English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

When facing objects/files of differing sizes in content delivery networks(CDNs) caches, pursuing an optimal object miss ratio (OMR) by approximatingBelady no longer ensures an optimal byte miss ratio (BMR), creating confusionabout how to achieve a superior BMR in CDNs. To address this issue, weexperimentally observe that there exists a time window to delay the eviction ofthe object with the longest reuse distance to improve BMR without increasingOMR. As a result, we introduce a deep reinforcement learning (RL) model tocapture this time window by dynamically monitoring the changes in OMR and BMR,and implementing a BMR-friendly policy in the time window. Based on thispolicy, we propose a Belady and Size Eviction (LRU-BaSE) algorithm, reducingBMR while maintaining OMR. To make LRU-BaSE efficient and practical, we addressthe feedback delay problem of RL with a two-pronged approach. On the one hand,our observation of a rear section of the LRU cache queue containing most of theeviction candidates allows LRU-BaSE to shorten the decision region. On theother hand, the request distribution on CDNs makes it feasible to divide thelearning region into multiple sub-regions that are each learned with reducedtime and increased accuracy. In real CDN systems, compared to LRU, LRU-BaSE canreduce "backing to OS" traffic and access latency by 30.05\% and 17.07\%,respectively, on average. The results on the simulator confirm that LRU-BaSEoutperforms the state-of-the-art cache replacement policies, where LRU-BaSE'sBMR is 0.63\% and 0.33\% less than that of Belady and Practical Flow-basedOffline Optimal (PFOO), respectively, on average. In addition, compared toLearning Relaxed Belady (LRB), LRU-BaSE can yield relatively stable performancewhen facing workload drift.

Optimizing Replacement Policies for Content Delivery Network Caching: Beyond Belady to Attain A Seemingly Unattainable Byte Miss Ratio