Preference-Aware Fault-Tolerant Function Embedding in Energy-Harvesting Serverless Edge Computing

Serverless edge computing (SEC) that integrates serverless and edge computing paradigms has facilitated the deployment of intelligent Internet-of-things (IoT) applications. In SEC systems, energy efficiency and serverless pricing are essential to maintain operational sustainability. Nevertheless, most existing energy-saving techniques focus only on stable energy scenarios and are therefore inapplicable to energy-harvesting SEC systems powered by intermittent renewable sources. On the other hand, serverless pricing policies generally neglect the personalized perceptions of user quality-of-experience (QoE) preferences, thereby resulting in holistic user QoE degradation from a system perspective. Moreover, these approaches cannot guarantee functional correctness of serverless applications due to the appearance of computation and communication errors in practical SEC systems. To tackle these challenges, we investigate the preference-aware fault-tolerant function embedding problem for enhancing the holistic user QoE in energy-harvesting SEC systems. We first design a personalized QoE preference predictor to characterize trade-offs between service completion time and resultant service fees of individual users. Subsequently, we develop a reinforcement learning method to decide static function embedding decisions at the offline phase. Considering the intermittency of renewable sources, we further provide an energy-adaptive function replica freezing strategy at the online phase. Evaluations demonstrate that our approach boosts the holistic user QoE by 32.2% over state-of-the-art algorithms.