Policy-Based Task Allocation at Runtime for a Self-Adaptive Edge Computing Infrastructure

Autonomous and distributed Industrial Internet of Things (IIoT) systems are increasingly developed and deployed. They have an enormous demand for resilience and availability. At the same time, they are in a constantly changing system environment. The underlying edge computing infrastructure is characterized by ever increasing processing power and connectivity as well as a high degree of decentralization. To reduce downtime and long redesign loops, self-adaptation capabilities are needed. Automatic reallocation of the executed tasks to the compute nodes is a possible self-adaptation measure. However, the reallocation should be compliant with the different demands, constraints and specifications of the design. At the same time, a major challenge is that the allocation decision should be fast enough to be calculated at runtime. This paper therefore proposes an allocation method that uses demands in the form of policies to compute automatic reallocation at runtime. The integration of the allocation method into runtime is enabled by combining constraint programming, step-wise multi-criteria solution approaches, and resource management at multiple levels. The policy-based allocation method is tested and evaluated in the context of a smart factory site for the function offloading of automated guided vehicles (AGVs) and driverless micromobiles. Our results show that the allocation method is capable of recalculating the allocation during runtime in milliseconds while maintaining design conformity. This enables the system to react to changes in the environment, thereby reducing the downtime of decentralized Industrial Internet of Things systems and increasing availability.