Task Allocation in Industrial Edge Networks with Particle Swarm Optimization and Deep Reinforcement Learning

To avoid the disadvantages of a cloud-centric infrastructure, next-generation industrial scenarios focus on using distributed edge networks. Task allocation in distributed edge networks with regards to minimizing the energy consumption is NP-hard and requires considerable computational effort to obtain optimal results with conventional algorithms like Integer Linear Programming (ILP). We extend an existing ILP problem including an ILP heuristic for multi-workflow allocation and propose a Particle Swarm Optimization (PSO) and a Deep Reinforcement Learning (DRL) algorithm. PSO and DRL outperform the ILP heuristic with a median optimality gap of 7.7 % and 35.9 % against 100.4 %. DRL has the lowest upper bound for the optimality gap. It performs better than PSO for problem sizes of more than 25 tasks and PSO fails to find a feasible solution for more than 60 tasks. The execution time of DRL is significantly faster with a maximum of 1 s in comparison to PSO with a maximum of 361 s. In conclusion, our experiments indicate that PSO is more suitable for smaller and DRL for larger sized task allocation problems.