scholarly journals A Comprehensive Availability Modeling and Analysis of a Virtualized Servers System Using Stochastic Reward Nets

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Tuan Anh Nguyen ◽  
Dong Seong Kim ◽  
Jong Sou Park
Keyword(s):  
Sensitivity Analysis ◽  
Virtual Machine ◽  
Failure Modes ◽  
Virtual Machine Monitor ◽  
System Availability ◽  
Modeling And Analysis ◽  
New Finding ◽  
Hardware Faults ◽  
Availability Modeling ◽  
Availability Model

It is important to assess availability of virtualized systems in IT business infrastructures. Previous work on availability modeling and analysis of the virtualized systems used a simplified configuration and assumption in which only one virtual machine (VM) runs on a virtual machine monitor (VMM) hosted on a physical server. In this paper, we show a comprehensive availability model using stochastic reward nets (SRN). The model takes into account (i) the detailed failures and recovery behaviors of multiple VMs, (ii) various other failure modes and corresponding recovery behaviors (e.g., hardware faults, failure and recovery due to Mandelbugs and aging-related bugs), and (iii) dependency between different subcomponents (e.g., between physical host failure and VMM, etc.) in a virtualized servers system. We also show numerical analysis on steady state availability, downtime in hours per year, transaction loss, and sensitivity analysis. This model provides a new finding on how to increase system availability by combining both software rejuvenations at VM and VMM in a wise manner.

scholarly journals A Comprehensive Sensitivity Analysis of a Data Center Network with Server Virtualization for Business Continuity

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Tuan Anh Nguyen ◽  
Dugki Min ◽  
Jong Sou Park
Keyword(s):  
Sensitivity Analysis ◽  
Data Center ◽  
Queuing Theory ◽  
Failure Modes ◽  
Business Continuity ◽  
System Availability ◽  
Modeling And Analysis ◽  
Server Virtualization ◽  
Sensitivity Assessment ◽  
Availability Modeling

Sensitivity assessment of availability for data center networks (DCNs) is of paramount importance in design and management of cloud computing based businesses. Previous work has presented a performance modeling and analysis of a fat-tree based DCN using queuing theory. In this paper, we present a comprehensive availability modeling and sensitivity analysis of a DCell-based DCN with server virtualization for business continuity using stochastic reward nets (SRN). We use SRN in modeling to capture complex behaviors and dependencies of the system in detail. The models take into account (i) two DCell configurations, respectively, composed of two and three physical hosts in a DCell0unit, (ii) failure modes and corresponding recovery behaviors of hosts, switches, and VMs, and VM live migration mechanism within and between DCell0s, and (iii) dependencies between subsystems (e.g., between a host and VMs and between switches and VMs in the same DCell0). The constructed SRN models are analyzed in detail with regard to various metrics of interest to investigate system’s characteristics. A comprehensive sensitivity analysis of system availability is carried out in consideration of the major impacting parameters in order to observe the system’s complicated behaviors and find the bottlenecks of system availability. The analysis results show the availability improvement, capability of fault tolerance, and business continuity of the DCNs complying with DCell network topology. This study provides a basis of designing and management of DCNs for business continuity.

Performance and Cost Assessment of a Small Solar Photovoltaic System Using Gumbel-Haugaard Family Copula Analysis

2021 ◽  
Author(s):  
Anas Maihulla ◽  
Ibrahim Yusuf ◽  
Saminu Bala
Keyword(s):  
Sensitivity Analysis ◽  
Photovoltaic System ◽  
Cost Assessment ◽  
Time To Failure ◽  
Solar Photovoltaic ◽  
Schematic Diagram ◽  
System Availability ◽  
Solar Photovoltaic System ◽  
Mean Time ◽  
Availability Model

Abstract The main objective of the present study is to analyze the availability of solar photovoltaic system. The solar photovoltaic system in this paper is simple one consisting of four subsystems namely, solar panel subsystem, charge controller subsystem, batteries subsystem and inverter subsystem. Through the schematic diagram of state of the system, availability model is formulated and Chapmen - Kolmogorov differential equations are developed and solved using Gumbel Haugaard family Copula technique. The numerical values for availability, reliability, mean time to failure (MTTF), cost analysis as well as sensitivity analysis are presented. The effects of failure rates to various solar photovoltaic subsystems were developed.

scholarly journals A lightweight virtual machine monitor for Blue Gene/P

2012 ◽  
Vol 26 (2) ◽  
pp. 95-109 ◽  
Author(s):  
Jan Stoess ◽  
Udo Steinberg ◽  
Volkmar Uhlig ◽  
Jens Kehne ◽  
Jonathan Appavoo ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Virtual Machine Monitor ◽  
Blue Gene

Making a Virtual Machine Monitor Interruptible

2011 ◽  
Vol 19 ◽  
pp. 411-420 ◽  
Author(s):  
Megumi Ito ◽  
Shuichi Oikawa
Keyword(s):  
Virtual Machine ◽  
Virtual Machine Monitor

Error Sensitivity Analysis for Machine Tool Based on Three-Dimensional Vector Chain

2013 ◽  
Vol 457-458 ◽  
pp. 1562-1565
Author(s):  
Qiang Huang ◽  
Chan Jun Gao
Keyword(s):  
Sensitivity Analysis ◽  
Machine Tool ◽  
Three Dimensional ◽  
Error Modeling ◽  
Dimensional Vector ◽  
Modeling And Analysis ◽  
Vector Operation ◽  
Error Sensitivity ◽  
Space Error ◽  
Error Sensitivity Analysis

Error modeling and analysis can provide some important direction to the machining precision control. According to the characteristics of topology structure on machine tool, a space error model of machine tool and detailed modeling method are presented in this paper, which are based on three-dimensional vector chain. Taking a lathe as an example, the application method of this model in error sensitivity analysis is introduced. By this model, the relationship between the relative error of workpiece-tool and each source error can be solved by ordinary vector operation, and the analysis efficiency should be enhanced greatly.

scholarly journals Machine learning-based feature importance approach for sensitivity analysis of steel frames

2021 ◽  
Author(s):  
Hyeyoung Koh ◽  
Hannah Beth Blum
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Feature Selection ◽  
Large Scale ◽  
Failure Modes ◽  
Model Development ◽  
Predictive Performance ◽  
Computational Effort ◽  
Structural Systems ◽  
Feature Importance

This study presents a machine learning-based approach for sensitivity analysis to examine how parameters affect a given structural response while accounting for uncertainty. Reliability-based sensitivity analysis involves repeated evaluations of the performance function incorporating uncertainties to estimate the influence of a model parameter, which can lead to prohibitive computational costs. This challenge is exacerbated for large-scale engineering problems which often carry a large quantity of uncertain parameters. The proposed approach is based on feature selection algorithms that rank feature importance and remove redundant predictors during model development which improve model generality and training performance by focusing only on the significant features. The approach allows performing sensitivity analysis of structural systems by providing feature rankings with reduced computational effort. The proposed approach is demonstrated with two designs of a two-bay, two-story planar steel frame with different failure modes: inelastic instability of a single member and progressive yielding. The feature variables in the data are uncertainties including material yield strength, Young’s modulus, frame sway imperfection, and residual stress. The Monte Carlo sampling method is utilized to generate random realizations of the frames from published distributions of the feature parameters, and the response variable is the frame ultimate strength obtained from finite element analyses. Decision trees are trained to identify important features. Feature rankings are derived by four feature selection techniques including impurity-based, permutation, SHAP, and Spearman's correlation. Predictive performance of the model including the important features are discussed using the evaluation metric for imbalanced datasets, Matthews correlation coefficient. Finally, the results are compared with those from reliability-based sensitivity analysis on the same example frames to show the validity of the feature selection approach. As the proposed machine learning-based approach produces the same results as the reliability-based sensitivity analysis with improved computational efficiency and accuracy, it could be extended to other structural systems.

scholarly journals Virtual Disks Performance Analysis Using Flexible I/O and Powerstat

2021 ◽  
Vol 9 (10) ◽  
pp. 267-296
Author(s):  
Pritam Patange
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Disk File ◽  
Virtual Machine Monitor ◽  
Image File ◽  
File Formats ◽  
Physical Infrastructure ◽  
Disk Image ◽  
The Impact

Abstract: Cloud computing has experienced significant growth in the recent years owing to the various advantages it provides such as 24/7 availability, quick provisioning of resources, easy scalability to name a few. Virtualization is the backbone of cloud computing. Virtual Machines (VMs) are created and executed by a software called Virtual Machine Monitor (VMM) or the hypervisor. It separates compute environments from the actual physical infrastructure. A disk image file representing a single virtual machine is created on the hypervisor’s file system. In this paper, we analysed the runtime performance of multiple different disk image file formats. The analysis comprises of four different parameters of performance namely- bandwidth, latency, input-output operations performed per second (IOPS) and power consumption. The impact of the hypervisor’s block and file sizes is also analysed for the different file formats. The paper aims to act as a reference for the reader in choosing the most appropriate disk file image format for their use case based on the performance comparisons made between different disk image file formats on two different hypervisors – KVM and VirtualBox. Keywords: Virtualization, Virtual disk formats, Cloud computing, fio, KVM, virt-manager, powerstat, VirtualBox.

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