IRS is enhanced to achieve scalability according to the required quantity model The objective is to modify the application architecture and infrastructure to handle an increased volume of data, transactions, (and user interactions efficiently), without compromising performance, availability, or reliability.
What's the benefit?
This change increases the efficiency and scalability of IRS by allowing it to handle more transactions and data volume, thus meeting future demand. It also reduces the complexity by identifying and addressing system bottlenecks and optimizing shared components.
What are the Risks/Dependencies ?
Breaking Change: Changes to the architecture could potentially introduce breaking changes to interfaces or performance.
Test Environments: Adequate testing in performance and stress testbeds is essential to minimize the risk of underperformance under heavy loads.
Detailed explanation
Analyze Scalability Limitations of IRS:
Evaluation of existing IRS architecture, infrastructure, and database capabilities to identify bottlenecks and areas limiting scalability.
Gather data on current performance metrics, including response times, throughput, and resource utilization under various loads.
Analyze Scalability Limitations of used (shared) components
Evaluation of limitations of used shared services
EDC
dDTR (Decentral Digital Twin Registry)
Submodel Servers
Create Testbeds
Create performance and stress test environments to track and measure the efficiency of the implemented improvements. These tests will validate the effectiveness of the architectural changes and ensure no performance degradation.
Current implementation
Proposed improvements
Transition to a more scalable architecture, which may include load balancing, distributed databases, and horizontal scaling of critical components.
Optimize the IRS and shared components to reduce latency, improve data flow, and ensure seamless interaction with external systems (EDC, dDTR).
Implement a mechanism for dynamic scaling based on system load.
[ ] IRS system can handle a predefined threshold of data volume and transactions (as per quantity model) without performance degradation.
[ ] The system remains stable under stress and load testing, with response times not exceeding a defined limit
Dynamic Scaling:
[ ] IRS infrastructure dynamically scales to manage varying loads, and autoscaling policies are in place to optimize resource usage based on load fluctuations.
[ ] Scaling mechanisms do not introduce any downtime or service interruptions during peak loads.
Seamless Integration with Shared Components:
[ ] IRS successfully interacts with shared services (EDC, dDTR, Submodel Servers) under high loads, with no critical errors or timeouts in communication.
[ ] Shared services continue to perform efficiently, with their respective response times maintained within acceptable limits during IRS operations.
Test Cases
Test Case 1
Steps
Do something
Click something
Add something
Expected Result
Expectation
Expectation
Expectation
Architectural Relevance
The following items are ensured (answer: yes) after this issue is implemented:
[ ] This feature aligns with our current architectural guidelines
[ ] The impact on the overall system architecture has been assessed. The Feature does not require changes to the architecture or any existing standard? Please have a look here on the overarching architecture
[ ] Potential risks or conflicts with existing architecture has been assessed
Justification:(Fill this out, if at least one of the checkboxes above cannot be ticked. Contact the Architecture Management Committee to get an approval for the justification)
Additional information
[x] I am aware that my request may not be developed if no developer can be found for it. I'll try to contribute a developer (bring your own developer)
Overview
Explain the topic in 2 sentences
IRS is enhanced to achieve scalability according to the required quantity model The objective is to modify the application architecture and infrastructure to handle an increased volume of data, transactions, (and user interactions efficiently), without compromising performance, availability, or reliability.
What's the benefit?
This change increases the efficiency and scalability of IRS by allowing it to handle more transactions and data volume, thus meeting future demand. It also reduces the complexity by identifying and addressing system bottlenecks and optimizing shared components.
What are the Risks/Dependencies ?
Detailed explanation
Analyze Scalability Limitations of used (shared) components
Evaluation of limitations of used shared services
Current implementation
Proposed improvements
Feature Team
Contributor
Committer
User Stories
Acceptance Criteria
Scalability Performance:
Dynamic Scaling:
Seamless Integration with Shared Components:
Test Cases
Test Case 1
Steps
Expected Result
Architectural Relevance
The following items are ensured (answer: yes) after this issue is implemented:
Justification: (Fill this out, if at least one of the checkboxes above cannot be ticked. Contact the Architecture Management Committee to get an approval for the justification)
Additional information