[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-673287-105":59,"doc-detail-673287-en":129},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":122,"head_meta":124,"extra_data":126,"updated_unix":128},105,"en","rethinking-http-api-rate-limiting-a-client-side-approach","Rethinking HTTP API Rate Limiting - A Client-Side Approach","","HTTP underpins modern Internet services, and providers enforce quotas to regulate HTTP API traffic for scalability and reliability. When requests exceed quotas, clients are throttled and must retry, yet server-only controls become inefficient when independent clients share a quota. Lacking visibility into others’ load, clients’ retry timing can fail, wasting attempts and incurring costs. This work designs adaptive client-side mechanisms using minimal feedback, presenting ATB and AATB algorithms that infer congestion to schedule retries. Experiments with real traces and synthetic datasets up to 100 clients reduce HTTP 429 errors by up to 97.3% versus exponential backoff, with a modest completion-time increase outweighed by fewer errors.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/rethinking-http-api-rate-limiting-a-client-side-approach/673287/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/rethinking-http-api-rate-limiting-a-client-side-approach/673287.png","ImageObject",300,407,{"name":92,"@type":93},"xXChrisXx","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02",true,{"@type":101,"interactionType":102,"userInteractionCount":8},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"Why do server-side rate limiting strategies become inefficient with shared quotas?","Question",{"text":111,"@type":112},"When multiple independent clients contribute to the same quota, clients lack visibility into others’ load. As a result, their retries may not align with real availability and can fail.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"What problem do conventional exponential backoff retry strategies cause?",{"text":116,"@type":112},"Although widely recommended, exponential backoff can lead to excessive retry attempts and significant costs, as shown by the evaluation in the paper.",{"name":118,"@type":109,"acceptedAnswer":119},"How do ATB and AATB improve client-side retries?",{"text":120,"@type":112},"They infer system congestion to schedule retries without central control. ATB works offline via service workers, while AATB enhances retry behavior using aggregated telemetry data.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},673287,1793627874,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":138,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":67,"update_tm":128,"read_time":142},8796112263567,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","This manuscript has been accepted for presentation at IEEE CCNC 2026 . You can use this material personally. Reprinting or republishing this material for the purpose of advertising or promotion, creating new collective works, reselling or redistributing to servers or lists, or using any copyrighted component in other works must adhere to IEEE policy. The DOI will be supplied as soon as it becomes available.  \nRethinking [HTTP API Rate Limiting: A](HTTP API Rate Limiting: A)[ ](HTTP API Rate Limiting: A)Client-Side Approach  \nBehrooz Farkiani, Fan Liu, Patrick Crowley  \nWashington University in St. Louis,  \n1 Brookings Dr., St. Louis, MO, 63130, USA  \nEmails: {b.farkiani, fan.liu, [pcrowley](pcrowley}@wustl.edu)[}](pcrowley}@wustl.edu)[@wustl.edu](pcrowley}@wustl.edu)  \narXiv :2510 .045 16v 3 [ cs .NI] 3 Nov 2025  \nAbstract—[HTTP](HTTP) underpins modern Internet services, and providers enforce quotas to regulate [HTTP API traffic](HTTP API traffic)[ ](HTTP API traffic)[for scalability and reliability. When requests exceed quotas](for scalability and reliability. When requests exceed quotas), clients are throttled and must retry. Server-side enforcement protects the service. However, when independent clients’ usage counts toward a shared quota, server-only controls are inefficient; clients lack visibility into others’load, causing their retry attempts to potentially fail. Indeed, retry timing is important since each attempt incurs costs and yields no benefit unless admitted. While centralized coordination could address this, practical limitations have led to widespread adoption of simple client-side strategies like exponential backoff. As we show, these simple strategies cause excessive retries and significant costs. We design adaptive client-side mechanisms requiring no central control, relying only on minimal feedback. We present two algorithms: ATB, an offline method deployable via service workers, and AATB, which enhances retry behavior using aggregated telemetry data. Both algorithms infer system congestion to schedule retries. Through emulations with real-world traces and synthetic datasets with up to 100 clients, we demonstrate that our algorithms reduce [HTTP 429](HTTP 429) errors by up to 97.3% compared to exponential backoff, while the modest increase in completion time is outweighed by the reduction in errors.  \nIndex Terms—[HTTP](HTTP) API, Rate Limiting, Distributed Algorithm, Congestion Control  \nI. INTRODUCTION  \nAcross the Internet, mechanisms at various network layers prevent senders from overwhelming receivers [1]–[6]. At the application layer, [HTTP carries](HTTP carries) over 50% of Internet traffic and is central to service delivery [7], [8] . [HTTP APIs](HTTP APIs) are ubiquitous in modern cloud services. To protect backend services, incoming [HTTP traffic must](HTTP traffic must)[ ](HTTP traffic must)be regulated [9]; as with other layers, controls can be deployed on both clients and servers. On the server side, providers must define quotas and monitor requests over time to prevent overload, which requires three elements: (1) mapping each request to its service usage,(2) selecting enforcement granularity and time horizons, and (3) enforcing quotas and handling excess usage [10] . The mapping from a client request to consumed ser  \nvice depends on the nature of service, ranging from oneThis work was supported by NSF CNS Award 2213672 .  \nto-one to cost-weighted calculations that reflect processing complexity (e.g., [11]–[13]) . Providers then choose enforcement granularity (IP address, API key, user ID) and interval (per second, minute, hour) to fit operational needs and set limits accordingly. Enforcement can use sliding window, leaky bucket, or token bucket [14] algorithms among others: sliding window counts requests within a time frame; the leaky bucket algorithm employs a fixed bucket size and processes requests at a constant rate, discarding any requests that exceed capacity; token bucket issues tokens","cbCaigm4JbeaOWTf","https://ap.wps.com/l/cbCaigm4JbeaOWTf","pdf",386083,"English","# Introduction\n# Server-Side Rate Limiting and Its Limits\n# Client-Side Retry Strategies\n# Proposed Client-Side Algorithms (ATB, AATB)\n# Evaluation Method and Results","[{\"question\":\"Why do server-side rate limiting strategies become inefficient with shared quotas?\",\"answer\":\"When multiple independent clients contribute to the same quota, clients lack visibility into others’ load. As a result, their retries may not align with real availability and can fail.\"},{\"question\":\"What problem do conventional exponential backoff retry strategies cause?\",\"answer\":\"Although widely recommended, exponential backoff can lead to excessive retry attempts and significant costs, as shown by the evaluation in the paper.\"},{\"question\":\"How do ATB and AATB improve client-side retries?\",\"answer\":\"They infer system congestion to schedule retries without central control. ATB works offline via service workers, while AATB enhances retry behavior using aggregated telemetry data.\"}]","Rethinking HTTP API Rate Limiting - A Client-Side Approach | PDF",15]