An RL-Based Cost-Effective Two-Layer Scaling Strategy for Multiregional Heterogeneous and Time-Varying Cloud Instances

Fueled by the advancements in 5G networks, Internet of Things (IoT) technologies are undergoing rapid development. Utilizing the computational power and elasticity of cloud computing, deploying IoT applications as Software-as-a-Service (SaaS) can reduce costs while enhancing scalability. However, in recent years, large-scale cloud downtime caused by natural disasters and network attacks, which seriously affects SaaS applications, has been widely observed. Deploying SaaS applications across geo-dispersed instances in a distributed way can effectively avoid service termination caused by cloud downtime. Generally, SaaS applications face dynamic traffic, which can lead to variations in service qualities and potentially fail to meet user demands. Scaling cloud instances according to the real traffic could save costs for service providers while ensuring service qualities. Considering the lack of effectiveness of reactive-threshold-based scaling strategies, many works suggest proactive scaling strategies based on reinforcement learning (RL). However, most RL-based scaling strategies are cursed by dimensionality when facing dynamic traffic. Moreover, heterogeneous and time-varying multiregional instances often lead to a complex state space which intensifies the curse of dimensionality. To address the above issues, in this article, we propose an RL-based two-layer scaling strategy that addresses the challenge of cloud resource scaling in complex state spaces through hierarchical decision-making. We formulate the scaling of multiregional heterogeneous instances as a bi-objective optimization problem and discuss the Markov property of time-varying variables, including time-varying instances and dynamic traffic. Experimental results based on two real-world workload datasets show that our two-layer scaling strategy achieves more fine-grained, cost-effective scaling while maintaining service quality, compared to the baselines.