[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118575-en":3,"doc-seo-118575-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118575,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","DyRAM: Dynamic Data Allocation and Resource Management in Distributed Machine Learning Systems","Digital technologies and always-on data connectivity have broadened decentralized machine learning, where distributed machine learning uses distributed data while targeting privacy and efficiency. Based on cloud computing principles, it decomposes large tasks into concurrent components across interconnected nodes to improve resource utilization and scalability. The work introduces the Cloud-Based Ratio Proportion Data Distribution Algorithm (CBRPDDA) to address data distribution inefficiencies by reallocating data according to each machine’s processing speed, supporting load balancing and secure workload distribution.","Montclair State University  \nMontclair State University Digital Commons  \n\n| School of Computing Faculty Scholarship and Creative Works | School of Computing |\n| --- | --- |\n| 1-1-2024\u003Cbr>DyRAM: Dynamic Data Allocation and Resource Management in Distributed Machine Learning Systems\u003Cbr>Vaibhavi Tiwari\u003Cbr>Montclair State University\u003Cbr>Rahul Thakkar\u003Cbr>Montclair State University, [thakkarr1@montclair.edu](thakkarr1@montclair.edu)\u003Cbr>Jiayin Wang\u003Cbr>Montclair State University, [wangji@montclair.edu](wangji@montclair.edu)\u003Cbr>Follow this and additional works at: [https://digitalcommons.montclair.edu/computing-facpubs](https://digitalcommons.montclair.edu/computing-facpubs) |  |\n\nMontclair State University Digital Commons Citation  \nTiwari, Vaibhavi; Thakkar, Rahul; and Wang, Jiayin, \"DyRAM: Dynamic Data Allocation and Resource Management in Distributed Machine Learning Systems\" (2024) . School of Computing Faculty Scholarship and Creative Works. 67.  \n[https://digitalcommons.montclair.edu/computing-facpubs/67](https://digitalcommons.montclair.edu/computing-facpubs/67)  \nThis Conference Proceeding is brought to you for free and open access by the School of Computing at Montclair State University Digital Commons. It has been accepted for inclusion in School of Computing Faculty Scholarship and Creative Works by an authorized administrator of Montclair State University Digital Commons. For more information, please contact [digitalcommons@montclair.edu](digitalcommons@montclair.edu).  \nDyRAM: Dynamic Data Allocation and Resource Management in Distributed Machine Learning  \nSystems  \nVaibhavi Tiwari School of Computing Montclair State University New Jersey, USA [tiwariv1@montclair.edu](tiwariv1@montclair.edu)  \nRahul Thakkar School of Computing Montclair State University New Jersey, USA [thakkarr1@montclair.edu](thakkarr1@montclair.edu)  \nJiaying Wang School of Computing Montclair State University New Jersey, USA [wangji@montclair.edu](wangji@montclair.edu)  \nAbstract—The rapid evolution of digital technologies and the pervasive nature of data connectivity have significantly expanded the scope of decentralized machine learning tasks. Atthe 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.  \nIndex Terms—Distributed Machine Learning, Resource Management, Data Distribution  \nI. INTRODUCTION  \nThe landscape of distributed machine learning tasks has been greatly expanded due to the rapid advancement of digital technologies and the increasing prevalence of data connectivity[1] . Effective resource management plays a crucial role in the success of distributed machine learning systems[2] . These sys","cbCaiedz5GRlqTNc","https://ap.wps.com/l/cbCaiedz5GRlqTNc","pdf",411761,1,9,"English","en",105,"# Introduction\n## Distributed machine learning background and scalability\n## Role of resource management and cloud-based architecture\n## Market growth context for cloud computing","[{\"question\":\"What problem does DyRAM address in distributed machine learning?\",\"answer\":\"DyRAM targets inefficiencies in traditional data distribution for distributed machine learning systems and emphasizes improved resource utilization and workload balance.\"},{\"question\":\"How does CBRPDDA improve data allocation across worker nodes?\",\"answer\":\"CBRPDDA reallocates data based on individual machine processing speeds, creating a dataset division strategy that improves load balancing and performance.\"},{\"question\":\"Why is resource management important for distributed machine learning systems?\",\"answer\":\"Effective resource management directly affects system success by enabling scalable and reliable execution of concurrent computation across interconnected nodes.\"}]","DyRAM: Dynamic Data Allocation and Resource Management in Distributed Machine Learning Systems | 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