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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Analyzing CSP Trustworthiness and Predicting Cloud Service Performance.

IEEE Computer Graphics and Applications

|May 15, 2020

Summary

Cloud computing outages cost businesses millions, yet industry growth continues. This research develops models to predict cloud service provider trustworthiness and availability performance, mitigating financial and operational risks.

Area of Science:

Computer Science
Information Technology
Risk Management

Background:

Cloud computing outages incur significant financial losses for businesses, with a 2017 Amazon outage costing S&P 500 companies $150 million and Fortune 1000 companies facing billions in data center outage costs.
Despite risks, the cloud computing industry is expanding, driven by the Internet of Things (IoT) and multi-cloud strategies, increasing reliance on cloud services for data processing and management.
This growing dependency heightens risks related to cloud service availability, performance, security, and financial impact, necessitating robust assessment methods.

Purpose of the Study:

To develop a solution for calculating cloud service provider (CSP) trustworthiness levels.
To predict the availability performance of cloud services and their associated service level agreements (SLAs).
To mitigate the financial and operational risks associated with cloud computing dependency.

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Main Methods:

Leveraging evolving industry standards for cloud SLAs, such as NIST and ISO/IEC.
Utilizing existing research on CSP trustworthiness.
Constructing regression-based predictive models to analyze CSP services, SLA performance, and trustworthiness.

Main Results:

The study proposes a method to quantify CSP trustworthiness.
Predictive models are developed to forecast cloud service and SLA availability.
The research provides a framework for assessing and managing cloud computing risks.

Conclusions:

Accurate prediction of CSP trustworthiness and SLA availability is crucial for managing cloud computing risks.
The proposed models offer a data-driven approach to enhance cloud service reliability and security.
Proactive risk assessment through these models can safeguard businesses against financial and operational disruptions.