Joint dimensioning of server and network infrastructure for resilient optical grids/clouds

We address the dimensioning of infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We design the resulting grid/cloud to be resilient against network link or server failures. To this end, we exploit relocation: Under failure conditions, a grid job or cloud virtual machine may be served at an alternate destination (i.e., different from the one under failure-free conditions). We thus consider grid/cloud requests to have a known origin, but assume a degree of freedom as to where they end up being served, which is the case for grid applications of the bag-of-tasks (BoT) type or hosted virtual machines in the cloud case. We present a generic methodology based on integer linear programming (ILP) that: 1) chooses a given number of sites in a given network topology where to install server infrastructure; and 2) determines the amount of both network and server capacity to cater for both the failure-free scenario and failures of links or nodes. For the latter, we consider either failure-independent (FID) or failure-dependent (FD) recovery. Case studies on European-scale networks show that relocation allows considerable reduction of the total amount of network and server resources, especially in sparse topologies and for higher numbers of server sites. Adopting a failure-dependent backup routing strategy does lead to lower resource dimensions, but only when we adopt relocation (especially for a high number of server sites): Without exploiting relocation, potential savings of FD versus FID are not meaningful

Recovery Model for Survivable System through Resource Reconfiguration

A survivable system is able to fulfil its mission in a timely manner, in the presence of attacks, failures, or accidents. It has been realized that it is not always possible to anticipate every type of attack or failure or accident in a system, and to predict and protect against those threats. Consequently, recovering back from any damage caused by threats becomes an important attention to be taken into account. This research proposed another recovery model to enhance system survivability. The model focuses on how to preserve the system and resume its critical service while incident occurs by reconfiguring the damaged critical service resources based on available resources without affecting the stability and functioning of the system. There are three critical requisite conditions in this recovery model: the number of pre-empted non-critical service resources, the response time of resource allocation, and the cost of reconfiguration, which are used in some scenarios to find and re-allocate the available resource for the reconfiguration. A brief specifications using Z language are also explored as a preliminary proof before the implementation .. To validate the viability of the approach, two instance cases studies of real-time system, delivery units of post office and computer system of a company, are provided in ensuring the durative running of critical service. The adoption of fault-tolerance and survivability using redundancy re-allocation in this recovery model is discussed from a new perspective. Compared to the closest work done by other researchers, it is shown that the model can solve not only single fault and can reconfigure the damage resource with minimum disruption to other services

Survivability modeling for cyber-physical systems subject to data corruption

Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii

Quantitative dependability and interdependency models for large-scale cyber-physical systems

Cyber-physical systems link cyber infrastructure with physical processes through an integrated network of physical components, sensors, actuators, and computers that are interconnected by communication links. Modern critical infrastructures such as smart grids, intelligent water distribution networks, and intelligent transportation systems are prominent examples of cyber-physical systems. Developed countries are entirely reliant on these critical infrastructures, hence the need for rigorous assessment of the trustworthiness of these systems. The objective of this research is quantitative modeling of dependability attributes -- including reliability and survivability -- of cyber-physical systems, with domain-specific case studies on smart grids and intelligent water distribution networks. To this end, we make the following research contributions: i) quantifying, in terms of loss of reliability and survivability, the effect of introducing computing and communication technologies; and ii) identifying and quantifying interdependencies in cyber-physical systems and investigating their effect on fault propagation paths and degradation of dependability attributes. Our proposed approach relies on observation of system behavior in response to disruptive events. We utilize a Markovian technique to formalize a unified reliability model. For survivability evaluation, we capture temporal changes to a service index chosen to represent the extent of functionality retained. In modeling of interdependency, we apply correlation and causation analyses to identify links and use graph-theoretical metrics for quantifying them. The metrics and models we propose can be instrumental in guiding investments in fortification of and failure mitigation for critical infrastructures. To verify the success of our proposed approach in meeting these goals, we introduce a failure prediction tool capable of identifying system components that are prone to failure as a result of a specific disruptive event. Our prediction tool can enable timely preventative actions and mitigate the consequences of accidental failures and malicious attacks --Abstract, page iii

Quantifying Influence of Strategies and Network Properties in Repairing Simultaneous Failures in Smart Grid

The behavior of networks under simultaneous failures has beensubject to various studies in the field of network science. However, themeasures used do usually not take into account the peculiarities of thestudied network. In this paper, we introduce a new measure for powergrids based on the balancing of power and on the accumulated cost ofenergy not supplied (CENS) during an outage. With the help of thismeasure we quantify the performance of seven repair strategies. We findthat both the choice of the right strategy and the topology of the powergrid has a major influence on the outage cost and the survivability ofthe power grid. Additionally, we appraise the potential of smart gridservices and conclude that both distributed energy resources (DER) anddemand response (DR) has a large potential to reduce the cost of anoutage

Recovery Model for Survivable System through Resource Reconfiguration

A survivable system is able to fulfil its mission in a timely manner, in the presence of attacks, failures, or accidents. It has been realized that it is not always possible to anticipate every type of attack or failure or accident in a system, and to predict and protect against those threats. Consequently, recovering back from any damage caused by threats becomes an important attention to be taken into account. This research proposed another recovery model to enhance system survivability. The model focuses on how to preserve the system and resume its critical service while incident occurs by reconfiguring the damaged critical service resources based on available resources without affecting the stability and functioning of the system. There are three critical requisite conditions in this recovery model: the number of pre-empted non-critical service resources, the response time of resource allocation, and the cost of reconfiguration, which are used in some scenarios to find and re-allocate the available resource for the reconfiguration. A brief specifications using Z language are also explored as a preliminary proof before the implementation .. To validate the viability of the approach, two instance cases studies of real-time system, delivery units of post office and computer system of a company, are provided in ensuring the durative running of critical service. The adoption of fault-tolerance and survivability using redundancy re-allocation in this recovery model is discussed from a new perspective. Compared to the closest work done by other researchers, it is shown that the model can solve not only single fault and can reconfigure the damage resource with minimum disruption to other services