[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128538-en":3,"doc-seo-128538-105":30,"detail-sidebar-cat-0-en-105":92},{"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":27,"seo_description":14,"update_tm":28,"read_time":29},128538,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning for Performance Prediction of Data Distribution Service (DDS) - accepted version","Networking middleware following the Data Distribution Service (DDS) specification supports real-time mission-critical applications including autonomous vehicles, energy management, and air traffic control. DDS uses publish-subscribe communication and exposes Quality of Service (QoS) parameters so applications can match communication needs. Achieving required performance is difficult because the QoS parameter space is large. The paper predicts DDS performance metric distributions across configurations by training ML models on measurements from selected setups.","City Research Online  \nCity, University of London Institutional Repository  \n\n| Citation: Peeroo, K. , Popov, P. T. , Stankovic, V. & Weyde, T. (2024) . Machine Learning for Performance Prediction of Data Distribution Service (DDS) . Paper presented at the European Dependable Computing Conference, 8-11 Apr 2024, Leuven, Belgium.\u003Cbr>This is the accepted version of the paper.\u003Cbr>This version of the publication may differ from the final published version. |\n| --- |\n| Permanent repository link: [https://openaccess.city.ac.uk/id/eprint/32652/](https://openaccess.city.ac.uk/id/eprint/32652/)[ ](https://openaccess.city.ac.uk/id/eprint/32652/)[Link to published version](Link to published version:)[:](Link to published version:)\u003Cbr>Copyright: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to.\u003Cbr>Reuse: Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content isnot changed in any way. |\n\n\n| City Research Online: | [http://openaccess.city.ac.uk/](http://openaccess.city.ac.uk/) | [publications@city.ac.uk](publications@city.ac.uk) |\n| --- | --- | --- |\n|  |  |  |\n\nMachine Learning for Performance Prediction of Data Distribution Service (DDS)  \nKaleem Peeroo  \nCity, University of London London, UK  \n[Kaleem.Peeroo@city.ac.uk](Kaleem.Peeroo@city.ac.uk)  \nPeter Popov  \nCity, University of London London, UK  \n[P.T.Popov@city.ac.uk](P.T.Popov@city.ac.uk)  \nVladimir Stankovic  \nCity, University of London London, UK  \n[Vladimir.Stankovic.1@city.ac.uk](Vladimir.Stankovic.1@city.ac.uk)  \nTillman Weyde  \nCity, University of London London, UK  \n[T.E.Weyde@city.ac.uk](T.E.Weyde@city.ac.uk)  \nAbstract—Networking middleware following the Data Distribution Service (DDS) specification is used in real-time missioncritical systems such as autonomous vehicles, energy management systems, and air traffic control. DDS follows the publishsubscribe communication pattern and offers a set of Quality of Service (QoS) parameters, allowing the users to align the data communication to the needs of the application.  \nConfiguring DDS to achieve the required performance is a difficult task, given the large space of QoS parameter values. Experimental evaluation of performance levels with a real DDS system for different QoS configurations can be complex and require substantial time and resources.  \nWe propose the use of Machine Learning (ML) models to predict the performance metric distribution of DDS under different configurations. This is done by using performance measurements of some configurations to train an ML model. The trained model can then be used to predict the performance distribution of DDS under other system configurations. Since the prediction is computationally inexpensive, we can predict the performance of many different configurations to find a suitable one for given requirements. To the best of our knowledge, this is the first time this approach has been applied to DDS performance evaluation.  \nWe used random forests (RF) as an ML method and linear regression (LR) as a baseline. We selected thirteen performance metrics, and for each, we trained an RF model and tuned its hyperparameters. We tested the final models on system configurations unseen during training, both for parameter values within the training range (interpolation) and outside the training range (extrapolation).  \nThe RF models show better predictive accuracy than the LR baseline. This paper focuses on the models for throughput and latency - the two well-established performance metrics. The models demonstrate coefficients of determination greater t","cbCaicw90DRcPkbG","https://ap.wps.com/l/cbCaicw90DRcPkbG","pdf",388553,1,6,"English","en",105,"# Abstract\n# Introduction\n## Data Distribution Service and its Configuration Parameters\n# Proposed Approach and Evaluation\n## Random Forest Models and Linear Regression Baseline\n## Throughput and Latency Results\n# Conclusion","[{\"question\":\"Why is DDS performance configuration challenging?\",\"answer\":\"DDS performance depends on many QoS and non-QoS parameters, creating a huge configuration space. Exhaustively experimenting with all combinations is time- and resource-intensive.\"},{\"question\":\"How do the proposed machine learning models predict DDS performance?\",\"answer\":\"The approach trains an ML model using performance measurements from some DDS configurations. The trained model is then used to predict the performance distribution for other, unseen configurations.\"},{\"question\":\"What learning methods are used and what do the results show?\",\"answer\":\"Random forests (RF) are used for prediction, with linear regression (LR) as a baseline. The RF models achieve higher predictive accuracy, with strong results for interpolation but reduced performance in extrapolation cases for throughput and latency.\"}]","Machine Learning for Performance Prediction of Data Distribution Service (DDS) - accepted version | PDF",1786001618,15,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-for-performance-prediction-of-data-distribution-service-dds-accepted-version","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-for-performance-prediction-of-data-distribution-service-dds-accepted-version/128538/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is DDS performance configuration challenging?","Question",{"text":76,"@type":77},"DDS performance depends on many QoS and non-QoS parameters, creating a huge configuration space. Exhaustively experimenting with all combinations is time- and resource-intensive.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do the proposed machine learning models predict DDS performance?",{"text":81,"@type":77},"The approach trains an ML model using performance measurements from some DDS configurations. The trained model is then used to predict the performance distribution for other, unseen configurations.",{"name":83,"@type":74,"acceptedAnswer":84},"What learning methods are used and what do the results show?",{"text":85,"@type":77},"Random forests (RF) are used for prediction, with linear regression (LR) as a baseline. The RF models achieve higher predictive accuracy, with strong results for interpolation but reduced performance in extrapolation cases for throughput and latency.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]