This dataset comprises a collection of synthetic application‐placement instance sets for heterogeneous cloud–edge/fog infrastructures, designed for the evaluation of single‐ and multi‐objective optimization strategies. Each instance describes: - a directed acyclic graph (DAG) of interdependent services forming an application, - a set of compute nodes (cloud, edge, fog) with resource capacities and connectivity latencies, - resource demands of each service (e.g., CPU, memory), service‐to‐service dependency weights or communication cost, - one or more placement solutions together with objective values (such as latency, energy consumption, deployment cost) generated by algorithms including the DRL model, a genetic algorithm (GA) and an NSGA-II multi‐objective heuristic.

The dataset is split into training and test sets and is generated via the provided instance_generator.py and generate_dataset.py scripts. It allows researchers to benchmark and compare placement algorithms in terms of Pareto-front coverage, convergence speed, and trade-offs between objectives.

Potential uses: Investigating learning‐based or heuristic algorithms for application placement, multi‐objective optimisation in the cloud/fog continuum, dependency‐aware placement of microservices, as well as enabling reproducibility and comparison across approaches.