{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:41:38Z","timestamp":1750308098989,"version":"3.41.0"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2005,9,1]],"date-time":"2005-09-01T00:00:00Z","timestamp":1125532800000},"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":["SIGMETRICS Perform. Eval. Rev."],"published-print":{"date-parts":[[2005,9]]},"abstract":"<jats:p>It is well known that scheduling the service of customers according to the shortest remaining processing time (SRPT) policy is optimal with respect to minimizing the mean sojourn time of customers. Recent studies have further argued that SRPT does not unfairly penalize large customers in order to benefit small customers, and therefore these studies propose the use of SRPT to improve performance in various applications. However, as Schrage and Miller point out [10], the SRPT policy can raise several difficulties for a number of important reasons. Such difficulties can arise from the inability to accurately predict service times, or the complicated nature of implementing the preemptive aspect of the SRPT policy which requires keeping track of the remaining service times of all waiting customers as well as of the customer in service. Normally, preemption also incurs additional costs. and thus one might want to avoid the preemption of customers in service whose remaining service time is not much larger than that of a new arrival.<\/jats:p>","DOI":"10.1145\/1101892.1101907","type":"journal-article","created":{"date-parts":[[2007,1,17]],"date-time":"2007-01-17T18:32:02Z","timestamp":1169058722000},"page":"42-44","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic scheduling to optimize utility functions of sojourn time moments in queueing systems"],"prefix":"10.1145","volume":"33","author":[{"given":"Yingdong","family":"Lu","sequence":"first","affiliation":[{"name":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark S.","family":"Squillante","sequence":"additional","affiliation":[{"name":"IBM Thomas J. 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A control study of time-function scheduling: Part I . Tech. Rep. RC 19765 , IBM Res. Div. , Sept. 1994 . M. S. Squillante, L. L. Fong, S. Liu, S. K. Ryan. A control study of time-function scheduling: Part I. Tech. Rep. RC 19765, IBM Res. 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