Abstract: Embodiments of the disclosure can include MapReduce systems and methods with integral mapper and reducer compute runtime environments. An example system with an integral reducer compute runtime environment can include mappers and reducers executable on a computer cluster. The mappers can be operable to receive raw input data and generate first input data based on the raw input data. The mappers can be operable to generate first result data based on the first input data. Based on the first result data, the mappers can be operable to generate (K, V) pairs. The reducers can be operable to receive the (K, V) pairs and generate second input data based on the (K, V) pairs. The reducers can be operable to transmit the second input data to integral compute runtime environment being run within the reducers and operable to generate second result data based on the second input data. Based on the second result data, the reducers can be operable to generate output data.

Abstract: A monitoring and diagnostics system for a fleet of rental power generation equipment utilizes a plurality of remote processors each operatively engaged with a respective power generation unit. The remote processors each include a plurality of sensors detecting operating data of the respective power generation unit. A managing processor receives the operating data from the plurality of remote processors and processes the operating data via an algorithm to determine a health status of the rental power generation fleet. In one arrangement, the fleet health status is configurable for presentation via the managing processor on a single display. In another arrangement, the managing processor processes the operating data via a predictive failure algorithm to determine the health status of the power generation equipment along with a failure prediction based on the received operating data.