TY - GEN
T1 - Progressive recovery from failure in multi-layered interdependent network using a new model of interdependency
AU - Mazumder, Anisha
AU - Zhou, Chenyang
AU - Das, Arun
AU - Sen, Arunabha
N1 - Funding Information:
This research is supported in part by a grant from the U.S. Defense Threat Reduction Agency under grant number HDTRA1-09-1-0032 and by a grant from the U.S. Air Force Office of Scientific Research under grant number FA9550-09-1-0120
Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - A number of models have been proposed to analyze interdependent networks in recent years. However most of the models are unable to capture the complex interdependencies between such networks. To overcome the limitations, we have recently proposed a new model. Utilizing this model, we provide techniques for progressive recovery from failure. The goal of the progressive recovery problem is to maximize the system utility over the entire duration of the recovery process. We show that the problem can be solved in polynomial time in some special cases, whereas for some others, the problem is NP-complete. We provide two approximation algorithms with performance bounds of 2 and 4 respectively. We provide an optimal solution utilizing Integer Linear Programming and a heuristic. We evaluate the efficacy of our heuristic with both synthetic and real data collected from Phoenix metropolitan area. The experiments show that our heuristic almost always produces near optimal solution.
AB - A number of models have been proposed to analyze interdependent networks in recent years. However most of the models are unable to capture the complex interdependencies between such networks. To overcome the limitations, we have recently proposed a new model. Utilizing this model, we provide techniques for progressive recovery from failure. The goal of the progressive recovery problem is to maximize the system utility over the entire duration of the recovery process. We show that the problem can be solved in polynomial time in some special cases, whereas for some others, the problem is NP-complete. We provide two approximation algorithms with performance bounds of 2 and 4 respectively. We provide an optimal solution utilizing Integer Linear Programming and a heuristic. We evaluate the efficacy of our heuristic with both synthetic and real data collected from Phoenix metropolitan area. The experiments show that our heuristic almost always produces near optimal solution.
KW - Analysis
KW - Critical infrastructure
KW - Inter-dependence
KW - Modeling
KW - Multi-layer networks
KW - Progressive recovery
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U2 - 10.1007/978-3-319-31664-2_38
DO - 10.1007/978-3-319-31664-2_38
M3 - Conference contribution
AN - SCOPUS:84962431672
SN - 9783319316635
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 368
EP - 380
BT - Critical Information Infrastructures Security - 9th International Conference, CRITIS 2014, Revised Selected Papers
A2 - Panayiotou, Christos G.
A2 - Ellinas, Georgios
A2 - Kyriakides, Elias
A2 - Polycarpou, Marios M.
PB - Springer Verlag
T2 - 9th International Conference on Critical Information Infrastructures Security, CRITIS 2014
Y2 - 13 October 2014 through 15 October 2014
ER -