Proactive Task Allocation in Extreme Edge Computing for Digital Twin Services

Extreme edge computing (EEC) exploits the untapped computational power of end devices, referred to as extreme edge devices (EEDs), and thus holds the potential to revolutionize the digital twin (DT) technology. However, traditional reactive task allocation approaches fail to address the complexities of DT processing tasks, where the execution of all the underlying subtasks is crucial. Additionally, these approaches suffer due to the intermittent availability of EEDs, which compromises the Quality of Service (QoS). In this article, we propose the proactive maximum weighted service capacity (P-MWSC) scheme. P-MWSC is the first scheme to employ a proactive approach, utilizing predictions of the dynamic resource usage and resource characterization of EEDs to tackle the intricacies of DT tasks while taming the effects of EEDs’ dynamicity and intermittent availability. We formulate the task allocation problem as a binary integer linear program (BILP) that aims to maximize the service capacity, weighted by the achieved gain from each fully assigned task. We derive an analytical solution using the Karush–Kuhn–Tucker (KKT) conditions and Lagrangian relaxation, and use a top-down decomposition approach to provide a solution that achieves up to 80% runtime reduction. Additionally, we propose a heuristic scheme with a bottom-up decomposition approach, that is, suitable for certain practical scenarios, yielding up to 90% runtime reduction. Extensive performance evaluations using data from a realistic testbed demonstrate that P-MWSC outperforms representatives of prominent reactive and proactive schemes, achieving up to 70% increase in the task success rate and a 39% reduction in the average response delay.