Optimizing Resource Allocation in Edge-distributed Stream Processing.

Emerging Web applications based on distributed IoT sensor systems and machine intelligence, such as in smart city scenarios, have posed many challenges to network and processing infrastructures. For example, environment monitoring cameras generate massive data streams to event-based applications that require fast processing for immediate actions. Finding a missing person in public spaces is an example of these applications, since his/her location is a piece of perishable information. Recently, the integration of edge computing with machine intelligence has been explored as a promising strategy to interpret such massive data near the sensor and reduce the end-to-end latency of processing events. However, due to the limited capacity and heterogeneity of edge resources, the placement of task processing is not trivial, especially when applications have different quality of service (QoS) requirements. In this paper, we develop an algorithm to solve the optimization problem of allocating a set of nodes with sufficient processing capacity to execute a pipeline of tasks while minimizing the operational cost related to latency and energy and maximizing availability. We compare our algorithm with the resource allocation algorithms (first-fit, best-fit, and worst-fit), achieving a lower cost in scenarios with different nodes' heterogeneity. We also demonstrate that distributing processing across multiple edge nodes reduces latency and energy consumption and still improves availability compared to processing only in the cloud.