Two-level cache architecture to reduce memory accesses for IP lookups
Bibliographic record
Abstract
Longest-prefix matching (LPM) is a key processing function of Internet routers. This is an important step in determining which outbound port to use for a given destination address. The time required to look up the outbound port must be less than the minimum inter-arrival time between packets on a given input port. Lookup times can be reduced by caching address prefixes from previous lookups. However all misses in the prefix cache (PC) will initiate a traversal of the routing table to find the longest matching prefix for the destination address. This table is stored in memory so a traversal requires multiple (perhaps many) memory references. These memory references become an increasingly serious bottleneck as line rates increase. In this paper we present a novel second level of caching that can be used to expedite lookups that miss in the PC. We call this second level a dynamic substride cache (DSC). Extensive experiments using real traffic traces and real routing tables show that the DSC is extremely effective in reducing the number of memory references required by a stream of lookups. We also present analytical models to find the optimal partition of a fixed amount of memory between the PC and DSC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".