Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 368-380 |
| Number of pages | 13 |
| Volume | 8985 |
| ISBN (Print) | 9783319316635 |
| DOIs | |
| State | Published - 2016 |
| Event | 9th International Conference on Critical Information Infrastructures Security, CRITIS 2014 - Limassol, Cyprus Duration: Oct 13 2014 → Oct 15 2014 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8985 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 9th International Conference on Critical Information Infrastructures Security, CRITIS 2014 |
|---|---|
| Country | Cyprus |
| City | Limassol |
| Period | 10/13/14 → 10/15/14 |
Fingerprint
Keywords
- Analysis
- Critical infrastructure
- Inter-dependence
- Modeling
- Multi-layer networks
- Progressive recovery
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Progressive recovery from failure in multi-layered interdependent network using a new model of interdependency. / Mazumder, Anisha; Zhou, Chenyang; Das, Arun; Sen, Arunabha.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8985 Springer Verlag, 2016. p. 368-380 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8985).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
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
UR - http://www.scopus.com/inward/record.url?scp=84962431672&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962431672&partnerID=8YFLogxK
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
VL - 8985
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 368
EP - 380
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -