Service Management in the Edge Cloud for Stream Processing of IoT Data

We consider an event-driven IoT data stream processing (DSP) model in the Edge Cloud. Periodical DSP workflows are issued dynamically to the edge. Instead of traditional deployment approach, we use a service-oriented deployment model in which the same DSP components in different workflows will be deployed as long running services. This can greatly reduce the overhead in transferring and starting DSP components. Accordingly, we develop a new edge resource allocation problem. Resources are allocated to long running services according to the statistical data flow rates to the services. Subsequently, a Robinhood greedy algorithm (RG) is developed to derive the service allocation solution. Experimental studies show that the RG algorithms can achieve allocations with significantly reduced communication cost and more balanced load compared to a baseline algorithm.