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.

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.

Comparing Single Tier and Three Tier Infrastructure Designs against DDoS Attacks

2017 ◽  
Vol 7 (3) ◽  
pp. 59-75 ◽  
Author(s):  
Akashdeep Bhardwaj ◽  
Sam Goundar
Keyword(s):  
Data Center ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Cyber Attack ◽  
Ddos Attacks ◽  
Design Data ◽  
Application Performance ◽  
Server Virtualization ◽  
Security Officers ◽  
Cloud Environments

With the rise in cyber-attacks on cloud environments like Brute Force, Malware or Distributed Denial of Service attacks, information security officers and data center administrators have a monumental task on hand. Organizations design data center and service delivery with the aim of catering to maximize device provisioning & availability, improve application performance, ensure better server virtualization and end up securing data centers using security solutions at internet edge protection level. These security solutions prove to be largely inadequate in times of a DDoS cyber-attack. In this paper, traditional data center design is reviewed and compared to the proposed three tier data center. The resilience to withstand against DDoS attacks is measured for Real User Monitoring parameters, compared for the two infrastructure designs and the data is validated using T-Test.

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.

Comparing Single Tier and Three Tier Infrastructure Designs against DDoS Attacks

2021 ◽  
pp. 541-558
Author(s):  
Akashdeep Bhardwaj ◽  
Sam Goundar
Keyword(s):  
Data Center ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Cyber Attack ◽  
Ddos Attacks ◽  
Design Data ◽  
Application Performance ◽  
Server Virtualization ◽  
Security Officers ◽  
Cloud Environments

With the rise in cyber-attacks on cloud environments like Brute Force, Malware or Distributed Denial of Service attacks, information security officers and data center administrators have a monumental task on hand. Organizations design data center and service delivery with the aim of catering to maximize device provisioning & availability, improve application performance, ensure better server virtualization and end up securing data centers using security solutions at internet edge protection level. These security solutions prove to be largely inadequate in times of a DDoS cyber-attack. In this paper, traditional data center design is reviewed and compared to the proposed three tier data center. The resilience to withstand against DDoS attacks is measured for Real User Monitoring parameters, compared for the two infrastructure designs and the data is validated using T-Test.

FMECA Modelling and Analysis in Blood Transfusion Chain

2017 ◽  
Vol 5 (1) ◽  
pp. 23-39
Author(s):  
Francesco Zedda ◽  
Gianluca Borelli ◽  
Francesco Valentino Caredda ◽  
Alessandro Fanti ◽  
Gianluca Gatto ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Blood Transfusion ◽  
Performance Indicators ◽  
Failure Modes ◽  
Actual Process ◽  
Clinical Risk Management ◽  
Analysis Method ◽  
Rfid Technology ◽  
Enhance Patient Safety ◽  
Concise Description

This paper describes the modelling and analysis of the processes and activities used in the Blood Transfusion Centre of Hospital Brotzu (Cagliari – Italy), via FMECA (Failure Modes Effects and Criticalities Analysis) method, in order to enhance patient safety and improve clinical risk management. The first part of the study consists on an analysis of the present blood transfusion chain processes (AS-IS), obtained by reverse engineering. Then a concise description of the FMECA methodology is presented. After the introduction of the reengineered process (TO-BE), developed via introduction of RFID technology, the results of simulation will be presented. For each activity of the two configurations studied (AS-IS and TO-BE) some performance indicators were evaluated, then a sensitivity analysis has been carried out to investigate the consistency of FMECA analysis. Finally follows the comparison of results between the simulation of actual process and the reengineered one.

Comparative Modeling and Analysis of Lead-Free Solder Extrusion for the Design of Reliability

2016 ◽  
Vol 2016 (1) ◽  
pp. 000111-000116
Author(s):  
Youngtak Lee ◽  
Doug Link
Keyword(s):  
Design Of Experiments ◽  
Flip Chip ◽  
Failure Modes ◽  
Form Factors ◽  
Lead Free ◽  
Lead Free Solder ◽  
Solder Paste ◽  
Modeling And Analysis ◽  
Surface Mount ◽  
Free Solder

Abstract Due to rapid growth of the microelectronics industry, packaged devices with small form factors, low costs, high power performance, and increased efficiency have become of high demand in the market. To realize the current market development trend, flip chip interconnection and System-in-Package (SiP) are some of the promising packaging solutions developed. However, a surprising amount of surface mount technology (SMT) defects are associated with the use of lead-free solder paste and methods by which the paste is applied. Two such defects are solder extrusion and tombstoning. Through the use of design of experiments (DOE), lead-free solder defect causes can be better understood and subsequently reduced or eliminated. This paper will examine the failure modes of solder extrusion and tombstoning that occurred when two different types of lead-free solders, Sn-Ag-Cu (SAC) and BiAgX were used within a SiP for attachment of surface mount devices (SMD) chip components. The systematic investigation will include the comprehensive failure analysis of the SMD components and compare the modeling and analysis of the two different solder types utilizing the design of experiments methods.

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