{"status":"public","year":"2019","extern":"1","external_id":{"arxiv":["1904.05474"]},"publication":"SIGMETRICS'19: International Conference on Measurement and Modeling of Computer Systems","_id":"11850","type":"conference","day":"20","scopus_import":"1","citation":{"apa":"Henzinger, M. H., Neumann, S., &#38; Schmid, S. (2019). Efficient distributed workload (re-)embedding. In <i>SIGMETRICS’19: International Conference on Measurement and Modeling of Computer Systems</i> (pp. 43–44). Phoenix, AZ, United States: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3309697.3331503\">https://doi.org/10.1145/3309697.3331503</a>","ista":"Henzinger MH, Neumann S, Schmid S. 2019. Efficient distributed workload (re-)embedding. SIGMETRICS’19: International Conference on Measurement and Modeling of Computer Systems. SIGMETRICS: International Conference on Measurement and Modeling of Computer Systems, 43–44.","chicago":"Henzinger, Monika H, Stefan Neumann, and Stefan Schmid. “Efficient Distributed Workload (Re-)Embedding.” In <i>SIGMETRICS’19: International Conference on Measurement and Modeling of Computer Systems</i>, 43–44. Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3309697.3331503\">https://doi.org/10.1145/3309697.3331503</a>.","ieee":"M. H. Henzinger, S. Neumann, and S. Schmid, “Efficient distributed workload (re-)embedding,” in <i>SIGMETRICS’19: International Conference on Measurement and Modeling of Computer Systems</i>, Phoenix, AZ, United States, 2019, pp. 43–44.","mla":"Henzinger, Monika H., et al. “Efficient Distributed Workload (Re-)Embedding.” <i>SIGMETRICS’19: International Conference on Measurement and Modeling of Computer Systems</i>, Association for Computing Machinery, 2019, pp. 43–44, doi:<a href=\"https://doi.org/10.1145/3309697.3331503\">10.1145/3309697.3331503</a>.","short":"M.H. Henzinger, S. Neumann, S. Schmid, in:, SIGMETRICS’19: International Conference on Measurement and Modeling of Computer Systems, Association for Computing Machinery, 2019, pp. 43–44.","ama":"Henzinger MH, Neumann S, Schmid S. Efficient distributed workload (re-)embedding. In: <i>SIGMETRICS’19: International Conference on Measurement and Modeling of Computer Systems</i>. Association for Computing Machinery; 2019:43–44. doi:<a href=\"https://doi.org/10.1145/3309697.3331503\">10.1145/3309697.3331503</a>"},"quality_controlled":"1","oa_version":"Preprint","month":"06","article_processing_charge":"No","language":[{"iso":"eng"}],"publication_status":"published","oa":1,"author":[{"full_name":"Henzinger, Monika H","first_name":"Monika H","last_name":"Henzinger","orcid":"0000-0002-5008-6530","id":"540c9bbd-f2de-11ec-812d-d04a5be85630"},{"full_name":"Neumann, Stefan","first_name":"Stefan","last_name":"Neumann"},{"full_name":"Schmid, Stefan","first_name":"Stefan","last_name":"Schmid"}],"main_file_link":[{"url":"https://arxiv.org/abs/1904.05474","open_access":"1"}],"publisher":"Association for Computing Machinery","date_updated":"2023-02-17T09:41:45Z","conference":{"start_date":"2019-06-24","name":"SIGMETRICS: International Conference on Measurement and Modeling of Computer Systems","location":"Phoenix, AZ, United States","end_date":"2019-06-28"},"abstract":[{"text":"Modern networked systems are increasingly reconfigurable, enabling demand-aware infrastructures whose resources can be adjusted according to the workload they currently serve. Such dynamic adjustments can be exploited to improve network utilization and hence performance, by moving frequently interacting communication partners closer, e.g., collocating them in the same server or datacenter. However, dynamically changing the embedding of workloads is algorithmically challenging: communication patterns are often not known ahead of time, but must be learned. During the learning process, overheads related to unnecessary moves (i.e., re-embeddings) should be minimized. This paper studies a fundamental model which captures the tradeoff between the benefits and costs of dynamically collocating communication partners on l servers, in an online manner. Our main contribution is a distributed online algorithm which is asymptotically almost optimal, i.e., almost matches the lower bound (also derived in this paper) on the competitive ratio of any (distributed or centralized) online algorithm.","lang":"eng"}],"date_published":"2019-06-20T00:00:00Z","page":"43–44","date_created":"2022-08-16T07:14:57Z","publication_identifier":{"isbn":["978-1-4503-6678-6"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","doi":"10.1145/3309697.3331503","title":"Efficient distributed workload (re-)embedding"}