Graph analytics powers modern intelligent systems such as smart cities, cyber-physical infrastructure, IoT security, and large-scale social networks. As these workloads scale in complexity, their execution in heterogeneous edge-cloud environments results in higher energy use and carbon emission footprint. To address this challenge, we propose MERSEM, a multi-objective evolutionary reinforcement learning framework for sustainable edge-cloud system management. MERSEM integrates evolutionary search with reinforcement learning (RL) to solve the problem of graph workload allocation and scheduling. The evolutionary component explores diverse global solutions, while the RL agent refines decisions through adaptive local optimization. The framework is designed to jointly minimize service-level agreement (SLA) violations and carbon emissions by considering dynamic carbon intensity, resource heterogeneity, and workload characteristics. Experimental results demonstrate that MERSEM outperforms the state-of-the-art with up to 45% SLA violation reductions and up to 12% carbon emission reductions.

翻译：图分析支撑着现代智能系统，例如智慧城市、信息物理基础设施、物联网安全以及大规模社交网络。随着这些工作负载复杂性的增长，它们在异构边缘-云环境中的执行导致了更高的能源使用和碳排放足迹。为了应对这一挑战，我们提出了MERSEM，一个用于可持续边缘-云系统管理的多目标进化强化学习框架。MERSEM将进化搜索与强化学习（RL）相结合，以解决图工作负载的分配与调度问题。进化组件负责探索多样化的全局解，而RL智能体则通过自适应局部优化来精炼决策。该框架旨在通过考虑动态碳强度、资源异构性以及工作负载特性，联合最小化服务等级协议（SLA）违规和碳排放。实验结果表明，MERSEM的性能超越了现有最先进的方法，实现了高达45%的SLA违规率降低以及高达12%的碳排放减少。