Blockchain Ledger for Cloud Service Provisioning Failure Diagnosis
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Solution Overview
Problem
Cloud service provisioning failures are challenging to diagnose and resolve due to the complexity of inter-system communication and the need for manual intervention, leading to service interruptions and delayed issue resolution.
Innovation Solution
A method that captures API calls and submits them to a blockchain ledger for root cause analysis, identifies problematic systems, and uses smart contracts to generate dummy responses, enabling automatic failure analysis and healing without service interruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention and back-and-forth discussion are used for root cause analysis, then accuracy of diagnosis can be improved, but service provisioning time increases and productivity decreases
Solution Approach 1:
The patent creates a simulated environment that copies the production system's architecture and dependencies. This virtual copy allows root cause analysis to be performed on replicas rather than the live system, enabling thorough investigation without impacting actual service provisioning speed. The simulation captures system state, API calls, and component interactions to recreate failure scenarios for analysis.
Solution Approach 2:
The system performs preliminary actions by proactively capturing and storing API call traces, system state information, and dependency mappings before failures occur. This pre-captured data is stored in the blockchain ledger, enabling immediate root cause analysis when failures happen without requiring manual data collection or system intervention during the actual incident.
2Measurement precision
If thorough root cause analysis is performed by involving multiple parties, then diagnostic accuracy improves, but time required to identify and fix issues increases
Solution Approach 1:
The patent introduces an intermediary simulated environment that mediates between multiple system parties and the analysis process. Instead of requiring direct communication and coordination among multiple stakeholders, the simulation serves as a neutral intermediary where all parties can independently analyze the same replicated failure scenario, eliminating negotiation overhead while maintaining collaborative diagnostic accuracy.
Solution Approach 2:
The system enables self-service by allowing automatic capture, storage, and analysis of failure data without requiring manual intervention from multiple parties. The simulated environment automatically reproduces failure conditions and provides diagnostic information, reducing the need for coordinated human analysis while maintaining thorough investigation capabilities.
3Reliability
If service provisioning is interrupted to fix identified issues, then system reliability improves, but service availability and customer satisfaction worsen
Solution Approach 1:
The patent implements beforehand cushioning by creating a protective simulated environment that absorbs the impact of failure analysis activities. Problems are investigated and fixed in the simulation copy rather than the production system, cushioning the live service from interruptions. This allows comprehensive testing and validation of fixes before applying them to the actual system, maintaining service availability while improving reliability.
Solution Approach 2:
By creating and analyzing failures in a copied simulated environment rather than the production system, the patent enables fix validation without interrupting actual service provisioning. The copy serves as a safe testing ground where reliability improvements can be developed and verified before deployment, maintaining service availability while systematically improving system stability.
Data Source
AI summary
In an approach to blockchain management of cloud service provisioning failures, one or more computer processors capture one or more application programming interface (API) calls associated with a service provision. One or more computer processors submit the captured one or more API calls to a blockchain ledger. One or more computer processors detect a system failure during the service provision. One or more computer processors extract the submitted one or more API calls from the blockchain ledger. Based on the extracted one or more API calls, one or more computer processors identify a problematic system associated with the system failure.