Internet of Things (IoT) domains generate large volumes of high velocity event streams from sensors, which need to be analyzed with low latency to drive intelligent decisions. Big Data platforms for Complex Event Processing (CEP) enable such analytics. Traditionally, limited analytics are performed on the gateway edge device, or comprehensive analytics performed on Cloud Virtual Machines (VM) across all sensor streams. Leveraging the growing prevalence of captive edge resources in combination with Cloud VMs can offer better performance, flexibility and monetary costs. Here, we propose an approach to schedule an event analytics application composed as a Directed Acyclic Graph (DAG) of CEP queries across a collection of edge and Cloud resources. The goal of this optimization problem is to map the queries to the resources such that the end-to-end latency for the DAG is minimized, while also ensuring that a resource's compute and energy capacity are not saturated. We propose a brute-force optimal algorithm (BF) and a Generic Algorithm (GA) meta-heuristic to solve this problem. We perform comprehensive real-world benchmarks on the compute, network and energy capacity of edge and Cloud resources for over 17 CEP query configurations. These results are used to define a realistic simulation study that validate the BF and solutions for a wide variety over 45 DAGs. Our results show that the GA approach comes within 99% of the optimal BF solution, maps DAGs with 4 - 50 queries within 0.001 - 25 secs (unlike BF which takes hours for > 12 queries), and in fewer than 10% of the experiments is unable to offer a feasible solution.
['Rajrup Ghosh', 'Yogesh Simmhan']
Distributed Scheduling of Event Analytics across Edge and Cloud