{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:19:22Z","timestamp":1750220362049,"version":"3.41.0"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMOD Rec."],"published-print":{"date-parts":[[2021,6,15]]},"abstract":"<jats:p>Distributed transactions on high-overhead TCP\/IP-based networks were conventionally considered to be prohibitively expensive. In fact, the primary goal of existing partitioning schemes is to minimize the number of cross-partition transactions. However, with the new generation of fast RDMAenabled networks, this assumption is no longer valid.<\/jats:p>\n          <jats:p>In this paper, we first make the case that the new bottleneck which hinders truly scalable transaction processing in modern RDMA-enabled databases is data contention, and that optimizing for data contention leads to different partitioning layouts than optimizing for the number of distributed transactions. We then present Chiller, a new approach to data partitioning and transaction execution, which aims to minimize data contention for both local and distributed transactions.<\/jats:p>","DOI":"10.1145\/3471485.3471490","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T05:22:06Z","timestamp":1623993726000},"page":"15-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Chiller"],"prefix":"10.1145","volume":"50","author":[{"given":"Erfan","family":"Zamanian","sequence":"first","affiliation":[{"name":"Brown University"}]},{"given":"Julian","family":"Shun","sequence":"additional","affiliation":[{"name":"MIT CSAIL"}]},{"given":"Carsten","family":"Binnig","sequence":"additional","affiliation":[{"name":"TU Darmstadt"}]},{"given":"Tim","family":"Kraska","sequence":"additional","affiliation":[{"name":"MIT CSAIL"}]}],"member":"320","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/2904483.2904485"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920853"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2815400.2815425"},{"volume-title":"The instacart online grocery shopping dataset","year":"2017","key":"e_1_2_1_4_1"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/3026877.3026892"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/1454159.1454211"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/305219.305248"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732269.2732270"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/2535461.2535475"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3025111.3025125"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2009.090202"},{"issue":"2","key":"e_1_2_1_12_1","first-page":"21","article-title":"The VoltDB main memory DBMS","volume":"36","author":"Stonebraker M.","year":"2013","journal-title":"IEEE Data Eng. 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