{"date_updated":"2023-09-26T10:40:55Z","article_processing_charge":"No","month":"05","day":"01","type":"conference","abstract":[{"text":"Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner?\r\nTo address this question, we define the batch dynamic CONGEST model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labelled graph under batch changes. We investigate, when a batch of alpha edge label changes arrive, - how much time as a function of alpha we need to update an existing solution, and - how much information the nodes have to keep in local memory between batches in order to update the solution quickly.\r\nOur work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. The diverse time complexity of our model spans from constant time, through time polynomial in alpha, and to alpha time, which we show to be enough for any task.","lang":"eng"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2005.07637"}],"department":[{"_id":"DaAl"}],"publication_status":"published","date_published":"2021-05-01T00:00:00Z","scopus_import":"1","publisher":"Association for Computing Machinery","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2022-03-18T08:48:41Z","oa":1,"oa_version":"Preprint","related_material":{"record":[{"relation":"extended_version","id":"10855","status":"public"}]},"status":"public","title":"Input-dynamic distributed algorithms for communication networks","quality_controlled":"1","publication_identifier":{"isbn":["9781450380720"]},"external_id":{"arxiv":["2005.07637"]},"page":"71-72","doi":"10.1145/3410220.3453923","publication":"Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","author":[{"first_name":"Klaus-Tycho","last_name":"Foerster","full_name":"Foerster, Klaus-Tycho"},{"first_name":"Janne","last_name":"Korhonen","id":"C5402D42-15BC-11E9-A202-CA2BE6697425","full_name":"Korhonen, Janne"},{"last_name":"Paz","first_name":"Ami","full_name":"Paz, Ami"},{"orcid":"0000-0002-6432-6646","id":"334EFD2E-F248-11E8-B48F-1D18A9856A87","full_name":"Rybicki, Joel","last_name":"Rybicki","first_name":"Joel"},{"full_name":"Schmid, Stefan","first_name":"Stefan","last_name":"Schmid"}],"language":[{"iso":"eng"}],"_id":"10854","ec_funded":1,"citation":{"ista":"Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. 2021. Input-dynamic distributed algorithms for communication networks. Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. SIGMETRICS: International Conference on Measurement and Modeling of Computer Systems, 71–72.","apa":"Foerster, K.-T., Korhonen, J., Paz, A., Rybicki, J., &#38; Schmid, S. (2021). Input-dynamic distributed algorithms for communication networks. In <i>Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems</i> (pp. 71–72). Virtual, Online: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3410220.3453923\">https://doi.org/10.1145/3410220.3453923</a>","chicago":"Foerster, Klaus-Tycho, Janne Korhonen, Ami Paz, Joel Rybicki, and Stefan Schmid. “Input-Dynamic Distributed Algorithms for Communication Networks.” In <i>Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems</i>, 71–72. Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3410220.3453923\">https://doi.org/10.1145/3410220.3453923</a>.","ama":"Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. Input-dynamic distributed algorithms for communication networks. In: <i>Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems</i>. Association for Computing Machinery; 2021:71-72. doi:<a href=\"https://doi.org/10.1145/3410220.3453923\">10.1145/3410220.3453923</a>","short":"K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, S. Schmid, in:, Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Association for Computing Machinery, 2021, pp. 71–72.","mla":"Foerster, Klaus-Tycho, et al. “Input-Dynamic Distributed Algorithms for Communication Networks.” <i>Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems</i>, Association for Computing Machinery, 2021, pp. 71–72, doi:<a href=\"https://doi.org/10.1145/3410220.3453923\">10.1145/3410220.3453923</a>.","ieee":"K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic distributed algorithms for communication networks,” in <i>Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems</i>, Virtual, Online, 2021, pp. 71–72."},"conference":{"start_date":"2021-06-14","name":"SIGMETRICS: International Conference on Measurement and Modeling of Computer Systems","location":"Virtual, Online","end_date":"2021-06-18"},"year":"2021","project":[{"grant_number":"805223","_id":"268A44D6-B435-11E9-9278-68D0E5697425","name":"Elastic Coordination for Scalable Machine Learning","call_identifier":"H2020"},{"call_identifier":"H2020","grant_number":"840605","name":"Coordination in constrained and natural distributed systems","_id":"26A5D39A-B435-11E9-9278-68D0E5697425"}],"acknowledgement":"We thank Jukka Suomela for discussions. We also thank our shepherd Mohammad Hajiesmaili and the reviewers for their time and suggestions on how to improve the paper. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML), from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska–Curie grant agreement No. 840605, from the Vienna Science and Technology Fund (WWTF) project WHATIF, ICT19-045, 2020-2024, and from the Austrian Science Fund (FWF) and netIDEE SCIENCE project P 33775-N."}