{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:12:42Z","timestamp":1779174762971,"version":"3.51.4"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2020,5]]},"abstract":"<jats:p>Resource disaggregation is a new architecture for data centers in which resources like memory and storage are decoupled from the CPU, managed independently, and connected through a high-speed network. Recent work has shown that although disaggregated data centers (DDCs) provide operational benefits, applications running on DDCs experience degraded performance due to extra network latency between the CPU and their working sets in main memory. DBMSs are an interesting case study for DDCs for two main reasons: (1) DBMSs normally process data-intensive workloads and require data movement between different resource components; and (2) disaggregation drastically changes the assumption that DBMSs can rely on their own internal resource management.<\/jats:p>\n          <jats:p>We take the first step to thoroughly evaluate the query execution performance of production DBMSs in disaggregated data centers. We evaluate two popular open-source DBMSs (MonetDB and PostgreSQL) and test their performance with the TPC-H benchmark in a recently released operating system for resource disaggregation. We evaluate these DBMSs with various configurations and compare their performance with that of single-machine Linux with the same hardware resources. Our results confirm that significant performance degradation does occur, but, perhaps surprisingly, we also find settings in which the degradation is minor or where DDCs actually improve performance.<\/jats:p>","DOI":"10.14778\/3397230.3397249","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T11:46:24Z","timestamp":1593431184000},"page":"1568-1581","source":"Crossref","is-referenced-by-count":47,"title":["Understanding the effect of data center resource disaggregation on production DBMSs"],"prefix":"10.14778","volume":"13","author":[{"given":"Qizhen","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Cai","sequence":"additional","affiliation":[{"name":"ShanghaiJiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyi","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastian","family":"Angel","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ang","family":"Chen","sequence":"additional","affiliation":[{"name":"Rice University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boon Thau","family":"Loo","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,6,26]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Apache spark - unified analytics engine for big data. https:\/\/spark.apache.org.  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