An analytical performance and power model based on the transition probability for hard disks

2011 ◽  
Author(s):  
Qiang Zou
Keyword(s):  
Transition Probability ◽  
Analytical Performance ◽  
Power Model ◽  
Hard Disks ◽  
Model Based

scholarly journals PROPOSAL OF THE SPATIAL DEPENDENCE EVALUATION FROM THE POWER SEMIVARIOGRAM MODEL

2017 ◽  
Vol 23 (3) ◽  
pp. 461-475 ◽  
Author(s):  
Ismael Canabarro Barbosa ◽  
Edemar Appel Neto ◽  
Enio Júnior Seidel ◽  
Marcelo Silva de Oliveira
Keyword(s):  
Spatial Correlation ◽  
Spatial Dependence ◽  
Second Order ◽  
Power Model ◽  
Semivariogram Model ◽  
Model Based ◽  
Symmetrical Distribution ◽  
Model Adjustment ◽  
Equivalent Factor

Abstract: In Geostatistics, the use of measurement to describe the spatial dependence of the attribute is of great importance, but only some models (which have second-order stationarity) are considered with such measurement. Thus, this paper aims to propose measurements to assess the degree of spatial dependence in power model adjustment phenomena. From a premise that considers the equivalent sill as the estimated semivariance value that matches the point where the adjusted power model curves intersect, it is possible to build two indexes to evaluate such dependence. The first one, SPD * , is obtained from the relation between the equivalent contribution (α) and the equivalent sill (C * = C 0 + α), and varies from 0 to 100% (based on the calculation of spatial dependence areas). The second one, SDI * , beyond the previous relation, considers the equivalent factor of model (FM * ), which depends on the exponent β that describes the force of spatial dependence in the power model (based on spatial correlation areas). The SDI * ,for β close to 2, assumes its larger scale, varying from 0 to 66.67%. Both indexes have symmetrical distribution, and allow the classification of spatial dependence in weak, moderate and strong.

A Power Monitoring System Based on a Multi-Component Power Model

2018 ◽  
Vol 10 (1) ◽  
pp. 16-30 ◽  
Author(s):  
Weiwei Lin ◽  
Haoyu Wang ◽  
Wentai Wu
Keyword(s):  
Energy Consumption ◽  
Coarse Grained ◽  
Average Error ◽  
Power Model ◽  
Hard Disks ◽  
Monitoring Methods ◽  
Power Estimate ◽  
Power Monitoring ◽  
Multiple Variables ◽  
Software Utility

As the increasing IT energy consumption emerged as a prominent issue, computer system energy consumption monitoring and optimization has gradually become a significant research forefront. However, most existing energy monitoring methods are limited to hardware-based measurement or coarse-grained energy consumption estimation. They cannot provide fine-grained energy consumption data (i.e., component energy consumption) and high-scalability for distributed cloud environments. In this article, the authors first study widely-used power models of CPUs, memory and hard disks. Then, following an investigation into disk power behaviors in sequential I/O and random I/O, they propose an improved I/O-mode aware disk power model with multiple variables and thresholds. They developed EnergyMeter, a monitoring software utility that can provide accurate power estimate by exploiting a multi-component power model. Experiments based on PCMark prove that the average error of EnergyMeter is merely 5% under a variety of workloads

Using power-model based preconditioners for 3D acoustic full waveform inversion

2013 ◽  
Author(s):  
Jean Kormann ◽  
Juan Esteban Rodriguez ◽  
Natalia Gutierrez ◽  
Josep de la Puente ◽  
Mauricio Hanzich ◽  
...  
Keyword(s):  
Waveform Inversion ◽  
Power Model ◽  
Full Waveform Inversion ◽  
Model Based ◽  
Full Waveform

Recovering Truncated Streaming Event Log Using Coupled Hidden Markov Model

2019 ◽  
Vol 34 (04) ◽  
pp. 2059012
Author(s):  
Riyanarto Sarno ◽  
Kelly Rossa Sungkono
Keyword(s):  
Information Systems ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Process Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Process Discovery ◽  
Model Based ◽  
Event Logs ◽  
Event Log

Process discovery is a technique for obtaining process model based on traces recorded in the event log. Nowadays, information systems produce streaming event logs to record their huge processes. The truncated streaming event log is a big issue in process discovery because it inflicts incomplete traces that make process discovery depict wrong processes in a process model. Earlier research suggested several methods for recovering the truncated streaming event log and none of them utilized Coupled Hidden Markov Model. This research proposes a method that combines Coupled Hidden Markov Model with Double States and the Modification of Viterbi–Backward method for recovering the truncated streaming event log. The first layer of states contains the transition probability of activities. The second layer of states uses patterns for detecting traces which have a low appearance in the event log. The experiment results showed that the proposed method recovered appropriately the truncated streaming event log. These results also have proven that the accuracies of recovered traces obtained by the proposed method are higher than those obtained by the Hidden Markov Model and the Coupled Hidden Markov Model.

iMeter: An integrated VM power model based on performance profiling

2014 ◽  
Vol 36 ◽  
pp. 267-286 ◽  
Author(s):  
Hailong Yang ◽  
Qi Zhao ◽  
Zhongzhi Luan ◽  
Depei Qian
Keyword(s):  
Power Model ◽  
Performance Profiling ◽  
Model Based
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