Evaluating the Efficiency of Caching Strategies in Reducing Application Latency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it