Stargazer: A Deep Learning Approach for Estimating the Performance of Edge- Based Clustering Applications

Cruz, Breno Dantas; Paul, Arnab; Zheng, Shuang; Tilevich, Eli

doi:10.1109/smds49396.2020.00009

Cited by 3 publications

(1 citation statement)

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“…This initial effort was not aimed at edgebased community detection. Several works have been carried out to evaluate the performance of clustering algorithms for social network [178] and edge community detection [179]. The concept of community detection in edge networks is illustrated in Figure 15.…”

Section: Community Detectionmentioning

confidence: 99%

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Shuja,

Bilal,

Alasmary

et al. 2020

Preprint

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Edge networks provide access to a group of proximate users who may have similar content interests. Caching popular content at the edge networks leads to lower latencies while reducing the load on backhaul and core networks with the emergence of high-speed 5G networks. Edge networks are complex and dynamic. User mobility, preferences, and content popularity are the dominant dynamic features of the edge networks. Temporal and social features of content, such as the number of views and likes are applied to estimate the popularity of content from a global perspective. However, such estimates may not be mapped to an edge network with particular social and geographic characteristics. In edge networks, machine learning techniques can be applied to predict content popularity based on user preferences, user mobility based on user location history, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network to lower latencies and increase cache hits. This article surveys the application of machine learning techniques for caching content in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (application and objective), (b) caching strategy (policy, location, and objective), and edge network (type and delivery strategy). We further survey supporting concepts for optimal edge caching decisions that require the application of machine learning. These supporting concepts are social-awareness, popularity prediction, and community detection in edge networks. A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning towards caching in edge networks.

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Section: Community Detectionmentioning

confidence: 99%