DyRAM: Dynamic Data Allocation and Resource Management in Distributed Machine Learning Systems

The rapid evolution of digital technologies and the pervasive nature of data connectivity have significantly expanded the scope of decentralized machine learning tasks. At the forefront of this shift is distributed machine learning, which leverages distributed data while promoting privacy and efficiency. Built on the principles of cloud computing, distributed machine learning decomposes complex computational tasks into smaller components processed concurrently across interconnected nodes, optimizing resource utilization and scalability. The global cloud computing market, integral to the advancement of distributed machine learning, is projected to grow substantially, reaching USD 2,495.2 billion by 2032. Central to this study is the Cloud-Based Ratio Proportion Data Distribution Algorithm (CBRPDDA), an innovative solution to traditional data distribution inefficiencies. CB-RPDDA reallocates data based on the processing speeds of individual machines, ensuring optimal resource utilization and effective workload distribution. This method introduces a new perspective on dataset division among worker nodes, enhancing load balancing and performance. By integrating CB-RPDDA with distributed machine learning frameworks, we improve the efficiency of decentralized learning processes, ensuring efficient data distribution across nodes while maintaining data security and privacy. Our findings demonstrate the potential of combining CB-RPDDA with distributed machine learning to offer scalable, efficient, and secure machine learning solutions, driving significant advancements in the field.