Video caching has been a basic network functionality in today's network architectures. Although the abundance of caching replacement algorithms has been proposed recently, these methods all suffer from a key limitation: due to their immature rules, inaccurate feature engineering or unresponsive model update, they cannot strike a balance between the long-term history and short-term sudden events. To address this concern, we propose LA-E2, a long-short-term fusion caching replacement approach, which is based on a learning-aided exploration-exploitation process. Specifically, by effectively combining the deep neural network (DNN) based prediction with the online exploitation-exploration process through a method, LA-E2 can both make use of the historical information and adapt to the constantly changing popularity responsively. Through the extensive experiments in two real-world datasets, we show that LA-E2 can achieve state-of-the-art performance and generalize well. Especially when the cache size is small, our approach can outperform the baselines by 17.5 -68.7 higher in total hit rate.
Content caching has been a basic network functionality in today's network architectures such as content delivery network (CDN) @cite_9 and 5G networking @cite_2 . Due to the simplicity of implementation, most currently used caching algorithms are still based on FIFO, LRU, and LFU @cite_10 . However, the effectiveness of these algorithms requires the presumption of the request patterns (e.g., Poisson arrival), which is always problematic in the real world.
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Striking a balance between the costs for Web content providers and the quality of service for Web customers. Content Delivery Networks (CDNs) differ from other caching systems in terms of both workload characteristics and performance metrics. However, there has been little prior work on large-scale measurement and characterization of content requests and caching performance in CDNs. For workload characteristics, CDNs deal with extremely large content volume, high content diversity, and strong temporal dynamics. For performance metrics, other than hit ratio, CDNs also need to minimize the disk operations and the volume of traffic from origin servers. In this paper, we conduct a large-scale measurement study to characterize the content request patterns using real-world data from a commercial CDN provider. Small cells constitute a promising solution for managing the mobile data growth that has overwhelmed network operators. Local caching of popular content items at the small cell base stations (SBSs) has been proposed to decrease the costly transmissions from the macrocell base stations without requiring high capacity backhaul links for connecting the SBSs with the core network. However, the caching policy design is a challenging problem especially if one considers realistic parameters such as the bandwidth capacity constraints of the SBSs that can be reached in congested urban areas. We consider such a scenario and formulate the joint routing and caching problem aiming to maximize the fraction of content requests served locally by the deployed SBSs. This is an NP-hard problem and, hence, we cannot obtain an optimal solution. Thus, we present a novel reduction to a variant of the facility location problem, which allows us to exploit the rich literature of it, to establish algorithms with approximation guarantees for our problem. Although the reduction does not ensure tight enough bounds in general, extensive numerical results reveal a near-optimal performance that is even up to 38 better compared to conventional caching schemes using realistic system settings.