This work presents, to the best of our knowledge of the literature, the first analytic model to address the performance of an LRU (Least Recently Used) implementing cache under non-stationary traffic conditions, i.e., when the popularity of content evolves with time. We validate the accuracy of the model using Monte Carlo simulations. We show that the model is capable of accurately estimating the cache hit probability, when the popularity of content is non-stationary.
We find that there exists a dependency between the performance of an LRU implementing cache and i) the lifetime of content in a system, ii) the volume of requests associated with it, iii) the distribution of content request volumes and iv) the shape of the popularity profile over time.
A key-value cache is a key component of many services to provide low-latency and high-throughput data accesses to a huge amount of data. To improve the end-to-end performance of such services, a key-value cache must achieve a high cache hit ratio with high throughput. In this paper, we propose a new cache replacement algorithm, multi-step LRU, which achieves high throughput by efficiently exploiting SIMD instructions without using per-item additional memory (LRU metadata) to record information such as the last access timestamp. For a small set of items that can fit within a vector register, SI…
Content-delivery applications can achieve scalability and reduce wide-area network traffic using geographically distributed caches. However, each deployed cache has an associated cost, and under time-varying request rates (e.g., a daily cycle) there may be long periods when the request rate from the local region is not high enough to justify this cost. Cloud computing offers a solution to problems of this kind, by supporting dynamic allocation and release of resources. In this paper, we analyze the potential benefits from dynamically instantiating caches using resources from cloud service prov…
When facing objects/files of differing sizes in content delivery networks (CDNs) caches, pursuing an optimal object miss ratio (OMR) by approximating Belady no longer ensures an optimal byte miss ratio (BMR), creating confusion about how to achieve a superior BMR in CDNs. To address this issue, we experimentally observe that there exists a time window to delay the eviction of the object with the longest reuse distance to improve BMR without increasing OMR. As a result, we introduce a deep reinforcement learning (RL) model to capture this time window by dynamically monitoring the changes in OMR…
We consider a content-caching system thatis shared by a number of proxies. The cache could belocated in an edge-cloud datacenter and the proxies couldeach serve a large population of mobile end-users. Eachproxy operates its own LRU-list of a certain capacity inthe shared cache. The length of objects simultaneouslyappearing in plural LRU-lists is equally divided amongthem,i.e., object sharing among the LRUs. We provide a "working-set" approximation for this system to quicklyestimate the cache-hit probabilities under such objectsharing, which can be used to facilitate admission control.Also, a w…
Tandon School of Engineering, New York University（纽约大学坦登工程学院）；New York Institute of Technology（纽约理工学院）
Edge caching will play a critical role in facilitating the emerging content-rich applications. However, it faces many new challenges, in particular, the highly dynamic content popularity and the heterogeneous caching configurations. In this paper, we propose Cocktail Edge Caching, that tackles the dynamic popularity and heterogeneity through ensemble learning. Instead of trying to find a single dominating caching policy for all the caching scenarios, we employ an ensemble of constituent caching policies and adaptively select the best-performing policy to control the cache. Towards this goal, w…
Chongqing University（重庆大学）；Binghamton University, State University of New York（纽约州立大学宾汉姆顿大学）；Swinburne University of Technology（斯威本科技大学）；Harbin Institute of Technology（哈尔滨工业大学）
As ubiquitous and personalized services are growing boomingly, an increasingly large amount of traffic is generated over the network by massive mobile devices. As a result, content caching is gradually extending to network edges to provide low-latency services, improve quality of service, and reduce redundant data traffic. Compared to the conventional content delivery networks, caches in edge networks with smaller sizes usually have to accommodate more bursty requests. In this paper, we propose an evolving learning-based content caching policy, named PA-Cache in edge networks. It adaptively le…
We propose a decentralized caching policy for wireless heterogeneous networks that makes content placement decisions based on pairwise interactions between cache nodes. We call our proposed scheme γ-exclusion cache placement (GEC), where a parameter γ controls an exclusion radius that discourages nearby caches from storing redundant content. GEC takes into account item popularity and the nodes' caching priorities and leverages negative dependence to relax the classic 0-1 knapsack problem to yield spatially balanced sampling across caches. We show that GEC guarantees a better concentration (red…
