Multi-Flow Complex Event Optimization in the Edge: A Smart Street Scenario

Internet of Things (IoT) devices can be used to provide safety, security, and other services that ensure that smart systems work as intended. However, the increasing complexity of the tasks is increasing the potential of performance loss when limited resources are not utilized appropriately. Distributed complex event processing (CEP) applications can be used to execute multiple unique tasks on sensor data. Since these operations can require a variety of data from multiple sensors across separate task steps, non-optimal code and data management can lead to increased processing delays. In this study, a mathematical model for optimizing critical path performance across multiple independent CEP flows is proposed. The model optimally assigns both where codes are executed at, as well as where their respective data should be placed at. The proposed solution is implemented within an open source library with the inclusion of operator placement heuristics from the literature. Approaches are tested within a realistic smart-street scenario. Consumer delays, algorithm runtimes, and delivery ratios within different time windows are reported. The results indicate that the proposed approach can reduce the delivery times for the critical CEP paths better than the heuristic solutions, with the downside of increased optimization runtimes.