Phan, Linh T.X.Zhang, ZhuoyaoTan, GodfreyJain, SaumyaDuong, HarrisonLee, InsupLoo, Boon Thau2023-05-222023-05-222010-11-012011-04-22https://repository.upenn.edu/handle/20.500.14332/6514This paper examines the feasibility of dynamic rescheduling techniques for effectively utilizing compute resources within a data center. Our work is motivated by practical concerns of Intel’s NetBatch system, an Internet-scale data center based distributed computing platform developed by Intel Corporation for massively parallel chip simulations within the company. NetBatch has been operational for many years, and currently is deployed live on tens of thousands of machines that are globally distributed at various data centers. We perform an analysis of job execution traces obtained over a one year period collected from tens of thousands of NetBatch machines from 20 different pools. Our analysis show that we observe that the NetBatch currently does not make full use of all the resources. Specifically, the job completion time can be severely impacted due to job suspension when higher priority jobs preempt lower priority jobs. We then develop dynamic job rescheduling strategies that adaptively restart jobs to available resources elsewhere, which better utilize system resources and improve completion times. Our trace-driven evaluation results show that dynamic rescheduling enables NetBatch to significantly reduce system waste and completion time of suspended jobs.© ACM 2010. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 11th ACM/IFIP/USENIX International Middleware Conference Industrial Track, http://dx.doi.org/10.1145/1891719.1891720.CPS Real-TimeDistributed computingDynamic reschedulingCloud resource managementTrace-driven analysisIntel NetBatchComputer EngineeringEngineeringPresentation