[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121550-en":3,"doc-seo-121550-105":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},121550,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Towards Workload-aware Efficient Machine Learning Systems","Machine learning (ML) requires computing platforms that can efficiently support large-scale ML workloads as models grow in size, complexity, and dynamism. Existing systems often struggle to adapt, reducing performance and limiting flexibility, while current approaches lack the mechanisms to integrate ML-driven improvements into traditional infrastructure. This dissertation designs and develops storage and scheduling solutions that exploit workload characteristics and system infrastructure, improving application performance and maximizing resource utilization.","Towards Workload-aware Efficient Machine Learning Systems  \nRedwan Ibne Seraj Khan  \nDissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nComputer Science and Applications  \nAli R. Butt, Chair  \nKirk W. Cameron  \nBo Ji  \nXun Jian  \nYue Cheng  \nFebruary 14, 2025  \nBlacksburg, Virginia  \nKeywords: Machine Learning, Deep Learning, Federated Learning, High Performance Computing, Cloud Computing, Storage Systems, Data Storage, Data Management, Machine Learning Systems, Job Scheduling, Resource Management, MLSys, SysML,  \nEfficiency, Flexibility  \nCopyright 2025, Redwan Ibne Seraj Khan  \nTowards Workload-aware Efficient Machine Learning Systems  \nRedwan Ibne Seraj Khan  \nABSTRACT  \nMachine learning (ML) is transforming various aspects of our lives, driving the need for computing systems that efficiently support large-scale ML workloads. As models grow in size and complexity, existing systems struggle to adapt, limiting both performance and flexibility. Additionally, ML techniques can enhance traditional computing tasks, but current systems lack the adaptability to integrate these advancements effectively.  \nBuilding systems for running machine learning workloads, and running workloads using machine learning-both require a careful understanding of the nature of the systems and ML models. In this dissertation we design and develop a series of novel storage and scheduling solutions for ML systems by bringing attention to the unique characteristics of workloads and the underlying system. We find that by designing ML systems that are finely tuned to workload characteristics and underlying infrastructure, we can significantly enhance application performance and maximize resource utilization.  \nIn the first part of this dissertation (Ch-3), we analyze popular ML models and datasets, uncovering insights that inspired SHADE, a data-importance-aware caching solution for ML. The second part of this dissertation (Ch-4) proposes to leverage system characteristics of hundreds of client devices along with the characteristics of the samples within the clients to design novel sampling, caching and client scheduling mechanisms to tackle the data and system heterogeneity among client devices and thereby fundamentally improve the performance of federated learning using edge devices in the cloud. The third part of this dissertation (Ch-5) proposes to leverage multi-agent LLM application and user request characteristics to design an efficient request scheduling mechanism that can serve clients in multi-tenant environments in a fair and efficient manner while preventing abuse.  \nMy dissertation demonstrates that leveraging workload-aware strategies can significantly enhance the efficiency (e.g., reduced training time, increased throughput, lower latency) and flexibility (e.g., improved ease of use, deployment, and programmability) of machine learning systems. By accounting for workload dynamicity and heterogeneity, these principles can guide the design of next-generation ML systems, ensuring adaptability to emerging models and evolving hardware technologies.  \nTowards Workload-aware Efficient Machine Learning Systems  \nRedwan Ibne Seraj Khan  \nGeneral Audience Abstract  \nMachine learning (ML) has become an integral part of our daily lives, powering applications from virtual assistants to medical diagnostics. As ML models grow larger and more complex, the systems that run them must evolve to keep pace. This dissertation exploreshow we can build more efficient and adaptable computing systems to support large-scale ML workloads.  \nTraditional computing systems often struggle to accommodate the ever-changing demands of ML applications. Similarly, ML techniques can be leveraged to improve the performance of non-ML workloads, but existing systems lack the flexibility to integrate these advancements seamlessly. This research tack","cbCaijNBsIc3Jo5v","https://ap.wps.com/l/cbCaijNBsIc3Jo5v","pdf",2236000,1,127,"English","en",105,"# Abstract\n## Storage and scheduling for ML systems\n## Data-importance-aware caching (SHADE)\n## Federated learning with edge devices\n## Multi-tenant fair request scheduling with LLMs\n## Efficiency and flexibility outcomes","[{\"question\":\"What problem does the dissertation address in ML computing systems?\",\"answer\":\"It addresses how ML workloads grow more complex and dynamic, causing existing systems to underperform and lack flexibility, while traditional infrastructure cannot fully integrate ML-driven advances.\"},{\"question\":\"What types of solutions are proposed in the dissertation?\",\"answer\":\"The research designs and develops storage and scheduling solutions tailored to ML workloads and underlying system infrastructure.\"},{\"question\":\"How does the dissertation improve federated learning performance?\",\"answer\":\"It proposes sampling, caching, and client scheduling mechanisms that account for data and system heterogeneity across client devices to improve edge-device federated learning in cloud settings.\"}]","Towards Workload-aware Efficient Machine Learning Systems | 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problem does the dissertation address in ML computing systems?","Question",{"text":75,"@type":76},"It addresses how ML workloads grow more complex and dynamic, causing existing systems to underperform and lack flexibility, while traditional infrastructure cannot fully integrate ML-driven advances.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What types of solutions are proposed in the dissertation?",{"text":80,"@type":76},"The research designs and develops storage and scheduling solutions tailored to ML workloads and underlying system infrastructure.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the dissertation improve federated learning performance?",{"text":84,"@type":76},"It proposes sampling, caching, and client scheduling mechanisms that account for data and system heterogeneity across client devices to improve edge-device federated learning in cloud 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