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1. Towards latency awareness for content delivery network caching

   Gang, Yan ; Li, Jian ( July 2022 , Annual Technical Conference)

   2022 USENIX Annual Technical Conference (Ed.)

   Caches are pervasively used in content delivery networks (CDNs) to serve requests close to users and thus reduce content access latency. However, designing latency-optimal caches are challenging in the presence of delayed hits, which occur in high-throughput systems when multiple requests for the same content occur before the content is fetched from the remote server. In this paper, we propose a novel timer-based mechanism that provably optimizes the mean caching latency, providing a theoretical basis for the understanding and design of latency-aware (LA) caching that is fundamental to content delivery in latency-sensitive systems. Our timer-based model is able to derive a simple ranking function which quickly informs us the priority of a content for our goal to minimize latency. Based on that we propose a lightweight latency-aware caching algorithm named LA-Cache. We have implemented a prototype within Apache Traffic Server, a popular CDN server. The latency achieved by our implementations agrees closely with theoretical predictions of our model. Our experimental results using production traces show that LA-Cache consistently reduces latencies by 5%-15% compared to state-of-the-art methods depending on the backend RTTs. 

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2. Delayed Hits in Multi-Level Caches

   Carleton, Benjamin ( January 2021 , Symposium on Operating Systems Principles (Poster Session))

   Traditional caching models emphasize hit rate as the principal measure of performance for cache replacement algorithms. However, hit rate alone can be misleading in the presence of a phenomenon known as a delayed hit. Delayed hits occur in high-throughput systems when multiple requests for an object accumulate before the object can be fetched from the backing store. Prior work by Atre et al. has explored the impact of delayed hits in simple caching scenarios, namely single-tier caches with uniform object sizes. In this work we seek to extend that investigation to consider multi-level caches, such as those that might be found in a modern CDN. Furthermore, we extend MAD, the delayed-hits-aware policy proposed by Atre et al, so that it can be deployed in a multi-tier caching system. We evaluate the performance of MAD using a multi-tier cache simulator and an empirical cache configuration based on modern CDNs. Our initial results lead us to believe that delayed hits can still be a prominent factor in the performance of multi-level caches, although their effect may be reduced in comparison to simpler cache configurations. 

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3. Learning from optimal caching for content delivery

   https://doi.org/10.1145/3485983.3494855  

   Yan, Gang ; Li, Jian ; Towsley, Don ( December 2021 , The 17th International Conference on emerging Networking EXperiments and Technologies (ACM CoNEXT))

   Content delivery networks (CDNs) distribute much of today's Internet traffic by caching and serving users' contents requested. A major goal of a CDN is to improve hit probabilities of its caches, thereby reducing WAN traffic and user-perceived latency. In this paper, we develop a new approach for caching in CDNs that learns from optimal caching for decision making. To attain this goal, we first propose HRO to compute the upper bound on optimal caching in an online manner, and then leverage HRO to inform future content admission and eviction. We call this new cache design LHR. We show that LHR is efficient since it includes a detection mechanism for model update, an auto-tuned threshold-based model for content admission with a simple eviction rule. We have implemented an LHR simulator as well as a prototype within an Apache Traffic Server and the Caffeine, respectively. Our experimental results using four production CDN traces show that LHR consistently outperforms state of the arts with an increase in hit probability of up to 9% and a reduction in WAN traffic of up to 15% compared to a typical production CDN cache. Our evaluation of the LHR prototype shows that it only imposes a moderate overhead and can be deployed on today's CDN servers. 

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4. Adaptive TTL-Based Caching for Content Delivery

   Basu, Soumya ; Sundarrajan, Aditya ; Ghaderi, Javad ; Shakkottai, Sanjay ; Sitaraman, Ramesh ( April 2018 , IEEE/ACM transactions on networking)

   Content delivery networks (CDNs) cache and serve a majority of the user-requested content on the Internet. Designing caching algorithms that automatically adapt to the heterogeneity, burstiness, and non-stationary nature of real-world content requests is a major challenge and is the focus of our work. While there is much work on caching algorithms for stationary request traffic, the work on non-stationary request traffic is very limited. Consequently, most prior models are inaccurate for non-stationary production CDN traffic. We propose two TTL-based caching algorithms that provide provable performance guarantees for request traffic that is bursty and nonstationary. The first algorithm called d-TTL dynamically adapts a TTL parameter using stochastic approximation. Given a feasible target hit rate, we show that d-TTL converges to its target value for a general class of bursty traffic that allows Markov dependence over time and non-stationary arrivals. The second algorithm called f-TTL uses two caches, each with its own TTL. The first-level cache adaptively filters out non-stationary traffic, while the second-level cache stores frequently-accessed stationary traffic. Given feasible targets for both the hit rate and the expected cache size, f-TTL asymptotically achieves both targets. We evaluate both d-TTL and f-TTL using an extensive trace containing more than 500 million requests from a production CDN server. We show that both d-TTL and f-TTL converge to their hit rate targets with an error of about 1.3%. But, f-TTL requires a significantly smaller cache size than d-TTL to achieve the same hit rate, since it effectively filters out non-stationary content. 

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5. RL-Cache: Learning-Based Cache Admission for Content Delivery

   https://doi.org/10.1109/JSAC.2020.3000415  

   Kirilin, Vadim ; Sundarrajan, Aditya ; Gorinsky, Sergey ; Sitaraman, Ramesh K. ( June 2020 , IEEE Journal on Selected Areas in Communications)

   Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to determine which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose a novel algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN’s cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai’s CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region. The paper also reports extensive analyses of the RL-Cache sensitivity to its features and hyperparameter values. The analyses validate the made design choices and reveal interesting insights into the RL-Cache behavior. 

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