SwiftCache: Model-Based Learning for Dynamic Content Caching in CDNs

We introduce SwiftCache, a "fresh" learning-based caching framework designed for content distribution networks (CDNs) featuring distributed front-end local caches and a dynamic back-end database. Users prefer the most recent version of the dynamically updated content, while the local caches lack knowledge of item popularity and refresh rates. We first explore scenarios with requests arriving at a local cache following a Poisson process, whereby we prove that the optimal policy features a threshold-based structure with updates occurring solely at request arrivals. Leveraging these findings, SwiftCache is proposed as a model-based learning framework for dynamic content caching. The simulation demonstrates near-optimal cost for Poisson process arrivals and strong performance with limited cache sizes. For more general environments, we present a model-free Reinforcement Learning (RL) based caching policy without prior statistical assumptions. The model-based policy performs well compared to the model-free policy when the variance of interarrival times remains moderate. However, as the variance increases, RL slightly outperforms model-based learning at the cost of longer training times, and higher computational resource consumption. Model-based learning's adaptability to environmental changes without retraining positions it as a practical choice for dynamic network environments. Distributed edge caches can utilize this approach in a decentralized manner to effectively meet the evolving behaviors of users.Comment: arXiv admin note: text overlap with arXiv:2401.0361

The role of social media content format and platform in users’ engagement behavior

The purpose of this study is to understand the role of social media content on users' engagement behavior. More specifically, we investigate: (i)the direct effects of format and platform on users' passive and active engagement behavior, and (ii) we assess the moderating effect of content context on the link between each content type (rational, emotional, and transactional content) and users' engagement. The dataset contained 1,038 social media posts and 1,336,741 and 95,996 fan likes and comments, respectively based on Facebook and Instagram. The results reveal that the effectiveness of social media content on users' engagement is moderated by content context. The findings contribute to understanding engagement and users' experience with social media. This study is a pioneering one to empirically assess the construct of social media engagement behavior through the effects of content types and content contexts on a dual social media platform

Data freshness and data accuracy :a state of the art

In a context of Data Integration Systems (DIS) providing access to large amounts of data extracted and integrated from autonomous data sources, users are highly concerned about data quality. Traditionally, data quality is characterized via multiple quality factors. Among the quality dimensions that have been proposed in the literature, this report analyzes two main ones: data freshness and data accuracy. Concretely, we analyze the various definitions of both quality dimensions, their underlying metrics and the features of DIS that impact their evaluation. We present a taxonomy of existing works proposed for dealing with both quality dimensions in several kinds of DIS and we discuss open research problems