The Function-as-a-Service (FaaS) paradigm has emerged as an evolution of traditional cloud computing services, promising easier development and operations, finer-grained pricing and seamless scalability. With the proliferation of computational capacity at the network edge and across the cloud-to-edge continuum, FaaS adoption has expanded beyond the borders of traditional cloud data centers. However, in such dynamic, distributed and heterogeneous environments, additional challenges arise, including how to deal with load peaks through computational offloading and how to execute functions in a carbon and energy-aware manner. In this paper, we tackle these challenges by introducing an approach to carbon- and Quality of Service (QoS)-aware function offloading based on spatial workload shifting and adaptive selection of function variants (i.e., multiple implementations trading off computational demand, accuracy and energy consumption). Our solution targets a FaaS system spanning the cloud-to-edge continuum hosting users belonging to multiple service classes with diverse QoS requirements. By solving a linear programming problem at run time, our approach determines how to allocate the available resources to optimize the trade-off between carbon emissions and QoS satisfaction. Extensive simulated experiments show that, under the same monetary budget, our approach allows 30% more requests to meet QoS requirements on average compared to a state-of-the-art baseline, with 20% less carbon emissions due to function execution.
Calavaro et al. (Mon,) studied this question.