Enhancing Cloud Availability via Intelligent Monitoring using Time Series Database and Machine Learning

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Software Science and Computational Intelligence-IJSSCI Pub Date : 2022-01-01 DOI:10.4018/ijssci.285591
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Abstract

Every cloud provider, wishes to provide 99.9999% availabil- ity for the systems provisioned and operated by them for the customer i.e. may it be SaaS or PaaS or IaaS model, the availability of the system must be greater than 99.9999%.It becomes vital for the provider to mon- itor the systems and take proactive measures to reduce the downtime.In an ideal scenario, the support colleagues (24*7 technical support) must be aware of the on-going issues in the production systems before it is raised as an incident by the customer. But currently, there is no effective alert monitoring solutions for the same. The proposed solution presented in this paper is to have a central alert monitoring tool for all cloud so- lutions offered by the cloud provider. The central alert monitoring tool constantly observes the time series database which contains metric val- ues populated by HA and compares the incoming metric values with the defined thresholds. When a metric value exceeds the defined threshold, using machine learning techniques the monitoring tool decides & takes actions.
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通过使用时间序列数据库和机器学习的智能监控增强云可用性
每个云提供商都希望为其为客户提供和运营的系统提供99.9999%的可用性,即无论是SaaS、PaaS还是IaaS模型,系统的可用性都必须大于99.9999%。提供商监控系统并采取积极措施减少停机时间变得至关重要。在理想的情况下,支持同事(24*7技术支持)必须意识到生产系统中正在发生的问题,然后才能将其作为事件由客户提出。但目前,还没有有效的警报监控解决方案。本文提出的解决方案是为云提供商提供的所有云解决方案提供一个中央警报监控工具。中央警报监控工具不断观察时间序列数据库,该数据库包含HA填充的度量值,并将传入的度量值与定义的阈值进行比较。当度量值超过定义的阈值时,使用机器学习技术,监控工具会决定并采取行动。
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