Preemptive Request Processing via Sequence Tracing
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Solution Overview
Problem
Current data processing systems in computer networks face inefficiencies in handling sequential client application requests, leading to increased latency and processing times, as they often require multiple delegations to service provider applications for subsequent requests, which can be predicted based on access patterns.
Innovation Solution
Implementing a preemptive request processing system that traces sequences of client requests to identify likely follow-ups, generates preemptive processing rules, and simulates predicted requests in a background process, storing data in a cache for immediate retrieval, thereby reducing the need for subsequent delegations to service provider applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential client requests are processed by delegating each request to service provider applications, then request processing is handled systematically, but data access time and processing latency increase
Solution Approach 1:
The system performs preliminary actions by predicting sequential client requests and pre-retrieving the corresponding data from service provider applications before the actual requests arrive. The framework traces request sequences, identifies likely follow-up requests, and executes them in advance, storing results in a data cache. This eliminates the need for sequential delegations when requests arrive, significantly reducing data access time while maintaining reliable processing.
2Manufacturing precision
If multiple delegations are made to service provider applications for sequential requests, then complete data processing is achieved, but system response time increases
Solution Approach 1:
The framework executes preliminary actions by analyzing traced request sequences to predict which requests will follow, then pre-delegating those predicted requests to service provider applications in advance. The pre-computed responses are stored in a data cache, enabling immediate system response when actual requests arrive without requiring multiple sequential delegations, thus maintaining processing completeness while dramatically improving response speed.
Solution Approach 2:
The system creates a copy of the data processing workflow by pre-executing predicted requests and storing their responses in a data cache. When actual requests arrive, the system uses these cached copies instead of executing the full delegation chain, maintaining data processing completeness while reducing system response time by avoiding repeated delegations.
3Ease of operation
If requests are processed in real-time sequence, then immediate response is provided, but processing efficiency decreases due to repeated delegations
Solution Approach 1:
The framework implements preliminary action by predicting sequential requests and pre-processing them in advance. The system traces request sequences, identifies likely follow-ups with probability above a threshold, and executes those requests beforehand, storing results in a data cache. When requests arrive, the system provides immediate response by retrieving pre-processed data from the cache, achieving both immediate response and high processing efficiency by eliminating repeated delegations.
Data Source
AI summary
Embodiments described herein relate to an improved technique for preemptive client application request processing based on observed use access patterns and/or models. The system includes a framework engine operable to trace sequences of requests to one or more service provider applications, including which particular client requests are likely to be followed by other particular client requests for each service. Based on the resulting traces, the framework can determine the probability of a particular request B following another particular request A. When request A is retrieved from the service provider application, and when the probability is high enough (e.g. >50%) that request B will follow request A in the sequence of requests, the framework is operable to simulate request B in a background process and provide a response to request B from a local memory storage.