Conditional Input Data Preservation in Cloud Services
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Solution Overview
Problem
In scenarios where a service fails to provide expected outputs to a requesting entity, the input data may be lost due to default deletion policies, especially when processing tasks exceed resource limits or time constraints, leading to unmet user expectations and data loss.
Innovation Solution
A cloud computing system applies failure criteria to requests, preserving input data when certain conditions are met, such as resource exhaustion or time limits, and stores it in user accounts for later access, ensuring data availability even if expected outputs are not provided.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the cloud computing service deletes input data after successful processing, then storage space is freed and processing efficiency is improved, but data loss occurs when processing fails
Solution Approach 1:
The system dynamically changes the deletion parameter based on processing outcome: input data is deleted when processing succeeds, but preserved when processing fails. This parameter change resolves the contradiction by adapting the data retention policy to the actual processing result, ensuring both efficiency and reliability.
Solution Approach 2:
The system implements feedback by monitoring whether processing tasks succeed or fail, and uses this feedback to determine whether to delete or preserve input data. This feedback mechanism ensures that data is only deleted when processing is successful, preventing data loss while maintaining storage efficiency.
2Reliability
If the cloud computing service preserves input data in all cases, then data loss is prevented, but storage costs increase and resource management efficiency decreases
Solution Approach 1:
The system changes the data retention parameter conditionally: input data is preserved when processing fails but deleted when processing succeeds. This conditional parameter change ensures data safety while avoiding unnecessary storage resource consumption for successfully processed data.
Solution Approach 2:
The system discards input data only when it is no longer needed (after successful processing), and recovers/preserves it when needed (when processing fails). This selective discarding and recovering approach balances data safety with storage resource efficiency.
3Productivity
If the cloud computing service processes more requests simultaneously, then productivity increases, but resource exhaustion and processing failures increase
Solution Approach 1:
The system applies beforehand cushioning by preserving input data before it can be lost in case of processing failures. This protective measure cushions against the increased failure risk when processing more requests simultaneously, allowing higher throughput while maintaining data safety.
Solution Approach 2:
The system converts the harmful effect of processing failures into a beneficial data preservation opportunity. When resource exhaustion causes processing failures, the preserved input data becomes a benefit rather than a loss, allowing the system to maintain high throughput while recovering from failures.
Data Source
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AI summary
This document relates to preserving input data. One example includes obtaining a request that a service perform processing on input data to produce an output representation of the input data. This example also includes applying criteria to the request, and preserving the input data responsive to determining that the criteria are met.