[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85775-en":3,"doc-seo-85775-105":29,"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":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},85775,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Analyzing Interaction Between CCAs and Traffic Policers","A formal framework analyzes how traffic policers—implemented via phantom queues or token buckets—interact with any congestion control algorithm (CCA). The framework characterizes how network operators can choose policer parameters such as phantom queue size and safe rate thresholds to enforce average service rates. It also explains why CCAs respond differently to traffic policers than to traffic shapers, yielding guidance for improved configuration and more predictable rate enforcement.","arXiv :2607 .09984v 1 [ cs .NI] 10 Jul 2026  \nAnalyzing Interaction Between CCAs and Traffic  \nPolicers  \nAmmar Tahir  \nUIUC  \nChampaign, IL  \n[ammart2@illinois.edu](ammart2@illinois.edu)  \nAbstract  \nWe describe details of a formal framework to study the interaction between traffic policers, implemented using phantom queues or token buckets, and any arbitrary congestion control algorithm (CCA) . This framework allows network providers to figure out configurations for their traffic policers (phantom queue size, safe rate thresholds, etc.) . We also use this framework to describe why CCAs interact differently with a traffic policer compared to traffic shapers.  \n1 Introduction  \nTraffic policers are a well-known and widely used mechanism to implement rate-limiting in modern networks. Compared to traffic shapers, the other common mechanism for rate-limiting, traffic policersare cheap and lightweight, as they do not need to store packets in memory buffers. Despite this, traffic policers are considered an inferior mechanism, as they interact poorly with widely deployed congestion control algorithms (like TCP Cubic, Reno, and BBR), often leading to high bursts, poorly enforced rates, and high drop rates. Our recent wor, BC-PQP [1], has proposed mechanisms to improve different aspects of traffic policers: better average rate conformity, controlled bursts, and the ability to implement various rate-sharing policies within the rate-limited traffic aggregate. This short writeup builds on insights from BC-PQP [1] to formally study the interaction between any arbitrary CCA and a traffic policer. Our analysis not only helps explain why CCAs interact quite differently with a traffic policer compared to a traffic shaper, but also helps operators configure traffic policers better.  \n2 Framework  \nWe are going to model an arbitrary CCA using a fluid model; thus, the sending rate of the CCA at time t is given by r(t) . Given that traffic policers lack physical buffers, our flow does not incur any queuing delay and only experiences packet losses as a signal of congestion (phantom queue being full) . Thus,  \nwe model a CCA with two functions: 1) an increment function, Inc, which increases the sending rate r (t) over time, i.e. , ~~dr~~dt , represented as, r˙(t), from now onwards, and 2) a decrement function, Dec (r), which decreases the sending rate r(t) after a loss event.  \nWe now assume that CCA is in its stable stage, i.e., it has exited its initial slow-start phase, and the entire CCA behavior is modeled by the Inc = r˙(t) and Dec (r) functions. In this stage, we call rh to be the rate right before a packet loss event (and accompanying rate reduction) and rl the rate right after a loss event. Then we have:  \nrl = Dec(rh) (1)  \nAfter the rate reduction, CCA increments its rate from rl over time. At any time τ since this rate reduction, rate, r (τ), can be given as:  \nr (τ) = rl + Z0τ r˙(t)dt (2)  \nFurthermore, we can find bytes sent over this duration by integrating r(τ) function w.r.t time t:  \nA (τ) = Z0τ r (t)dt (3)  \nThis can be expanded and simplified as follows:  \nA (τ) = rl τ + Z0τ Z0u r˙(u)dudt  \nA (τ) = rl τ + Z0τ (τ − t)r˙(t)dt (4)  \n2.1 Sizing the Traffic Policer  \nWe now describe how to find the phantom queue size (or interchangably token bucket size) that ensures that any flow using the defined CCA can maintain set service rate r on average over time. Note that after a rate decrement, triggered by a packet loss once phantom queue became fill, the rate is reduced to rl \u003C r. If it takes τl time for the rate to go from rl to r, the flow has a byte deficit of A(τl) − rτl . Thus phantom queue should be sized at least greater than this to ensure that flow is able to send deficit amount of bytes once its rate starts exceeding service rate r.  \nTo calculate this, we first compute τl by putting r on the right hand side of Eq. 2. Then we calculate phantom queue size Q by the following relationship:  \nQ ≥ max(0, rτl − A(τl )) (5)  \nNote tha","cbCaim7zJvVHa0o4","https://ap.wps.com/l/cbCaim7zJvVHa0o4","pdf",432973,1,9,"English","en",105,"# Introduction\n# Framework\n## Sizing the Traffic Policer\n## Safe Rate Thresholds\n## Congestion Window Based CCAs","[{\"question\":\"What problem does the framework address regarding CCAs and traffic policers?\",\"answer\":\"It studies the interaction between traffic policers (phantom queues or token buckets) and an arbitrary congestion control algorithm, focusing on how policer behavior affects CCA dynamics and enforcement quality.\"},{\"question\":\"How is phantom queue size determined in the framework?\",\"answer\":\"The method computes the time for the CCA to increase from the post-loss rate to the target service rate, then sets the phantom queue size to cover the resulting byte deficit, using Q ≥ max(0, rτl − A(τl )).\"},{\"question\":\"Why do traffic policers interact poorly with common CCAs in practice?\",\"answer\":\"Because they enforce rate limiting without buffering, leading to behaviors such as high bursts, poorly enforced rates, and high drop rates, which can interact unfavorably with algorithms like TCP Cubic, Reno, and BBR.\"}]",1784206184,23,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":27},"analyzing-interaction-between-ccas-and-traffic-policers","",{"@graph":35,"@context":85},[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/analyzing-interaction-between-ccas-and-traffic-policers/85775/",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",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the framework address regarding CCAs and traffic policers?","Question",{"text":75,"@type":76},"It studies the interaction between traffic policers (phantom queues or token buckets) and an arbitrary congestion control algorithm, focusing on how policer behavior affects CCA dynamics and enforcement quality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is phantom queue size determined in the framework?",{"text":80,"@type":76},"The method computes the time for the CCA to increase from the post-loss rate to the target service rate, then sets the phantom queue size to cover the resulting byte deficit, using Q ≥ max(0, rτl − A(τl )).",{"name":82,"@type":73,"acceptedAnswer":83},"Why do traffic policers interact poorly with common CCAs in practice?",{"text":84,"@type":76},"Because they enforce rate limiting without buffering, leading to behaviors such as high bursts, poorly enforced rates, and high drop rates, which can interact unfavorably with algorithms like TCP 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