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Relocating the Load-Shedding Strategy in the Data Stream Processing Architecture

Data Stream Processing Strategy (DSPS) is a data stream engine oriented to measurement projects, which could incorporate Internet-of-Thing (IoT) devices as data collectors. Due to the IoT devices could be heterogeneous and even have its own format for data interchanging, the integration of such measures and its traceability increase the system complexity. For that reason, our strategy incorporates the role of the Measurement Adapter (MA), a software component that knowing the measurement project definition is able to match each IoT device to each metric. Previously, MA was responsible for data collecting from its associated sensors (i.e., IoT devices) and informing measures to the gathering function. Fog computing tends to carry out the data processing as close as possible on the fog devices. In this work, a proposal to relocate the load shedding techniques from the gathering function to the measurement adapter is outlined. This allows to avoid the overhead in the gathering function, which is responsible for the global data collecting, increasing the workload in the measurement adapter. Moreover, the synthesis algorithms are coordinated locally in the MA with the load shedding techniques before transmitting the measures’ streams, decreasing the use of the network, optimizing the use of local resources, and releasing resources in the data stream engine. An updated process view related to the MA’s role jointly with the processing architecture is shown. In addition, simulation results for analyzing the new MA’s behavior are presented.