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bayes theorem Based Virtual Machine Scheduling for Optimal Energy Consumption

Computer Systems Science & Engineering 2022年第10期43卷 159-174页

作者： R.Swathy B.Vinayagasundaram Computer Center Madras Institute of TechnologyAnna UniversityChennai600044India

This paper proposes an algorithm for scheduling Virtual Machines(VM)with energy saving strategies in the physical servers of cloud data *** saving strategy along with a solution for productive resource utilizationfor VM deployment in cloud data centers is modeled by a combination of“VirtualMachine Scheduling using bayes theorem”algorithm(VMSBT)and Virtual Machine Migration(VMMIG)*** is shown that the overall data center’sconsumption of energy is minimized with a combination of VMSBT algorithmand Virtual Machine Migration(VMMIG)*** machine migrationbetween the active physical servers in the data center is carried out at periodicalintervals as and when a physical server is identified to be *** VMscheduling,the optimal data centers are clustered using bayes theorem and VMsare scheduled to appropriate data center using the selection policy that identifiesthe cluster with lesser energy *** using bayes rule minimizesthe number of server choices for the selection *** of bayestheorem in clustering has enabled the proposed VMSBT algorithm to schedule thevirtual machines on to the physical server with minimal execution *** proposedalgorithm is compared with other energy aware VM allocations algorithmsviz.“Ant-Colony”optimization-based(ACO)allocation scheme and“min-min”scheduling *** experimental simulation results prove that the proposedcombination of‘VMSBT’and‘VMMIG’algorithm outperforms othertwo strategies and is highly effective in scheduling VMs with reduced energy consumptionby utilizing the existing resources productively and by minimizing thenumber of active servers at any given point of time.

关键词： Energy saving strategy VM scheduling VM migration bayes theorem resource utilization

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Population pharmacokinetic model of valproate and prediction of valproate serum concentrations in children with epilepsy

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Acta Pharmacologica Sinica 2004年第12期25卷 10-17页

作者： De-chun JIANG, Li WANG2 Department of Pediatrics, Peking University First Hospital, TherapeuticDrug Monitoring and Clinical Toxicology, Center of Peking University, Beijing 100034, China Department of Pediatrics Peking University First Hospital Therapeutic Drug Monitoring and Clinical Toxicology Center of Peking University 北京 100034

AIM: Using sparse data of valproate (VPA) serum concentrations to build a population pharmacokinetic (PPK) model of VPA in Chinese children with epilepsy and to predict serum concentrations for new patients using a bayesian approach. METHODS: Two hundred epileptic children, whose VPA serum concentrations were collected, were divided randomly into two groups (A and B, n=100 each). The PPK parameter values of group A were calculated to establish a PPK Model by using the NPEM Program of USC*PACK software. Based on it, VPA serum concentrations of group B were predicted with the bayesian Fitting Program of the USC*PACK software. To assess the accuracy and precision of prediction, a paired-comparisons t-test was run between predicted and ob- served concentrations, and then the mean prediction error (MPE), mean square prediction error (MSPE), root mean square prediction error (RMSPE), and coincidence rates for different percentages of prediction error were all calculated. RESULTS: Optimum PPK parameters were: Ka, 2.522±2.743 h-1; Vs, 0.329±0.496 L/kg; and Kel, 0.0438±0.0384 h-1. For group B, there was no significant difference between predicted and observed concentrations. MPE was -0.43 mg/L, MSPE was 115.40 (mg/L)2, and RMSPE was 5.47 mg/L. The coincidence rates for percent- ages of prediction error, which were less than 5 %, 10 %, 15 %, 20 %, 25 %, and 30 %, were 62 %, 74 %, 82 %, 85 %, 89 %, and 93 %, respectively. CONCLUSION: A PPK model of VPA in epileptic children was successfully established. Based on it, VPA serum concentrations can be predicted accurately with a bayesian approach.

关键词： pharmacokinetics valproic acid epilepsy bayes theorem USC*PACK software

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