Active Probe Target Management for Network Reachability
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Solution Overview
Problem
In computer networks, determining the validity of targets for probe packets is challenging, as non-responding targets can be due to network issues or other reasons, leading to incorrect conclusions about network reachability and performance.
Innovation Solution
A novel target management technique dynamically selects an optimal set of valid targets by sending probe packets to a pool of possible targets for an evaluation period, deselecting non-responding targets, and replacing them with unused targets, while invalid learned targets are discarded and configured targets are returned to the pool for potential re-selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If probe packets are sent to targets to measure network parameters, then network performance can be monitored, but non-responding targets may lead to incorrect conclusions about network reachability
Solution Approach 1:
The system performs preliminary actions by sending probe packets to multiple targets before making reachability determinations. The source sends probe packets to a set of targets and waits for responses during an evaluation period, ensuring that multiple potential targets are tested before concluding whether a prefix is reachable, thereby preventing incorrect conclusions from single non-responding targets.
Solution Approach 2:
The system uses feedback from target responses to dynamically manage the set of active targets. Based on which targets respond to probe packets, the source adjusts the evaluation process and determines reachability. The feedback mechanism ensures that only targets providing useful information (responding targets) influence the reachability determination, while non-responding targets are appropriately handled.
2Reliability
If multiple targets are monitored to improve measurement accuracy, then network parameter reliability improves, but system complexity increases
Solution Approach 1:
The system dynamically manages the set of active targets based on their responsiveness. Targets are added to or removed from the active set during evaluation periods based on whether they respond to probe packets. This dynamic adjustment allows the system to maintain measurement reliability while adapting the complexity level based on actual network conditions and target behavior.
Solution Approach 2:
The system changes parameters such as the evaluation period duration, the number of probe packets sent, and the set of active targets based on observed network conditions. By adjusting these parameters dynamically, the system optimizes the balance between measurement reliability and management complexity for different network scenarios.
3Measurement precision
If targets are selected from a pool of possible targets, then valid representatives can be identified, but time is lost in evaluation periods
Solution Approach 1:
The system uses periodic evaluation periods to assess target validity. During each evaluation period, probe packets are sent to targets and responses are collected. After the period expires, the source determines which targets were valid based on their responses. This periodic approach allows thorough evaluation of target validity while managing time through structured intervals rather than continuous assessment.
Data Source
AI summary
A technique manages targets to which probe packets are sent from a source of a computer network. The novel target management technique dynamically selects an optimal set of valid targets for a particular (monitored) prefix of the computer network. Specifically, one or more targets are selected from a pool of possible learned and configured targets to be used as representatives of the monitored prefix. Probe packets are sent from the source to the selected targets for the duration of an evaluation period. Targets that do not respond to the probe packets during the evaluation period are invalid and deselected, and if available, an unused target is selected from the pool of possible targets to replace each non-responding target for a next evaluation period. Invalid learned targets are discarded, while invalid configured targets are returned to the pool of possible targets for potential re-selection in a later evaluation period.