[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290609-105":59,"doc-detail-290609-en":131},{"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":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","static-scheduling-in-clouds-research","Static Scheduling in Clouds - research","","Cloud computing delivers pay-per-use resources without requiring users to manage underlying infrastructure. Flextic presents a new job execution environment using scalable static scheduling to offer flexible pricing tradeoffs between execution speed and cost while lowering scheduling overhead for the provider. A prototype evaluation on Amazon EC2 compares against Hadoop, showing low scheduling overhead and up to 15% shorter job duration for data parallel workloads in machine learning, image processing, and gene sequencing.",{"@graph":69,"@context":123},[70,84,106],{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/static-scheduling-in-clouds-research/290609/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/static-scheduling-in-clouds-research/290609.png","ImageObject",300,407,{"name":92,"@type":93},"Stanley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the pricing model in cloud scheduling try to address?","Question",{"text":113,"@type":114},"The document explains that cloud pricing depends on job runtime and data transfer, and that dynamic scheduling prevents clouds from quoting a fixed price before execution due to limited task information.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does Flextic differ from dynamic cloud scheduling?",{"text":118,"@type":114},"Flextic uses static scheduling when task durations can be estimated, enabling lower runtime scheduling overhead and allowing a precomputed schedule so a price can be quoted in advance.",{"name":120,"@type":111,"acceptedAnswer":121},"What performance results are reported for Flextic compared with Hadoop?",{"text":122,"@type":114},"Evaluation on Amazon EC2 shows Flextic has low scheduling latency, can compute ten schedules in about two seconds on a 200-core cloud, and can reduce job execution time by up to 15% versus Hadoop for considered workloads.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290609,1789642190,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},2336477405376,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Static Scheduling in Clouds  \nThomas A. Henzinger IST Austria  \nAnmol V. Singh IST Austria  \nVasu Singh IST Austria  \nThomas Wies IST Austria  \nDamien Zufferey IST Austria  \nAbstract  \nCloud computing aims to give users virtually unlimited pay-per-use computing resources without the burden of managing the underlying infrastructure. We present anew job execution environment Flextic that exploits scalable static scheduling techniques to provide the user with a flexible pricing model, such as a tradeoff between different degrees of execution speed and execution price, and at the same time, reduce scheduling overhead for the cloud provider. We have evaluated a prototype of Flextic on Amazon EC2 and compared it against Hadoop. For various data parallel jobs from machine learning, image processing, and gene sequencing that we considered, Flextic has low scheduling overhead and reduces job duration by up to 15% compared to Hadoop, a dynamic cloud scheduler.  \n1 Introduction  \nComputing services that are provided by datacenters over the internet are now commonly referred to as cloud computing. The price that a user is required to pay for the execution of a particular job in the cloud depends on the length of the job and the amount of data transfer involved in the job execution. We believe that this pricing model is intuitive for long term rentals of computing instanceson the cloud. However, in our experience of running MapReduce jobs using Amazon Elastic MapReduce [2], we observed nonlinearity in pricing due to the fact that instances are rented on per-hour basis. Amazon Elastic MapReduce allows the user to specify the number of instances for the mappers, the mapper and reducer tasks, and the input data. For example, consider a MapReduce job that finishes in 61 minutes using 10 instances, and in 105 minutes using 6 instances. The cloud user certainly pays a lot less for the job with 6 instances. A more user-friendly pricing model will inform the user beforehand about the price of the computation depending upon  \nFigure 1: Flextic Workflow  \nthe number of instances rented. However, as clouds conventionally rely on dynamic scheduling, it is not possible for clouds to quote a certain price to the user before the actual execution of the job. Dynamic scheduling is generally justified by the lack of information of the job components (called tasks) . It turns out that in a large fraction of jobs from domains of machine learning, biocomputing, and image processing, it is indeed possible to estimate the maximum time required for each task in the job. We believe that static scheduling for these jobs can give significant benefits over dynamic scheduling. First of all, static scheduling imposes less runtime overhead. Moreover, static scheduling allows for pre-fetching required data and pipelining different stages of task execution. At the same time, static scheduling is more userfriendly, as the precomputed schedule allows to quote a price for the computation. Based on static scheduling, we develop Flextic, a system where the user specifies the job in terms of mapper and reducer tasks (in general a directed acyclic graph of tasks), the input data, and instead of choosing the number of instances, chooses a price of execution and the finish time.  \nA brief overview of Flextic. We first describe our vision of Flextic (shown in Figure 1) . (1) A user writes  \na program in the Flextic job description language specifying the job characteristics, like maximum task durations and data sizes. (2) The program is parsed into an execution plan. An execution plan is a directed acyclic graph, corresponding to the user program. The execution plan is input to a static job scheduler. (3) The static job scheduler takes the execution plan and computes possible schedules for executing the user job on the cloud.  \n(4) These schedules (in terms of finish time and price) are presented to the user. (5) The user chooses a schedule from the set of presented schedules, thus specifyin","cbCaipnJo4HCZPbx","https://ap.wps.com/l/cbCaipnJo4HCZPbx","pdf",187365,"English","# Introduction\n# Flextic\n## An Example\n## The Job Language","[{\"question\":\"What problem does the pricing model in cloud scheduling try to address?\",\"answer\":\"The document explains that cloud pricing depends on job runtime and data transfer, and that dynamic scheduling prevents clouds from quoting a fixed price before execution due to limited task information.\"},{\"question\":\"How does Flextic differ from dynamic cloud scheduling?\",\"answer\":\"Flextic uses static scheduling when task durations can be estimated, enabling lower runtime scheduling overhead and allowing a precomputed schedule so a price can be quoted in advance.\"},{\"question\":\"What performance results are reported for Flextic compared with Hadoop?\",\"answer\":\"Evaluation on Amazon EC2 shows Flextic has low scheduling latency, can compute ten schedules in about two seconds on a 200-core cloud, and can reduce job execution time by up to 15% versus Hadoop for considered workloads.\"}]","Static Scheduling in Clouds - research | PDF",15]