Position: Research Associate

Current Institution: Pennsylvania State University

Abstract:
Stopping the disaster in interdependent networks: predicting, monitoring and recovering

Large-scale failures in data communication networks due to natural disasters such as Hurricane Katrina in 2005 can affect the communicating entities in the network and put in danger lives of people in that area. A large body of works investigates prediction of failure propagation, or design of recovery methods with the aim of stopping the disaster. Although for a single network most of the problems have already been studied, there are still many unsolved issues that should be tackled in the case of inter-dependent networks. To fill this gap, we investigate useful approaches to stop a massive disruption in interdependent networks in three different phases: predicting the propagation of failures in interdependent networks, monitoring of failures, and recovering from existing failures.

To fill this gap, we investigate useful approaches to stop a massive disruption in interdependent networks in three different phases: predicting the propagation of failures in interdependent networks, monitoring of failures, and recovering from existing failures.

A wide range of studies has been carried out to investigate the propagation of phenomena across networked systems. These works investigate the size of giant component and did not look into the evolution of propagation over time. Also, they focused on one specific model of propagation and not a general model that can incorporate different scenarios in one shot, so their approaches may be useful for that specific model of propagation. We propose a generalized model and study the propagation of failures over time. We study how initial failure is propagated among the nodes inside each network and across multiple networks for a general threshold model of propagation. Our analysis allows us to determine the most influential nodes in the propagation of failures and predict the behavior of propagation depending on the network coupling models. Therefore, a preventive approach is presented by protecting the most influential nodes of the network. Our results indicate that by making only 5% of the nodes resistant to the phenomena propagation, we may be able to stop the propagation of phenomena from one network to the other network when less than 10% of the nodes are affected.

In the second phase, we consider the problem of placing services in a telecommunication network in the presence of failures, and with the goal of failure monitoring. In contrast to existing service placement algorithms that focus on optimizing the quality of service (QoS), we consider the performance of monitoring failures from end-to-end connection states between clients and servers, and investigate service placement algorithms that optimize the monitoring performance subject to QoS constraints. Our evaluations based on real network topologies verify the effectiveness of the proposed algorithms in improving the monitoring performance compared with QoS-based service placement. Finally, we study network recovery after a massive disruption assuming that only partial knowledge of the failure area is available. The goal is to introduce optimal recovery algorithms that can reduce the number of unnecessary repairs when only partial knowledge is available after the disruption. Our initial results show that the proposed algorithms outperform the state-of-the-art recovery algorithms in the event of uncertain network failures while we can configure our choice of

Finally, we study network recovery after a massive disruption assuming that only partial knowledge of the failure area is available. The goal is to introduce optimal recovery algorithms that can reduce the number of unnecessary repairs when only partial knowledge is available after the disruption. Our initial results show that the proposed algorithms outperform the state-of-the-art recovery algorithms in the event of uncertain network failures while we can configure our choice of trade-off between the complexity and accuracy of the algorithm.

Bio:

Hana Khamfroush is a research associate at the Computer Science and Electrical Engineering Department of Penn State University. Prior to this, Hana was served as a postdoctoral scholar for one year at the computer science department of Penn State University working with Prof. Thomas La Porta. Hana received her PhD with highest distinction from University of Porto, Portugal and in collaboration with Aalborg University, Denmark in Nov 2014. She received her B.Sc. and M.Sc. degrees in Electrical engineering from Iran in 2005 and 2009, respectively. Her PhD research focused on network coding for cooperation in dynamic wireless networks. Currently at Penn State University, she is working on security of interdependent networks, and network recovery after massive disruptions. Her research interests include complex networks, communication networks, wireless communications, and mathematical modeling and analysis. Hana received a four-year scholarship from the ministry of science of Portugal for her PhD, and was awarded many travel grants and fellowships from the European Union and others. She has served on the technical program committee of IEEE ICC, IEEE PIMRC, and EW conferences, and as reviewer for many prestigious Journals and Conferences including IEEE JSAC, IEEE Transactions on Communications, and Elsevier COMNET. Hana was recently selected as the social media co-chair of N2Women community, where she initiated a series of online discussions for women in computer science to discuss gender issues.

She was invited as a qualified young researcher to participate in Heidelberg Laureate Forum (HLF) 2016.