Note that the Runtime Adaptation Engine (RAE) sits at the core of our framework, as shown in Fig. 3.1. The RAE maintains an inventory of available units and cloud to utilize Machine/Cloud Discovery, and displays the state of their out there resources. The RAE employs a Logistic Regression [11] algorithm to learn the most value-effective coverage for distributing duties among devices and cloud, given the useful resource state. Right here, the definition of the fee function is the weighted sum of the useful resource state (such as battery life), community, and CPU utilization. The coverage obtained by working the Logistic Regression is enforced by the System/Cloud Selection module that chooses the most economical compute sources, based on the expected value-value for a given process.

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