Network Communication Model Conversion for Legacy Applications
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Solution Overview
Problem
Legacy applications often utilize continuous pull requests, which consume significant CPU and network resources, lacking support for notification services and inefficient in modernizing communication models.
Innovation Solution
A method and system that analyze network packets to identify follow-up requests and characterize them as pull model communications, providing notifications for conversion to a push model, optimizing resource utilization by selecting the appropriate communication strategy based on network conditions and request frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If continuous pull requests are used in legacy applications, then communication compatibility is maintained, but CPU and network resources are significantly consumed
Solution Approach 1:
The system dynamically selects between pull and push communication models based on real-time analysis of communication patterns. By monitoring request-response sequences and identifying follow-up requests, the system adapts the communication strategy from static continuous pull to dynamic hybrid approach, reducing resource consumption while maintaining compatibility.
Solution Approach 2:
The invention changes the communication model parameter from continuous pull to push-based notifications for specific request types. By analyzing network packets and identifying patterns where push model is appropriate, the system transforms the communication parameters to optimize resource usage while preserving necessary pull model functionality for legacy compatibility.
2Speed
If continuous pull requests are used to ensure communication responsiveness, then real-time updates are achieved, but network traffic increases significantly
Solution Approach 1:
Instead of continuous periodic pull requests, the system implements event-driven push notifications that occur only when data changes. This transforms the periodic action from regular intervals to event-triggered moments, maintaining real-time responsiveness while eliminating unnecessary network traffic during stable states.
Solution Approach 2:
The system extracts and identifies specific follow-up requests from the communication stream that are suitable for push model conversion. By separating these identifiable patterns from the general pull model traffic, the invention reduces overall network traffic volume while preserving real-time update capabilities for critical communications.
3Ease of manufacture
If legacy applications use pull model communications, then implementation simplicity is maintained, but resource efficiency deteriorates
Solution Approach 1:
The system implements a universal communication management layer that handles both pull and push models through a unified interface. This multi-functionality allows legacy applications to maintain their simple pull model code while the system automatically applies push model optimizations where applicable, preserving implementation simplicity while improving resource efficiency.
Solution Approach 2:
The invention introduces an intermediary communication management system that sits between the legacy application and the network. This mediator analyzes communication patterns and automatically applies the appropriate model (pull or push) without requiring changes to the legacy application code, thus maintaining implementation simplicity while achieving resource efficiency improvements.
Data Source
AI summary
Determining network communication parameters by analyzing network packets at the communication endpoints to determine number of requests and responses; and determining from the number of requests and responses which are follow up requests. The follow up requests are characterized as communications being performed with a pull model. Notifications are provided of applications employing pull model communications with the client for conversion to push model.