Adaptive AI Algorithms for Scaling Serverless Architectures in Multi-Cloud Deployments
Keywords:
adaptive AI, serverless architecture, multi-cloud, scalabilityAbstract
As cloud computing continues to evolve, serverless architectures have gained prominence due to their ability to scale applications dynamically without requiring developers to manage the underlying infrastructure. However, in multi-cloud environments, scaling serverless applications becomes increasingly complex due to factors like workload distribution, inter-cloud communication, and resource availability. This paper explores the potential of adaptive artificial intelligence (AI) algorithms to optimize and scale serverless architectures in multi-cloud deployments. Specifically, we examine how machine learning, reinforcement learning, and deep learning models can be leveraged to improve resource management, reduce latency, and optimize performance across diverse cloud platforms. The paper highlights key challenges such as cloud heterogeneity, security concerns, and the need for real-time decision-making. Furthermore, it proposes a framework for integrating adaptive AI algorithms to enhance the scalability and efficiency of serverless systems in multi-cloud environments.
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