A Network Disaster Recovery Plan Framework for Academic Computing Centre

This thesis presents a network disaster recovery plan (DRP) framework for academic computing centre. Universiti Utara Malaysia Computer Centre is taken as a case study. The proposed framework consists of seven phases of disaster recovery plan which has been enhanced and improved by researcher based on past studies. The phases of the framework are risk assessment, prevention, preparedness, reaction, immediate recovery, restoration and review. The type of disaster in this study focuses on virus threats. In addition, the framework describes the virus management processes in each phases which is before, during and after virus occurs. The framework of network disaster recovery plan outlined here should provide the direction necessary for planning at any academic computing centre

E-DRP (Disaster Recovery Planning) in CIMA

This project presents a disaster recovery plan in manufacturing and industrial organizations. Cement Industries of Malaysia Berhad headquarter (CIMA HQ) is taken as a case study. The proposed strategies and recommendations of disaster recovery plan which has been enhanced and improved buy researcher based on risk assessment and business impact analysis. The type of disaster in this study focuses on fire, hardware and software failure, network failure (LAN & WAN), breakdown of air conditioner, power failure) no electrical supply and virus and malicious attacks. Disaster Recovery Planning is becoming a necessity for manufacturing and industrial organizations to support critical business software applications during a major incident or disaster that can disrupt day-to-day operations that involve the utilization of the specified software applications and to ensure business processes continue as a normal. If disaster happens, it will disrupt business operations involving the use of hardware and software. This study also describes the methods that should be taken in the planning and execution phases of disaster recovery. The proposed framework includes the operations of recovery before disaster, during disaster and the process after disaster. This recovery plan also describes the operations that need to prevent disasters such as hardware and software failure. Finally, through the research and business impact analysis, has introduced a disaster recovery model. The developed model is based on three techniques of data analysis, formulation and observation. UML is used to illustrate and visualize the model needs. The system prototype was developed with the appropriate application to the impact on business processes and manufacturing activities had been done simple and understandable way

Multi-level Analytic Network Process Model to Mitigate Supply Chain Disruptions in Disaster Recovery Planning

Over the past few decades, environmental changes have led to more frequent occurrences and greater intensities of natural disasters worldwide. In terms of globally connected supply chains, this has resulted in an enormous economical loss for corporations. Therefore, Business Continuity and Disaster Recovery (BC/DR) planning and management has become essential for businesses in order to protect their critical business flow. Yet there is a lack of systematic and transparent methodologies for companies to handle this problem. Hence, this thesis introduces a novel approach to combine consecutive steps of the Disaster Recovery Planning (DRP) process within one application. The multi-criteria decision-making (MCDM) tool called the Analytic Network Process (ANP) is employed to identify critical products of a business and match them with optimal disruption mitigation strategies based on an evaluation of benefits, opportunities, costs, and risks (BOCR). To validate the method developed in this thesis, a case study using historical data of a U.S. company (Company XYZ) is introduced. The results of the ANP mathematical modeling demonstrate that the developed methodology provides a valuable approach to analyze and confirm BC/DR planning decisions. Moreover, an expert of Company XYZ confirmed that the suggested solution established through this case study is in agreement with the preferable choice based on his expertise and professional decision-making. Further research could extend the proposed methodology to other fields of BC/DR planning, such as IT Disaster Recovery Planning or Human Disaster Relief

Resilience-Based Performance Modeling and Decision Optimization for Transportation Network

The economy and social well-being of a community heavily rely on the availability and functionality of its critical infrastructure systems, including power, water, gas, and transportation. Roadway networks are a fundamental component of transportation systems and, in the event of an extreme hazard, play a critical role during and after the event. Consequently, quantifying the performance of transportation infrastructures and optimizing decisions to mitigate, prepare for, respond to, and recover from the potential hazards. This research presented a novel resilience-based framework to support resilience planning regarding pre-disaster mitigation and post-disaster recovery. First, the author proposes a new performance metric for transportation network, weighted number of independent pathways (WIPW), integrating the network topology, redundancy level, traffic patterns, structural reliability of network components, and functionality of the network during community’s post-disaster recovery in a systematical way. To the best of our knowledge, WIPW is the only performance metric that permits risk mitigation alternatives for improving transportation network resilience to be compared on a common basis. Based on the WIPW, a decision methodology of prioritizing transportation network retrofit projects is developed. Second, our studies extend from pre-disaster mitigation to post-hazard recovery, i in which this research presents two metrics to evaluate the restoration over the horizon after disasters . That is, total recovery time and the skew of the recovery trajectory. Both metrics are involved in the multi-objective stochastic optimization problem of restoration scheduling. The metrics provided a new dimension to evaluate the relative efficiency of alternative network recovery strategies. The author then develops a restoration scheduling methodology for network post-disaster recovery that minimizes the overall network recovery time and optimizes the recovery trajectory, which ultimately will reduce economic losses due to network service disruption. The WIPW, pre-disaster mitigation, and post-disaster recovery are illustrated in the same hypothetical bridge network with 30 nodes and 37 bridges subjected to a scenario seismic event. Finally, a comprehensive stage-wise decision framework is introduced. The entire resilience planning is separated into three stages, pre-disaster mitigation, post-disaster emergency response, and long-term recovery. The WIPW is decomposed to three specific decision metrics to measure the performance of a network regarding robustness, redundancy, and recoverability, respectively. Decision support models for mitigation and recovery developed in the previous studies are revised to accommodate the stage-wise metrics. The proposed stage-wise framework is applied to a real-world roadway network of Shelby County, TN, USA subjected to seismic hazards

Toward an integrated disaster management approach: How artificial intelligence can boost disaster management

Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters

Simplicial Homology for Future Cellular Networks

Simplicial homology is a tool that provides a mathematical way to compute the connectivity and the coverage of a cellular network without any node location information. In this article, we use simplicial homology in order to not only compute the topology of a cellular network, but also to discover the clusters of nodes still with no location information. We propose three algorithms for the management of future cellular networks. The first one is a frequency auto-planning algorithm for the self-configuration of future cellular networks. It aims at minimizing the number of planned frequencies while maximizing the usage of each one. Then, our energy conservation algorithm falls into the self-optimization feature of future cellular networks. It optimizes the energy consumption of the cellular network during off-peak hours while taking into account both coverage and user traffic. Finally, we present and discuss the performance of a disaster recovery algorithm using determinantal point processes to patch coverage holes