Evaluating the Efficiency of Caching Strategies in Reducing Application Latency
Bibliographic record
Abstract
The paper discusses the efficiency of various caching strategies that can reduce application latency. A test application was developed for this purpose to measure latency from various conditions using logging and profiling tools. These scenario tests simulated high traffic loads, large data sets, and frequent access patterns. The simulation was done in Java; accordingly, T-tests and ANOVA were conducted in order to measure the significance of the results. The findings showed that the highest reduction in latency was achieved by in-memory caching: response time improved by up to 62.6% compared to non-cached scenarios. File-based caching decreased request processing latency by about 36.6%, while database caching provided an improvement of 55.1%. These results enhance the huge benefits stemming from the application of various caching mechanisms. In-memory caching proved most efficient in high-speed data access applications. On the other hand, file-based and database caching proved to be more useful in certain content-heavy scenarios. This research study provides some insight for developers on how to identify proper caching mechanisms and implementation to further boost responsiveness and efficiency of applications. Other recommendations for improvements to be made on the cache involve hybrid caching strategies, optimization of the eviction policies further, and integrating mechanisms with edge computing for even better performance.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".