FIFO
An eviction or scheduling policy that preferentially removes the data that entered the queue earliest, using temporal order as a proxy variable for importance.
Timeline (last 90 days)
Both LRU (least recently used) and FIFO (first in, first out) eviction policies treat "time" as a proxy variable for importance, but in the debug capturer scenario, observation frequency is negatively correlated with information value—what appears at high frequency is retry noise, while the malformed payload that appears only once at low frequency is what is actually wanted.
Any combination of bounded storage plus an eviction policy implicitly contains a value judgment about "what is worth keeping"; the default LRU or FIFO presupposes that "time" is a proxy variable for importance, but this fails in scenarios such as retries, deduplication, and anomaly detection.
The traditional scheduling strategy FIFO processes packets only in order of arrival, without considering the value of the data to mission objectives, and is prone to causing service unfairness.
Experimental results show that the proposed method has clear improvements over baseline strategies such as FIFO in both age-based utility and inter-flow fairness.
All Facts (4)
Both LRU (least recently used) and FIFO (first in, first out) eviction policies treat "time" as a proxy variable for importance, but in the debug capturer scenario, observation frequency is negatively correlated with information value—what appears at high frequency is retry noise, while the malformed payload that appears only once at low frequency is what is actually wanted.
50%UnverifiedAny combination of bounded storage plus an eviction policy implicitly contains a value judgment about "what is worth keeping"; the default LRU or FIFO presupposes that "time" is a proxy variable for importance, but this fails in scenarios such as retries, deduplication, and anomaly detection.
50%UnverifiedThe traditional scheduling strategy FIFO processes packets only in order of arrival, without considering the value of the data to mission objectives, and is prone to causing service unfairness.
50%UnverifiedExperimental results show that the proposed method has clear improvements over baseline strategies such as FIFO in both age-based utility and inter-flow fairness.
50%