OPTIMIZING DISTRIBUTED MACHINE LEARNING WORKLOADS AND RESOURCE MANAGEMENT IN MULTI-CLOUD ENVIRONMENTS UNDER VARIOUS WORKLOAD CONDITIONS

Distributed machine learning leverages multiple computing resources to process large datasets efficiently.However, running workloads across multi-cloud environments presents challenges like varying resource availability, high communication latency, and cost inefficiencies.This study addresses the problem of optimizing workload distribution and resource management in heterogeneous multi-cloud setups.The objective is to develop a dynamic framework that improves execution speed, reduces communication overhead, and lowers operational costs, ensuring scalable and efficient distributed machine learning across diverse cloud platforms.This study models a heterogeneous multi-cloud environment using AWS, Google Cloud, and Azure, leveraging containerization and Kubernetes for scalable deployment.Large datasets are efficiently partitioned and distributed to minimize inter-cloud communication and enhance fault tolerance.An Adaptive Scheduling using Reinforcement Learning Optimization (ASRLO) dynamically balances workloads based on real-time patterns.Compressed Routing and Messaging Architecture (CRMA) reduces communication overhead, while Real-time Elastic Monitoring with Predictive Adjustment (REMPA) ensures proactive resource management.Together, these techniques optimize distributed machine learning workloads for improved performance, scalability, and cost-efficiency in multi-cloud settings.Findings show that the optimized multi-cloud framework significantly enhances distributed ML workloads by reducing operational costs from 50 to 30 hours, lowering data transfer from 200 GB/hr to 120 GB/hr, and increasing availability to 99.5%.OBSMO achieves the lowest VM cost (18-79) and energy consumption (1.5-7 KJ).The proposed method offers stable convergence (0.59-0.528) and scalability, implemented in Python Software.Future scope includes refining accuracy, reducing inter-cloud latency, and balancing energy efficiency with performance in real-time, high-variance multi-cloud environments.