Self-Training Storage System for Dynamic Execution Mode Classification
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Solution Overview
Problem
Conventional content management systems face inefficiencies and user dissatisfaction due to predefined execution modes, leading to slow response times and resource burdens when processing resource-intensive requests, especially under heavy system loads.
Innovation Solution
A self-training storage system dynamically optimizes execution modes in real-time using machine learning models that predict optimal execution modes based on request parameters, user policies, and historical data, combining proposed modes with weightage systems to determine the ultimate execution mode for efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If predefined execution modes are used to process user requests, then system simplicity is maintained, but response time deteriorates and system resources are burdened under heavy loads
Solution Approach 1:
The system dynamically selects execution modes based on real-time system load conditions and request characteristics. Instead of using fixed predefined modes, the system adapts its execution mode selection to current conditions, switching between synchronous, asynchronous, batch, scheduled, and queued modes optimally. This dynamic adaptation resolves the contradiction by maintaining simplicity through automated selection while improving response time through context-aware mode choice.
Solution Approach 2:
The system changes the execution mode parameter based on system load and request properties. By monitoring system conditions and adjusting the execution mode parameter dynamically, the system can optimize response time without increasing structural complexity. The parameter change approach allows the same system architecture to achieve better performance under different conditions.
2Reliability
If synchronous execution mode is used to ensure complete processing before user action, then processing reliability is improved, but system resources are heavily burdened and user interface responsiveness deteriorates
Solution Approach 1:
The system dynamically determines whether to use synchronous or asynchronous execution based on request characteristics and system load. For time-critical requests, synchronous mode ensures reliability; for batch operations, asynchronous mode improves resource efficiency. This dynamic selection resolves the contradiction by matching execution mode to task requirements rather than using a fixed mode for all requests.
3Speed
If more system resources are allocated to process resource-intensive requests, then processing speed is improved, but system load increases and overall system performance deteriorates
Solution Approach 1:
The system applies partial action by selecting appropriate execution modes that allocate resources judiciously. Instead of always allocating maximum resources to individual requests, the system uses asynchronous processing, batching, or queuing to distribute resource allocation over time. This partial action approach maintains acceptable processing speed while preventing system overload and preserving overall system performance.
Data Source
AI summary
Embodiments of the present invention are directed to techniques for optimizing an execution mode used to process a request. A self-training storage system can determine one or more proposed execution modes based on a real-time evaluation of various factors, including a history of execution modes used to process historical requests. This history can serve as training data for a machine learning model that can predict an execution mode based on one or more request parameters. This predicted execution mode can be taken as one of a number of proposed execution modes that can be combined using a weightage system to automatically classify an ultimate execution mode used to process the request. Associated weights for combining proposed execution modes can be determined by training a machine learning model using a training set constructed based on a survey of user satisfaction with a determined execution mode.