{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T13:40:57Z","timestamp":1777902057012,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"4-5","license":[{"start":{"date-parts":[[2004,5,1]],"date-time":"2004-05-01T00:00:00Z","timestamp":1083369600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIMULATION"],"published-print":{"date-parts":[[2004,5]]},"abstract":"<jats:p>As clusters are being deployed to support a wide range of parallel workloads, scheduling becomes a challenging research issue because these workloads exhibit diverse characteristics and impose varying quality-of-service requirements. Many scheduling strategies are thus proposed, each intended for a different application\/system setting. Due to the lack of a uniform simulation platform, a significant amount of research effort is spent in building a unique simulator for each algorithm, which may lead to false conclusions. This article presents ClusterSchedSim, which is a unifying simulation frame-work of cluster scheduling strategies. The core of ClusterSchedSim includes the node model and an interconnect model. ClusterSchedSim has implemented variations of popular cluster scheduling schemes, and it is flexible enough to add on new schemes. Using ClusterSchedSim, one can conveniently compare different scheduling schemes, profile their executions, and understand the impact of different application and system configuration parameters.<\/jats:p>","DOI":"10.1177\/0037549704044080","type":"journal-article","created":{"date-parts":[[2004,9,13]],"date-time":"2004-09-13T20:00:29Z","timestamp":1095105629000},"page":"191-206","source":"Crossref","is-referenced-by-count":3,"title":["ClusterSchedSim: A Unifying Simulation Framework for Cluster Scheduling Strategies"],"prefix":"10.1177","volume":"80","author":[{"given":"Yanyong","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Electrical & Computer Engineering, Rutgers University, Piscataway, NJ 08854,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anand","family":"Sivasubramaniam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Pennsylvania State                         University, University Park, PA 16802"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2004,5,1]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1109\/71.273047"},{"key":"atypb2","first-page":"226","volume-title":"Proceedings of the ACM SIGMETRICS 1990 Conference on Measurement and Modeling of Computer Systems","author":"Leutenegger, S. 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Ph.D. diss., Department of Electrical Engineering                 and Computer Science, Massachusetts Institute of Technology, Cambridge, MA."},{"key":"atypb14","volume-title":"Proceedings of the USENIX Windows NT Workshop","author":"Buchanan, M."},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1145\/335231.335241"},{"key":"atypb16","doi-asserted-by":"publisher","DOI":"10.1109\/71.954632"},{"key":"atypb17","first-page":"590","volume-title":"Proceedings of the IEEE Symposium on Parallel and Distributed Processing","author":"Crovella, M."},{"key":"atypb18","doi-asserted-by":"publisher","DOI":"10.1109\/32.90447"},{"key":"atypb19","first-page":"219","volume-title":"Proceedings of the Symposium on Parallel Algorithms and Architectures","author":"Herlihy, M."},{"key":"atypb20","first-page":"41","volume-title":"The 13th Symposium on Operating Systems Principles","author":"Karlin, A. 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