Intent-based Decentralized Orchestration for Green Energy-aware Provisioning of Fog-native Workflows

The cloud native paradigm is emerging as a pathway to developing applications for intrinsic operation on the cloud. This prompted application modularity, leveraging the adoption of the microservices architecture. Meanwhile, fog computing is emerging as a geo-dispersed cloud, bringing services closer to the end-user for localization and improved responsiveness. Transitioning to fog-native applications, i.e. managing microservice workflows over the fog, is a non-trivial challenge. On one hand, engineering workflows require awareness of the dependencies across microservices, as they impact the perceived quality of service. On the other hand, the heterogeneity of capacities, energy prices and supply, introduce challenges that can negate the sought advantages of the fog. This work proposes a novel algorithm based on Alternating Direction Method of Multipliers for intent-based workflow mapping and admission, iADMM. The performance of the algorithm is evaluated analytically and experimentally and compared to a baseline compute-network cost minimization alternative. Evaluation results show that iADMM achieves near optimal decisions in minimizing operational costs without violating workflow intents.