Controlled Distribution of Engineering Jobs in Cloud Services
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Solution Overview
Problem
The deployment of engineering jobs in cloud computing environments often lacks sufficient testing, risk evaluation, and scope control, leading to service interruptions and customer dissatisfaction due to errors and resource overloading.
Innovation Solution
Implementing automated policies based on dynamic risk analysis, scope attributes, and deployment rules, including ring level validation, throttle, cool-down periods, and timing, to control the distribution of engineering jobs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If engineering jobs are deployed quickly and broadly, then productivity is improved, but service reliability deteriorates due to insufficient testing and risk evaluation
Solution Approach 1:
The patent implements preliminary risk evaluation and scope control before deploying engineering jobs. The system assesses potential risks and defines deployment scopes in advance, allowing fast deployment within safe boundaries without compromising service reliability.
Solution Approach 2:
The patent dynamically adjusts deployment parameters such as throttle rates, cool-down periods, and scope limits based on risk assessments. These parameter changes enable controlled deployment speed that maintains both productivity and reliability.
2Productivity
If engineering jobs are deployed without sufficient testing, then productivity is improved, but harmful factors increase due to errors and service interruptions
Solution Approach 1:
The system performs preliminary risk evaluation and validation testing before full deployment. This preliminary action identifies potential errors and harmful effects early, allowing corrections before production deployment while maintaining efficient rollout.
Solution Approach 2:
The patent implements preventive measures including risk assessment, scope validation, and throttle controls before deployment. These preliminary anti-actions counteract potential harmful effects before they can manifest as service interruptions.
3Productivity
If engineering jobs are deployed without scope control, then productivity is improved, but harmful factors increase due to resource overloading
Solution Approach 1:
The patent dynamically adjusts deployment parameters including throttle rates, cool-down periods, and scope limits based on risk assessments and resource capacity. These parameter changes enable high deployment rates without overloading system resources.
Solution Approach 2:
The system implements dynamic scope control that adapts deployment parameters in real-time based on system state and risk levels. This dynamic approach maintains productivity while preventing resource overloading through flexible parameter adjustment.
Data Source
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AI summary
Traditionally, engineers (or developers) of a software provider may implement or maintain aspects of application services by executing jobs or joblets on computing resources of various scopes in a cloud computing environment. However, in some cases, executing certain types of engineer-initiated jobs may negatively impact customer experience and/or satisfaction with the application services. Automated policies for distributing engineer-initiated jobs increase validation testing, scope control, and deployment timing based on a dynamic risk analysis of each job. A multi-faceted approach to distributing engineer-initiated jobs ensures adequate regression testing (e.g., via ring validation and cool-down period) and facilitates controlled distribution (e.g., based on throttle, distribution timing, and deployment train position). An automatic override ensures critical customer outages can be resolved quickly and efficiently by bypassing at least some of the rules. Thereby, customer experience is improved and service interruptions and customer dissatisfaction are minimized when distributing engineer-initiated jobs for application services.