Enhancing the Scalability and Efficiency of Distributed Machine Learning Frameworks in Heterogeneous Cloud Environments

Abstract

Distributed machine learning (DML) systems are instrumental to efficiently train large models at scale, especially at large data scales and leveraging smarter automation. However, traditional DML platforms work quite bad in heterogeneous cloud environments in that the computing resources on the cloud are of various structure, scale and speed. This paper explores possible approaches for scaling and optimizing distributed machine learning frameworks to be able to run on various infrastructures. To address this challenge, we present a strategic approach that combines adaptive resource scheduling, dynamic workload balancing, and topology-aware communication does it to improve the performance of DML operations in multi-cloud and hybrid deployments. The architecture enables fine-grained management of compute, memory and data movement thanks to smart orchestration layers and containerized infrastructures including Kubernetes and Docker. Mechanisms are included at the system level, such as hardwareconscious algorithms, fault tolerant checkpointing, and asynchronous gradient updates to reduce latency and improve resource utilization. We further benchmark different DML frameworks, such as parameter server model and AllReduce method, in diverse complex environments, including the strong heterogeneous ones. Our experimental results demonstrate that: (1) infrastructure-aware scheduling susceptibility and adaptive parallelism can reduce time to train by up to 45%—without compromising model accuracy or system reliability. Finally, overall this work represents a strong foundation for enhancing distributed machine learning across heterogeneous clouds and offers key takeaways for those who are looking to scale AI solutions in a cost-effective manner. It also shines a light on infrastructure heterogeneity as both a barrier and a positive opportunity in the future of cloud-native machine learning.