Cloud Service Trustworthiness Prediction via Graph Theory and ML

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Solution Overview

Problem

Current solutions lack a comprehensive approach to calculating and predicting cloud service provider (CSP) trustworthiness and cloud service level agreement (SLA) compliance, making it difficult for organizations to manage risk and set realistic service levels due to proprietary and inconsistent SLAs, lack of transparent performance reporting, and dependency on multiple CSPs.

Innovation Solution

A system utilizing Graph theory, multi-criteria decision analysis (MCDA), and machine learning regression models to evaluate CSP trustworthiness based on multiple criteria, including historical performance, security controls, and industry standards, predicting future cloud service and SLA compliance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If CSPs use proprietary and minimal SLAs, then CSP flexibility and autonomy are improved, but transparency and comparability of service levels deteriorate

Engineering Contradiction:

ImproveCSP SLA flexibilityVSAvoidSLA transparency

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The patent introduces an intermediary system that acts as a mediator between CSPs and customers. This system standardizes SLA metrics and provides a common framework for measuring and comparing cloud service performance across different providers, enabling transparency without restricting CSP flexibility in designing their service offerings

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent creates a universal SLA measurement framework that can be applied across multiple CSPs and cloud service types. This universal system allows for consistent evaluation of service levels across different providers while accommodating various service models and CSP-specific implementations

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If organizations increase dependency on multiple CSPs, then service capabilities and scalability are improved, but risk management and governance complexity increase

Engineering Contradiction:

Improvecloud service scalabilityVSAvoidmulti-CSP governance complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the complex multi-CSP management problem into standardized, measurable components. By breaking down service level agreements into discrete, comparable metrics and using systematic evaluation methods, the patent enables organizations to manage multiple CSPs through structured assessment rather than unwieldy holistic management

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transforms qualitative governance challenges into quantitative parameters that can be measured and compared. By establishing specific performance metrics and evaluation criteria, the system converts complex governance decisions into data-driven assessments of CSP trustworthiness and service level compliance

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If comprehensive CSP evaluation methods are implemented, then service level prediction accuracy is improved, but computational complexity and data requirements increase

Engineering Contradiction:

ImproveSLA compliance prediction accuracyVSAvoidevaluation system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent performs preliminary actions by establishing standardized measurement frameworks and collecting baseline data before actual service delivery. This preparatory work includes defining evaluation metrics, setting up data collection mechanisms, and creating comparison benchmarks that simplify subsequent analysis and reduce computational complexity during operational evaluation

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms that continuously monitor CSP performance against standardized metrics and provide systematic evaluation results. This feedback loop enables ongoing assessment of SLA compliance and CSP trustworthiness, improving prediction accuracy through iterative learning while maintaining manageable system complexity through structured information flow

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11615328B2System and method for analyzing cloud service provider trustworthiness and for predicting cloud service level agreement performance

Publication Date: 2023.03.28 GEORGE WASHINGTON UNIVERSITY

AI summary

Effective management of cloud computing service levels (e.g. availability, performance, security) and financial risk is dependent on the cloud service provider's (CSP's) capability and trustworthiness. The invention applies industry cloud service level agreements (SLAs) with statistical and machine learning models for assessing CSPs and cloud services, and predicting performance and compliance against service levels. Cloud SLAs (ISO/IEC, EC, ENISA), cloud security requirements and compliance (CSA CCM, CAIQ), along with CSP performance (SLAs, cloud services) are analyzed via Graph Theory analysis and MCDA AHP to calculate CSP trustworthiness levels. CSP trustworthiness levels are input with CSP SLA content, cloud service performance measurements and configuration parameters into machine learning Regression analysis models to predict CSP cloud service performance and cloud SLA compliance, and enable model analysis and comparison. This can be used to determine which regression variables provide the highest predictive accuracy, enabling cloud service customers (CSCs) and CSPs opportunities for transparency, traceability and effective governance of cloud service levels, cloud services and management of risk.