live monitoring of Y target.

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cloud proactive, microsoft azure, appinsight, monitoring evaluation, government gateway

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D. V. B. Y. Z. Nikhil Saswade, "Virtual machine monitoring in cloud computing," Elsevier, Vols. 7th International Conference on Communication, Computing and Virtualization 2016, pp. 135-142, 2016.

M. L. X. L. J. L. Z. Z. Yan Yu, "Effects of Entrepreneurship and IT Fashion on SMEs’ Transformation toward Cloud Service through Mediation of Trust," INFMAN, 2016.

S. T. R. T. a. S. S. G. d. Shreshth Tulia, "Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing," Elsevier Internet of Things, 2020.

J. T. N. L. Yuli Tian, "Cloud Reliability and Efficiency Improvement via Failure Risk Based Proactive Actions," Journal of Systems and Software - Elsevier, 2020.

M. Corporation, "What is Application Insights?," 06 03 2019. [Online]. Available: [Accessed 14 01 2021].

M. R. Lyu, Handbook of Software Reliability Engineering, CA: IEEE computer society press, 1996.

A. E. A. M. Waheed Iqbal, "Dynamic workload patterns prediction for proactive auto-scaling of web applications," Journal of Network and Computer Applications, 2018.

F. N. O. H. F. K. H. N. K. J. M. S. E. Changa, "Proactive management of SLA violations by capturing relevant external events in a Cloud of Things environment," Future Generation Computer Systems - Elsevier, pp. 26-44, 2018.

Microsoft Corporation, "Monitoring using SCOM and Application Insights," N/A, Kosovo, 2019.

M. A. a. M. M. S. Y.-B. L. c. Z. A. A.-S. S. A. A. Ziyad R. Alashhab a, "Impact of coronavirus pandemic crisis on technologies and cloud computing applications," Journal of Electronic Science and Technology - , 2020.

M. M. ,. E. D. N. Damian A. Tamburri, "Cloud applications monitoring: An industrial study," Information and Software Technology - Elsevier, 2020.

Microsoft Azure Cloud, "Azure Monitor documentation," [Online]. Available:

"davepaquette," 05 02 2020. [Online]. Available: [Accessed 16 01 2021].

Microsoft Cortporation, "Custom metrics in Azure Monitor (Preview)," 01 06 2020. [Online]. Available: [Accessed 16 01 2021].

B. C. A. Karthikeyan, Understanding Azure Monitoring, Springer, 2019.

∗. N. K. a. O. A. W. a. C. E. b. Y. L. b. Cédric St-Onge a, "Detection of time series patterns and periodicity of cloud computing workloads," Future Generation Computer Systems - Elsevier, pp. 249-261, 2020.

"Dynamic Workload Patterns Prediction for Proactive Auto-scaling of Web Applications," Journal of Network and Computer Applications, 2018.

C. Z.-P. L. b. C.-M. W. c. Dennis Linders a, "Proactive e-Governance: Flipping the service delivery model from pull to push in Taiwan," Elsevier, 2015.

*. S. R. P. C. J. M. C. a. J. B. Fernando De la Prieta1, "Survey of agent-based cloud computing applications," Elsevier Future Generation Computer Systems, 2019.

⁎. J. K. N. Yaman Roumania, "An empirical study on predicting cloud incidents," Elsevier , vol. 47, pp. 131-139, 2019.

ENISA, "Cybersecurity for SMEs - Challenges and Recommendations," 28 06 2021. [Online]. Available:

A. Buja, M. Apostolova, A. Luma and Y. Januzaj, "Cyber Security Standards for the Industrial Internet of Things (IIoT)– A Systematic Review," 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), no. IEEE, 2022.

M. W. H. Z. K. T. &. C. A. B. Patrick Dallasega, "Requirement Analysis for the Design of Smart Logistics in SMEs," palgrave macmilan, no. Springer, 2019.

M. W. R. a. W. W. Helmut Zsifkovits, "State-of-the-Art Analysis of the Usage and Potential of Automation in Logistics," no. Springer, 2019.



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