Dynamic Policy Selection for Network Traffic Management
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Solution Overview
Problem
Current solutions for mapping application sessions to user network usage policies are inflexible and do not effectively allow users to control their Internet traffic behavior, particularly in dynamic pricing scenarios, as they require pre-programmed user preferences or manual selection of utility before each application session, which is impractical for average users.
Innovation Solution
A method and system that uses a scoring system to match application sessions with user network usage policies based on stored criteria, allowing for flexible selection of policies that reflect user values, with the option to adjust scores and consider environmental conditions, ensuring appropriate policy selection for different applications and network situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a default user policy is provided by the ISP for all network applications, then the system complexity is reduced and ease of operation is improved, but the adaptability to different application-specific user preferences deteriorates
Solution Approach 1:
The system segments user policies into multiple distinct policies, each tailored to specific application types or tasks. Instead of a single default policy, the system maintains a library of specialized policies that can be selectively applied based on the active application, thereby achieving both ease of operation (automatic selection) and adaptability (application-specific optimization).
Solution Approach 2:
The system dynamically selects and applies appropriate user policies based on the current application session. The policy selection mechanism automatically adapts to changing application contexts, transitioning between different policies as applications are launched or terminated, thus providing adaptability without requiring user intervention.
2Adaptability or versatility
If the user has to select utility before the start of each session, then the adaptability to user preferences is improved, but the loss of time and ease of operation deteriorates
Solution Approach 1:
The system performs preliminary configuration by pre-defining multiple user policies with different utility preferences for various application types. During actual application sessions, the appropriate pre-configured policy is automatically selected and applied, eliminating the need for users to make selections at session start while maintaining adaptability to different application-specific preferences.
Solution Approach 2:
The system implements self-service by automatically selecting and applying the appropriate user policy based on the active application session. The policy selection mechanism operates autonomously without requiring user input, thus achieving adaptability while preventing time loss and operational inconvenience.
3Adaptability or versatility
If inexperienced users manually select utility for each application session, then the adaptability is improved, but the reliability deteriorates due to costly mistakes
Solution Approach 1:
The system introduces an intermediary policy selection mechanism that acts as a mediator between the user and the utility selection process. This intermediary automatically matches the active application with the appropriate pre-configured policy, ensuring reliable and appropriate utility selections without requiring user expertise or manual intervention.
Solution Approach 2:
The system implements self-service by automatically selecting the appropriate user policy based on the active application session. This eliminates the need for user intervention in policy selection, thereby preventing costly mistakes by inexperienced users while maintaining adaptability to different application-specific preferences through pre-configured policies.
Data Source
AI summary
Application sessions are matched to user network usage or buying policies based upon a scoring system which reflects a user value to each policy for different applications. Several user policies are stored, each containing a list of matching criteria. Each criterion has a score for each element. The content of an application session description is used to compare with each user policy, and a policy score is awarded to each criterion if matched, or else, no score is awarded. The user policy which scores the highest policy score (which may be adjusted to be expressed as a percentage) is then chosen as the policy to use for the application session.