Optimal Redundancy Allocation for Information Technology Disaster Recovery in the Network Economy Benjamin B.M. Shao IEEE Transaction on Dependable Secure.

Slides:



Advertisements
Similar presentations
Risk Modeling The Tropos Approach PhD Lunch Meeting 07/07/2005 Yudistira Asnar –
Advertisements

Multi‑Criteria Decision Making
Planning for Change Corporate Plans
INTRODUCTION TO MODELING
A Conceptual Framework for Economic Resiliency in the Context of Resistive Economics Reza Hosnavi Reza Hosnavi, Associate professor, Malek Ashtar University.
Optimal redundancy allocation for information technology disaster recovery in the network economy Benjamin B.M. Shao IEEE Transaction on Dependable and.
Chapter 2 Linear Programming Models: Graphical and Computer Methods © 2007 Pearson Education.
Dynamic Service Composition with QoS Assurance Feb , 2009 Jing Dong UTD Farokh Bastani UTD I-Ling Yen UTD.
Decision Making: An Introduction 1. 2 Decision Making Decision Making is a process of choosing among two or more alternative courses of action for the.
Network Initiated Handovers T. Melia, J. Korhonen, R. Aguiar, S. Sreemanthula, V. Gupta Based on draft-melia-mipshop-niho-ps-00.
Defending Complex System Against External Impacts Gregory Levitin (IEC, UESTC)
Math443/543 Mathematical Modeling and Optimization
Internet Research Needs a Critical Perspective Towards Models –Sally Floyd –IMA Workshop, January 2004.
Faculty of Electrical Engineering, Technion DSN 2004 Gal Badishi Exposing and Eliminating Vulnerabilities to Denial of Service Attacks in Secure Gossip-Based.
Lesson 11 – NETWORK DISASTER RECOVERY Disaster recovery plans Network backup and restoration OVERVIEW.
Faculty of Electrical Engineering, Technion DSN 2004 Gal Badishi Exposing and Eliminating Vulnerabilities to Denial of Service Attacks in Secure Gossip-Based.
Distributed Scheduling. What is Distributed Scheduling? Scheduling: –A resource allocation problem –Often very complex set of constraints –Tied directly.
1 Cost Analysis Yale Braunstein School of Information Management & Systems.
An Approach to Case Analysis
Dynamic Islanding of Critical Infrastructures, a Suitable Strategy to Survive and Mitigate Critical Events Joint Infrastructure Interdependencies Research.
2001 South First Street Champaign, Illinois (217) Davis Power Consultants Strategic Location of Renewable Generation Based on Grid Reliability.
Helsinki University of Technology Systems Analysis Laboratory A Portfolio Model for the Allocation of Resources to Standardization Activities Antti Toppila,
Supply Contract Allocation Gyana R. Parija Bala Ramachandran IBM T.J. Watson Research Center INFORMS Miami 2001.
Tourism Port-of-Spain, Trinidad and Tobago, March 2003.
Isdefe ISXXXX XX Your best ally Panel: Future scenarios for European critical infrastructures protection Carlos Martí Sempere. Essen.
March 8, 2006  Yvo Desmedt Robust Operations Research II: Production Networks by Yvo Desmedt University College London, UK.
VTT-STUK assessment method for safety evaluation of safety-critical computer based systems - application in BE-SECBS project.
Modeling.
Why Model? By the way …. A model is a representation, abstraction, or a simulation of a phenomenon that we are trying to understand.
2008 IEEE International Conference on Technologies for Homeland Security A Modeling Framework for Evaluating Effectiveness of Smart-Infrastructure Crises.
Wetlands and Poverty Reduction Project Anglophone regional practitioners training course MODULE 3 POLICY SETTING AND ADVOCACY By Teddy Tindamanyire, Isah.
[ §3 : 1 ] 2. Life-Cycle Perspective Overview 2.1 Motivation 2.2 Waterfall Model 2.3 Requirements in Context.
University of Westminster – Y. Zetuny, G. Terstyanszky, S. Winter, P. Kacsuk Centre for Parallel Computing Cavendish School of Informatics.
Guidelines for Impact and Adaptation Assessment Design versus Implementation Issues RICHARD J.T. KLEIN POTSDAM INSTITUTE FOR CLIMATE IMPACT RESEARCH (PIK)
© K.Fedra DSS for Integrated Water Resources Management (IWRM) Problems, data, instruments DDr. Kurt Fedra ESS GmbH, Austria
Appendix C: Designing an Operations Framework to Manage Security.
Optimal Content Delivery with Network Coding Derek Leong, Tracey Ho California Institute of Technology Rebecca Cathey BAE Systems CISS 2009 March 19, 2009.
Urban Infrastructure and Its Protection Responding to the Unexpected Interest Group Report Group Members G. Giuliano (USC), Jose Holguin-Veras (CUNY),
Session 9 & 10. Definition of risk assessment and pre condition for risk assessment Establishment of clear, consistent agency objectives. Risk assessment.
Chapter 1 The Nature of Strategic Management
Optimizing NASA IV&V Benefits Using Simulation Grant Number: NAG David M. Raffo, Ph.D College of Engineering and Computer Science School of Business.
Investigating Survivability Strategies for Ultra-Large Scale (ULS) Systems Vanderbilt University Nashville, Tennessee Institute for Software Integrated.
Development of Methodologies for Independent Verification and Validation of Neural Networks NAG OSMA-F001-UNCLASS Methods and Procedures.
Formalizing the Resilience of Open Dynamic Systems Kazuhiro Minami (ISM), Tenda Okimoto (NII), Tomoya Tanjo (NII), Nicolas Schwind (NII), Hei Chan (NII),
Advanced Decision Architectures Collaborative Technology Alliance An Interactive Decision Support Architecture for Visualizing Robust Solutions in High-Risk.
Security in Mobile Ad Hoc Networks: Challenges and Solutions (IEEE Wireless Communications 2004) Hao Yang, et al. October 10 th, 2006 Jinkyu Lee.
CIA Annual Meeting LOOKING BACK…focused on the future.
The new EC impact assessment: what for? EUROPEAN TRADE UNION CONFEDERATION Sophie Dupressoir.
Matching Analyses to Decisions: Can we Ever Make Economic Evaluations Generalisable Across Jurisdictions? Mark Sculpher Mike Drummond Centre for Health.
Management Strategy Evaluation (MSE) Bob O’Boyle & Tana Worcester Bedford Institute of Oceanography Dartmouth, Nova Scotia, Canada.
10/11/20071 Business Continuity and Disaster Recovery Planning CMPE296T Fall 2007 Final Project Professor Richard Sinn Team Members Li Yang Smita Uniyal.
ENGINEERING DESIGN PROCESS. OBJECTIVES IDENTIFY THE STEPS OF THE ENGINEERING DESIGN PROCESS. DETERMINE CRITERIA FOR THE DEVELOPMENT OF A NEW TECHNOLOGY.
Urban Infrastructure and Its Protection Responding to the Unexpected Interest Group Report.
Oliver Deke Institute for World Economics (IfW) Kiel Supply side externalities in markets for genetic resources.
1 Chapter 2: Wireless LANs and PANs  Introduction  Fundamentals of WLANs  IEEE Standard  HIPERLAN Standard  Bluetooth  HomeRF.
Pouya Ostovari and Jie Wu Computer & Information Sciences
Chapter 2 Linear Programming Models: Graphical and Computer Methods
Erik Ela, Eamonn Lannoye, Bob Entriken, Aidan Tuohy
Integrated Planning of Transmission and Distribution Systems
The Value of Water Monitoring
Parallel Programming By J. H. Wang May 2, 2017.
Parallel Programming in C with MPI and OpenMP
The Extensible Tool-chain for Evaluation of Architectural Models
Strayer University at Arlington, VA
Location Analysis and Planning Chapter 8
Policy to Mitigate Effects of ENSO-Related Climate Variability
Marginal Analysis for Optimal Decision Making
Secure Proactive Recovery – a Hardware Based Mission Assurance Scheme
Dong Xuan*, Sriram Chellappan*, Xun Wang* and Shengquan Wang+
THE USA’S NEW POLICY DIRECTIVE ON NATIONAL PREPAREDNESS
Presentation transcript:

Optimal Redundancy Allocation for Information Technology Disaster Recovery in the Network Economy Benjamin B.M. Shao IEEE Transaction on Dependable Secure Computing (2005) Gun-woong Lee 1

2 Overview  Motivation  Problem: Disasters lead to the interruption of IT-supported business processes.  I dea : Allocate redundancy for IT disaster recovery planning  Academic: Lack of studies on IT-oriented disaster recovery planning and on redundancy  Practice: How to allocate IT resources for disasters preparation and how to evaluate the tradeoffs among alternative  RQ: How to allocate redundancy to IT functions against potential disasters ?  Research Model (Discrete optimization model-integer programing)  Problem : (a)Select among competing alternatives for redundancy level (b) Find the best returns  Objective FN (RAP) : Max survivability of IT solution (Min Failure prob. of solution)  Decision Variable : Selection of IT solutions  Constraints : (a) Budget limitation, (b) at least one solution is selected  Research Methods  Simulation

Findings  Effectiveness of Redundancy Allocation Model (RAP)  The scalability of the solution method  Need for a structured decision analysis of IT disaster recovery planning  Scenario based disaster recovery planning 3

4  Design as an Artifact  Model : RAP  Problem Relevance  Disaster recovery plan based on optimal redundancy allocation is attractive to many organizations  Design Evaluation  Exact solution methods based on probabilistic dynamic programming  Simulation and sensitive analysis  Contributions  Guideline for practitioners  New insight for academic research Strengthens

5  Problematic Title  Disaster recovery (?)  Emphasize proactive prevention and reactive recovery!  Network Economy (?)  Products and services are created and value is added through social network operating on large or global scales(Wikipedia) Weaknesses

6  Strong Assumptions  Seemingly fixed exogenous variables: survivability of solution (S) and importance weight of IT function (W)  Require a robust disaster assessment  Survivability of solution is only affected by a single disaster.  Combinational impact of disasters on a IT function?  e.g., Hurricane + System Hacking  Cost of a solution is only consideration for selecting a solution  But reliability or compatibility more matters! Weaknesses and Extensions

Unanswered Questions  Is this solution method also preventive or reactive to other potential disasters?  Natural (hurricane and earthquake), Technological (computer virus and Hacking), Economic (currency crises and recession), Political (Terrorism and war), and Man-made disasters  Can this solution method be applicable to other business functions (domains)? 7