From cloud to edge: dynamic placement optimization of business processes in IIoT networks

Abstract

Breakthroughs in edge computing offer new prospects for businesses to extend Industrial Internet of Things (IIoT) networks beyond analytics to actionable processing. In particular, cloud-based business processes, which provide administrative actions and rules through workflow-sequenced activities, can be streamlined on the edge for low-latency access in physical spaces. Although this advances business controls, particularly for critical events of industrial applications, it faces operational barriers. Edge devices, which support high volume and competing demands from a large number of sensors, vary in capacity, reliability, and proximity to sensors and cloud gateways. This warrants a highly efficient placement of process activities, from cloud to edge, given a variety of constraints, including resource demand, capacity, and compatibility, to satisfy timeliness constraints. In contrast to the related IIoT optimization research underway, including those of singleton service placements, business processes pose new challenges. Not only do sets of dependent activities have to be considered for co-deployment, but the meaning of timing constraints needs to be respected, given alternative, parallel, and iterative control-flow paths in processes. In addition, instantiation (replication) to scale activities for increasing data volumes poses further deployment constraints, i.e., on sets of nodes supporting dynamic instantiation of order-dependent activities. Here we present an optimization strategy for business processes that addresses these challenges. We first conceptualize processes in coherent fragments to precisely derive both responsiveness and throughput execution time heuristics and formulate a multi-objective process placement problem. Next, we develop a genetic algorithm-based process placement procedure. To adapt to fluctuating event frequencies, we support an interplay between scaling algorithms for service instances and process placement optimization. Validation through an industrial safety monitoring use case drawn from the construction industry shows that our approach improves timeliness responses by almost one-third and more than doubles execution throughput compared to existing methods.