Task Orchestration in the Cloud Continuum Via Multi-objective Evolutionary Algorithms.

As IoT ecosystems expand, the need for efficient computational offloading becomes increasingly critical. Traditional cloud-based solutions often suffer from latency and bandwidth limitations, prompting the emergence of cloud-fog/edge-IoT architectures where processing can also occur closer to the data source (edge of the network). This flexibility, introduces new challenges in orchestrating task execution under diverse and dynamic conditions. In this work, we provide a new multi-objective optimization model for the transparent task orchestration problem across the cloud-continuum, aiming to minimize latency, energy consumption, and load imbalance. Our model incorporates task dependencies, task priorities, heterogeneous communication and execution models, making it well-suited for practical deployment. We evaluate state-of-the-art multi-objective evolutionary algorithms (MOEAs), including NSGA-II, NSGA-III, MOEA/D, and SPEA2. We also introduce NS-CSA, a new evolutionary approach inspired by Cuckoo Search and NSGA-II. Extensive experiments across diverse configurations demonstrate the advantages of NS-CSA and provide valuable insights into the strengths and limitations of different MOEAs for realistic IoT offloading scenarios.