The service provided by content delivery networks (CDNs) may overlook content locality, leaving the potential to improve performance. In this study, we explore the feasibility of leveraging generated data as a replacement for fetching data in missing scenarios based on content locality. Due to sufficient local computing resources and reliable generation efficiency, we propose a content-oriented generative-hit framework (CGHit) for CDNs. CGHit utilizes idle computing resources on edge nodes to generate requested data based on similar or related cached data, achieving hits. Extensive experiments in a real-world system demonstrate that CGHit reduces the average access latency by half. In addition, experiments conducted on a simulator confirm that CGHit can enhance current caching algorithms, leading to lower latency and reduced bandwidth usage.

‘‘Learned” admission policies have shown promise in improving Content Delivery Network (CDN) cache performance and lowering operational costs. Unfortunately, existing learned policies are optimized with a few fixed cache sizes while in reality, cache sizes often vary over time in an unpredictable manner. As a result, existing solutions cannot provide consistent benefits in production settings. We present SLAP , a learned CDN cache admission approach based on segmented object reuse time prediction. SLAP predicts an object’s reuse time range using the Long-Short-Term-Memory model and admits objects that will be reused (before eviction) given the current cache size. SLAP decouples model training from cache size, allowing it to adapt to arbitrary sizes. The key to our solution is a novel segmented labeling scheme that makes SLAP without requiring precise prediction on object reuse time. To further make SLAP a practical and efficient solution, we propose aggressive reusing of computation and training on sampled traces to optimize model training, and a specialized predictor architecture that overlaps prediction computation with miss object fetching to optimize model inference. Our experiments using production CDN traces show that SLAP achieves significantly lower write traffic (38%-59%), longer SSDs lifetime (104%-178%), a consistently higher hit rate (3.2%-11.7%), and requires no effort to adapt to changing cache sizes, outperforming existing policies.

As dependence on Content Delivery Networks (CDNs) increases, there is a growing need for innovative solutions to optimize cache performance amid increasing traffic and complicated cache-sharing workloads. Allocating exclusive resources to applications in CDNs boosts the overall cache hit ratio (OHR), enhancing efficiency. However, the traditional method of creating the miss ratio curve (MRC) is unsuitable for CDNs due to the diverse sizes of items and the vast number of applications, leading to high computational overhead and performance inconsistency. To tackle this issue, we propose alightweight andadaptive cachepartitioning scheme called$\varepsilon$-LAP. This scheme uses a corresponding shadow cache for each partition and sorts them based on the average hit numbers on the granularity unit in the shadow caches. During partition resizing,$\varepsilon$-LAP transfers storage capacity, measured in units of granularity, from the$(N-k+1)$-th ($k\leq \frac{N}{2}$) partition to the$k$-th partition. A learning threshold parameter, i.e.,$\varepsilon$, is also introduced to prudently determine when to resize partitions, improving caching efficiency. This can eliminate about 96.8% of unnecessary partition resizing without compromising performance.$\varepsilon$-LAP, when deployed inPicCloudatTencent, improved OHR by 9.34% and reduced the average user access latency by 12.5 ms. Experimental results show that$\varepsilon$-LAP outperforms other cache partitioning schemes in terms of both OHR and access latency, and it effectively adapts to workload variations.

Content delivery networks (CDNs) caching systems usually use multi-tenant shared caching due to their operational simplicity. However, this approach often results in interference among applications. Dynamic cache allocation schemes based on miss ratio curve (MRC) could be a good choice except for its high computational overheads and performance fluctuations. In this paper, we propose a lightweight and adaptive cache allocation scheme for CDNs (LACA). Rather than searching near-optimal configurations for each tenant, LACA detects in real time whether any tenants are using cache space inefficiently (named abnormal tenants), and then adjusts space restricted within these abnormal tenants by constructing their local MRCs instead of the global ones. We have deployed LACA in Tencent's CDN system and LACA can reduce the miss ratio by 27.1 % and reduce the average user access latency by 28.5 ms. Compared with the-state-of-the-art schemes, LACA also achieves a higher-accuracy local MRC with marginal overhead.

"Learned" admission policies have shown promise in improving Content Delivery Network (CDN) cache performance and lowering operational costs. Unfortunately, existing learned policies are optimized with a few fixed cache sizes while in reality, cache sizes often vary over time in an unpredictable manner. As a result, existing solutions cannot provide consistent benefits in production settings.We present SLAP, a learned CDN cache admission approach based on segmented object reuse time prediction. SLAP predicts an object’s reuse time range using the Long-Short-Term-Memory model and admits objects that will be reused (before eviction) given the current cache size. SLAP separates model training from cache size, allowing it to adapt to arbitrary sizes. The key to our solution is a novel segmented labeling scheme that enables SLAP to precisely predict object reuse time. To further make SLAP a practical and efficient solution, we propose aggressive reusing of computation and training on sampled traces to optimize model training, and a specialized predictor architecture that overlaps prediction computation with miss object fetching to optimize model inference. Our experiments with production CDN traces show that SLAP achieves significantly lower write traffic (38%-59%), longer SSDs service life (104%-178%), a consistently higher hit rate (3.2%-11.7%), and requires no effort to adapt to changing cache sizes, outperforming existing policies.