The Internet evolved from a network with a few terminals to an intractable network of millions of nodes. Recent interest in information-centric networks (ICNs) is gaining significant momentum as a Future Internet paradigm. The key question is, hence, how to model the massive amount of connected nodes with their content requests in dynamic paradigm. In this paper, we present a novel method to characterize data requests based on content demand ellipse (CDE), focusing on efficient content access and distribution as opposed to mere communication between data consumers and publishers. We employ an approach of a promising eminence, where requests are characterized by type and popularity. Significant case studies are used to demonstrate that critical properties of ellipses may be used to characterize the content request irregularity during peak times. Depending on the degree of irregularity, the curve we plot becomes elliptic with a positive eccentricity less than one and an orientation centered with a bias. Real traffic data have been used to demonstrate how various data demand/request types affect eccentricity, orientation, and bias. Through simulations, we propose a dynamic resource allocation framework for Virtual Data Repeaters (VDRs) by correlating the resource allocation schema with the factors that affect the CDE in ICN.