Network Survivability

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# Network Survivability - PowerPoint PPT Presentation

Network Survivability. Yishi Li, Matt Willis (Mentor: Svetlana Poroseva) Summer 2005 Research Experience for Undergraduates at Florida State University School of Computational Science, Florida State University, Tallahassee, FL, USA. Definitions of Network Survivability.

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## Network Survivability

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Presentation Transcript

### Network Survivability

Yishi Li, Matt Willis

(Mentor: Svetlana Poroseva)

Summer 2005 Research Experience for Undergraduates at Florida State University

School of Computational Science, Florida State University, Tallahassee, FL, USA

Definitions of Network Survivability

Network Topology –This is a set up in which a given node has one or more links to others, and they can appear in a variety of different shapes. Topologies consist of generators (a computer, for example), vertical edges (possibly a wire connecting to the rest of the network), and horizontal edges that serve to carry information and reinforce the structure of the topology.

Definitions

Survivability. The goal of this project is to determine how network topologies react while undergoing multiple failures simultaneously. Systems respond differently when different faulty scenarios occur. The below figure illustrates three primary responses that a network would experience.

Probability of Selected Scenarios Occurs (Denotations)

Denotations: m represents the number of faults in a topology

S is the number of possible fault scenarios

SN is the number of N-scenarios,

SF of F-scenarios,

SR1 of R-scenarios in which at least one generator is destroyed

SR2 is the number of R-scenarios with all generators intact.

The Baseball Diamond

blue represents the chance of failure P(F), green represents P(R), and red P(N). The x-axis represents the number of faults occurring at a given time.

Number of faults occurring simultaneously

The Double Bus

blue represents the chance of failure P(F), green represents P(R), and red P(N). The x-axis represents the number of faults occurring at a given time.

Number of faults occurring simultaneously

Results Analysis

This graph shows the chance of failure in different topologies with two generators, two vertical edges, and a varying number of horizontal edges while undergoing two faults.

Similar improvements can be seen when vertical edges are added. The next slide shows the chance of failure at two faults in topologies with two generators and two horizontal edges.

Results Analysis Continued

By adding just one vertical edge, the chance of failure is less than half of what it was previously!!

Computational Network Survivability

Computational Network Survivability is the use of computers to generate results sufficiently to predict the outcomes of network survivability at any given scenario.

Findings and Discoveries

P(N) vs. M ( 3 G)

P(N) vs. M (4 G)

By looking at two graphs, one can see the similarity between them. Let’s look at the next one.

Findings and Discoveries

The plot illustrates how P(N) varies depending on the number of generators. It is seen that the difference in P(N) is small for a small number of faults. As M grows, the effect of the number of generators on P(N) becomes more pronounced.

Conclusions

Potential of this Research. In the future, network will become more and more essential to our lives. In order to design a reliable network, it is extremely important to understand the network performance under various conditions. Since networks are a very complex system, by combining computational science and network survivability theory, we will significantly improve our capability for analysing various forms of network topologies.

Final Discussion

New Generation of Network Design. Due to a high demand for reliable communication and power network systems, it is crucial to develop a highly survivable network that can sustain catastrophic events. Continuous research on this topic would enhance our understanding on survivability and reliability performance in different configuration. It provides essential reference for network engineers to develop systematic schemes in designing a highly reliable network.