An Approach for Deployment of Service-Oriented Simulation Run-Time Resources

The requirements for low latency and high stability in large-scale geo-distributed training simulations have made cloud-edge collaborative simulation an emerging trend. However, there is currently limited research on how to deploy simulation run-time resources (SRR), including edge servers, simulation services, and simulation members. On one hand, the deployment schemes of these resources are coupled and have mutual impacts. It is difficult to ensure overall optimum by deploying these resources separately. On the other hand, the pursuit of low latency and high system stability is often challenging to achieve simultaneously because high stability implies low server load, while a small number of simulation services implies high response latency. We formulate this problem as a multi-objective optimization problem for the joint deployment of SRR, considering the complex combinatorial relationship between simulation services. Our objective is to minimize the system time cost and resource usage rate of edge servers under constraints such as server resource capacity and the relationship between edge servers and base stations. To address this problem, we propose a learnable genetic algorithm for SRR deployment (LGASRD) where the population can learn from elites and adaptively select evolution operators performing well. Extensive experiments with different settings based on real-world data sets demonstrate that LGASRD outperforms the baseline policies in terms of optimality, feasibility, and convergence rate, verifying the effectiveness and excellence of LGASRD when deploying SRR.

Automated summary

This study proposes a learnable genetic algorithm for the deployment of simulation run-time resources (SRR) in cloud-edge collaborative simulation. The experiments demonstrate that the proposed method outperforms baseline policies in terms of optimality, feasibility, and convergence rate.