[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122724-en":3,"doc-seo-122724-105":30,"detail-sidebar-cat-0-en-105":95},{"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},122724,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Single Parameter Driven Tunable Resource Allocation for Machine Learning Tasks","Machine learning evaluation and inference workloads are scheduled by splitting tasks into multiple server queues, where different categories (e.g., production vs research) may require different priorities. Conventional scheduling approaches such as weighted round-robin or batch strategies are approximate, non-deterministic, and lack real-time tunability to reflect current resource availability. This disclosure introduces a single optimization parameter λ, computed from real-time resource conditions, to form exponential-distribution quantiles. Updating task queues using λ and quantiles yields a tunable, priority-aware mechanism for task scheduling and execution.","Technical Disclosure Commons  \nDefensive Publications Series  \nApril 2023  \nSingle Parameter Driven Tunable Resource Allocation for Machine Learning Tasks  \nPeter Danenberg  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nDanenberg, Peter, \"Single Parameter Driven Tunable Resource Allocation for Machine Learning Tasks\", Technical Disclosure Commons,(April 19, 2023)  \n[https://www.tdcommons.org/dpubs_series/5812](https://www.tdcommons.org/dpubs_series/5812)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \nSingle Parameter Driven Tunable Resource Allocation for Machine Learning Tasks  \nABSTRACT  \nMachine learning tasks, such as natural language processing evaluations, are assigned to server resources by dividing the tasks into a number of queues. In such an environment, different tasks may have different priorities, e.g., production tasks may be higher priority than research  \ntasks. Task scheduling mechanisms such as weighted round-robin or batch scheduling are  \napproximate, non-deterministic, and cannot be tuned based on the real time availability of  \nresources. This disclosure describes the use of a single resource optimization parameter,  \ndetermined based on real time resource availability to establish quantiles based on the  \nexponential distribution for the single parameter. Task queues are updated based on the single  \nparameter and the established quantiles. By updating the single parameter based on real-time  \navailability of resources and workloads, the described techniques provide a tunable mechanism  \nfor task scheduling.  \nKEYWORDS  \n● Task queue  \n● Task scheduling  \n● Task priority  \n● Machine learning evaluations  \n● Production model  \n● Queue automation  \n● Exponential distribution  \nPublished by Technical Disclosure Commons, 2023 2  \nBACKGROUND  \nMachine learning models, such as natural language models, are commonly deployed on  \nserver infrastructure. A given server infrastructure may be utilized to train machine learning  \nmodels as well as to deploy trained models for production workloads. In such an environment,  \nevaluation tasks for different categories for models, e.g., production models, research models,  \nad-hoc models, etc. may need to be prioritized differently. Resources such as training queues,  \nevaluation queues, CPUs, machine learning accelerators, etc. are scarce. A simple but effective  \nprioritization mechanism can ensure appropriate resource allocation to the different workloads.  \nWeighted round-robin [1] can be utilized to assign tasks by dividing the tasks into a number of queues and assigning weights to the queues, e.g., in proportion to processing power requirements. However, there are no mechanisms available to automatically recalculate weights for the queues in real time as resource availability changes. Also, this mechanism does not  \nenable allocating higher amounts of resources for higher priority workloads. While batch  \nstrategies can be used for task scheduling, these are approximate, non-deterministic, and cannot  \nbe tuned based on the real time availability of resources.  \nDESCRIPTION  \nThis disclosure describes automated techniques to prioritize higher-priority evaluation  \nworkloads. The techniques utilize an exponential bias that is tuned by a single parameter λ .  \n[https://www.tdcommons.org/dpubs_series/5812](https://www.tdcommons.org/dpubs_series/5812) 3  \nFig. 1: Automatically prioritize and run model evaluations on tasks in queue  \nFig. 1 illustrates an example method to automatically prioritize and run model evaluations on tasks that are in a queue. The task queue for natural language processing models (or other machine lear","cbCailr3FXAAdGvr","https://ap.wps.com/l/cbCailr3FXAAdGvr","pdf",289926,1,7,"English","en",105,"# Abstract\n# Background\n## Prioritization and scheduling limitations\n# Description\n## Exponential bias and parameter λ\n## Quantile-based queue generation\n## Real-time reprioritization example","[{\"question\":\"How does the method schedule machine learning tasks across server resources?\",\"answer\":\"Tasks are divided into queues, then a single optimization parameter λ is determined from real-time resource availability. That λ is used to establish exponential-distribution quantiles to generate an updated prioritized queue.\"},{\"question\":\"Why are existing scheduling mechanisms insufficient for the stated goal?\",\"answer\":\"Weighted round-robin and batch strategies are approximate and non-deterministic, and they cannot be tuned based on real-time resource availability, nor do they provide a mechanism that automatically recalculates weights as conditions change.\"},{\"question\":\"What does the single parameter λ control in the described techniques?\",\"answer\":\"λ controls the strength of exponential bias toward higher-priority work, ranging from an equitable queue at λ=1 to increasingly strong bias at larger values, with high-priority-only behavior at sufficiently high λ (e.g., λ=10).\"},{\"question\":\"How is queue reprioritization handled over time?\",\"answer\":\"The value of λ is tuned in real time based on the availability of resources and the current task queue, and the task queue is updated accordingly. The prioritization process can be rerun whenever needed.\"}]","Single Parameter Driven Tunable Resource Allocation for Machine Learning Tasks | PDF",1785812541,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"single-parameter-driven-tunable-resource-allocation-for-machine-learning-tasks","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/single-parameter-driven-tunable-resource-allocation-for-machine-learning-tasks/122724/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"How does the method schedule machine learning tasks across server resources?","Question",{"text":75,"@type":76},"Tasks are divided into queues, then a single optimization parameter λ is determined from real-time resource availability. That λ is used to establish exponential-distribution quantiles to generate an updated prioritized queue.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are existing scheduling mechanisms insufficient for the stated goal?",{"text":80,"@type":76},"Weighted round-robin and batch strategies are approximate and non-deterministic, and they cannot be tuned based on real-time resource availability, nor do they provide a mechanism that automatically recalculates weights as conditions change.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the single parameter λ control in the described techniques?",{"text":84,"@type":76},"λ controls the strength of exponential bias toward higher-priority work, ranging from an equitable queue at λ=1 to increasingly strong bias at larger values, with high-priority-only behavior at sufficiently high λ (e.g., λ=10).",{"name":86,"@type":73,"acceptedAnswer":87},"How is queue reprioritization handled over time?",{"text":88,"@type":76},"The value of λ is tuned in real time based on the availability of resources and the current task queue, and the task queue is updated accordingly. 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