Estimating Instantaneous Cache Hit Ratio Using Markov Chain Analysis
Bibliographic record
Abstract
This paper introduces a novel analytical model for estimating the cache hit ratio as a function of time. The cache may not reach the steady-state hit ratio when the number of Web objects, object popularity, and/or caching resources themselves are subject to change. Hence, the only way to quantify the hit ratio experienced by Web users is to calculate the instantaneous hit ratio. The proposed analysis considers a single Web cache with infinite or finite capacity. For a cache with finite capacity, two replacement policies are considered: Least Recently Used (LRU) and First-In-First-Out (FIFO). Based on the insights from the proposed analytical model, we propose a new replacement policy, called Frequency-Based-FIFO (FB-FIFO). The results show that FB-FIFO outperforms both LRU and FIFO, assuming that the number of Web objects is fixed. Assuming that new popular objects are generated periodically, the results show that FB-FIFO adapts faster than LRU and FIFO to the changes in the popularity of the cached objects when the cache capacity is large relative to the number of newly generated objects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".