[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82139-en":3,"doc-seo-82139-105":29,"detail-sidebar-cat-0-en-105":83},{"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":20,"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":13,"seo_description":14,"update_tm":27,"read_time":28},82139,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Pareto-Optimal Scheduling in the Half-batch Multiserver-job Model","Large-scale computing systems host heterogeneous job classes, where latency-sensitive large jobs need simultaneous access to many servers, while small jobs consume a single server and exist to preserve throughput. The half-batch multiserver-job (MSJ) framework models this with Poisson arrivals for large jobs and perpetually available small jobs. A complete, non-asymptotic Pareto characterization is proved for the frontier balancing large-job mean response time against small-job throughput, generated by (k, n)-convoy policies and their convex mixtures, valid for any stable load, server count, and large-job size distribution.","arXiv :2607 .08999v 1 [ cs .PF] 10 Jul 2026  \nPareto-Optimal Scheduling in the Half-batch Multiserver-job Model  \nZiyuan Wanga , Izzy Grosofa  \na Northwestern University, Deparment of Industrial Engineering and Management Science, Evanston, 60201, IL, USA  \n[ziyuanwang2027@u.northwestern.edu](ziyuanwang2027@u.northwestern.edu), [izzy.grosof@northwestern.edu](izzy.grosof@northwestern.edu)  \nAbstract  \nIn large-scale computing systems, jobs often demand heterogeneous server allocations: large jobs that occupy a substantial fraction of the servers are of high importance and are thus latency-sensitive, while small jobs fill in the remaining capacity to maintain throughput. To model this dynamic, we introduce the half-batch multiserver-job (MSJ) framework, a queueing model in which large jobs arrive according to a Poisson process and require all servers simultaneously, while small jobs, each needing only one server, are always available.  \nWe prove that, in the half-batch MSJ model, the Pareto frontier for large-job mean response time and small-job throughput admits a simple and exact characterization. It is generated by a family of convoy policies, under which the system serves small jobs until k large jobs have arrived and then switches to serving large jobs, together with convex combinations of neighboring convoy policies. Our result is fully general and non-asymptotic, holding for every stable arrival rate λ, every number of servers n, and every large-job size distribution S.  \n1. Introduction  \nIn modern large-scale computing systems, jobs often have highly heterogeneous resource requirements. This is especially clear in supercomputing systems such as Frontier at Oak Ridge [1], where the system is built primarily to serve very large jobs, often called leadership-class jobs, that request more than half of the existing nodes. At the same time, smaller jobs are used to fill otherwise idle capacity.  \nThis setting is naturally modeled by the multiserver-job (MSJ) framework [2] . In these systems, a job requests a number of servers or nodes and occupies them simultaneously throughout service. This distinguishes the setting both from classical single-server models, which cannot represent simultaneous server needs, and from multiresource models, where multiple resource dimensions are needed. Similar phenomena also arise in datacenters and large-scale machine learning workloads, where a relatively small number of important, latency-sensitive large jobs are given priority while coexisting with a much larger population of low-resource jobs.  \nThe presence of these two job classes creates a trade-off between small-job throughput and large-job latency. Moreover, conventional queueing models do not capture this trade-off well. In a standard open system, a large throughput region is typically a prerequisite for good latency, while in a closed system latency and throughput are linked by Little’s law. Here, by contrast, the central question is how to design a policy that balances latency against throughput.  \nA key ingredient of this trade-off is non-preemptive scheduling. If preemption were free, then one could simply run small jobs whenever capacity is available and interrupt them immediately when a large job needs to start. In practice, however, preemption is often expensive or unavailable. A running small job may have to be killed, checkpointed, or allowed to complete at the expense of large jobs.  \nExisting studies of nonpreemptive policies do not fully capture this trade-off. Some nonpreemptive throughput-oriented policies, such as Randomized Timers [3] and the Markovian Service Rate policy [4], can achieve strong stability guarantees by building very long convoys of large jobs. These policies accumulate many jobs of identical resource requirements, and then serve all of them in sequence in a very long convoy. Such policies may be unacceptable in practice because of the latency they impose.  \nTo study systems in which this trade-off is ","cbCaiqdgTxuK1WES","https://ap.wps.com/l/cbCaiqdgTxuK1WES","pdf",648035,1,35,"English","en",105,"# Abstract\n# Introduction\n## System model and motivation\n## Trade-off between throughput and latency\n## Non-preemptive scheduling and limitations of existing policies\n## Half-batch MSJ model and the Pareto-optimal question\n## Convoy policies and exact characterization","[{\"question\":\"How is the Pareto frontier characterized?\",\"answer\":\"The Pareto frontier is generated exactly by a family of (k, n)-convoy policies: serve small jobs until the k-th large job arrives, then clear small jobs quickly and serve large jobs exhaustively. Each frontier point is achieved by one convoy policy, or by convex combinations of neighboring convoy policies, including an endpoint with no small-job service.\"}]",1784178409,88,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":27},"pareto-optimal-scheduling-in-the-half-batch-multiserver-job-model","",{"@graph":35,"@context":77},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/pareto-optimal-scheduling-in-the-half-batch-multiserver-job-model/82139/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How is the Pareto frontier characterized?","Question",{"text":75,"@type":76},"The Pareto frontier is generated exactly by a family of (k, n)-convoy policies: serve small jobs until the k-th large job arrives, then clear small jobs quickly and serve large jobs exhaustively. Each frontier point is achieved by one convoy policy, or by convex combinations of neighboring convoy policies, including an endpoint with no small-job service.","Answer","https://schema.org",{"og:url":51,"og:type":79,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":81,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":45,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":45,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":45,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":45,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]