To do this, RECON combines coarse-grained resource utilization and component-level Web page load information available from existing tools. During the initial training stage, RECON uses a power monitor to measure the energy consumption during a set of page load processes and juxtaposes this power consumption with coarse-grained resource and component information. RECON uses both simple linear regression and more complex neural networks to build a model of the power consumption as a function of the resources used and the individual page load components, thus providing benefits over individual models. Using the model, RECON can estimate the energy consumption of any Web page loaded as-is or upon applying any enhancement, without the monitor.
We experimentally evaluate RECON on the Samsung Galaxy S4, S5, and Nexus devices using 80 Web pages. Comparisons with actual power measurements from a fine-grained power meter show that, using the linear regression model, RECON can estimate the energy consumption of the entire page load with a mean error of 6.3% and that of individual page load activity segments with a mean error of 16.4%. When trained as a neural network, RECON's mean error for page energy estimation reduces to 5.4% and the mean segment error is 16.5%. We show that RECON can accurately estimate the energy consumption of a Web page under different network conditions, such as lower bandwidth or higher RTT, even when the model is trained under a default network condition. RECON also accurately estimates the energy consumption of a Web page after applying popular Web enhancements including ad blocking, inlining, compression, and caching.