The rapid digitalization across industries comes with many challenges. One key
problem is how the ever-growing and volatile data generated at distributed
locations can be efficiently processed to inform decision making and improve
products. Unfortunately, wide-area network capacity cannot cope with the growth
of the data at the network edges. Thus, it is imperative to decide which data
should be processed in-situ at the edge and which should be transferred and
analyzed in data centers.
In this paper, we study two families of proactive online data replication strategies, namely ski-rental and machine learning al-gorithms, to decide which data is processed at the edge, close to where it is generated, and which is transferred to a data center. Our analysis using real query traces from a Global 2000 company shows that such online replication strategies can significantly reduce data transfer volume (in many cases up to 50% compared to naive approaches) and achieve close to optimal performance. After analyzing their shortcomings for ease of use and performance, we propose a hybrid strategy that combines the advantages of both competitive and machine learning algorithms.