Services with distributed and interdependent components are becoming a popular option for harnessing dispersed resources available on cloud and edge networks. However, effective deployment and management of these services, namely service coordination, is a challenging task. Service coordination comprises the placement and scalability of components and scheduling incoming traffic requesting for services between deployed instances. Due to the online nature of the problem and the success of Deep Reinforcement Learning (DRL) methods, previous works considered DRL agents for solving service coordination problems, yet these solutions have to be retrained for every unseen scenario. Other works have tried to tackle this shortcoming by incorporating Graph Neural Networks (GNN) into their solutions, but they often focus on specific aspects (and disregard others) or cannot operate in dynamic and practical situations where there is no labeled dataset and feedback from the network might be delayed. In response to these challenges, we present GSC, a generalizable service coordinator that jointly considers service placement, scaling, and traffic scheduling. GSC can operate in unseen situations without significant performance degradation and outperforms existing state-of-the-art solutions by 40%, as determined by simulating real-world network situations.
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