TY - GEN
T1 - QoS analysis of weighted multi-state probabilistic networks via decision diagrams
AU - Terruggia, Roberta
AU - Bobbio, Andrea
PY - 2010
Y1 - 2010
N2 - Network reliability analysis is usually carried out under the simplified hypothesis that the elements of the network are binary entities that can be in one of two mutually exclusive states, perfect functioning or failed. The present paper enlarges this view from two points of view. The elements of the networks are described by multiple states that can represent a variety of different situations, like degradation levels or multiple failure modes. Furthermore, in order to increase the description power of the model, we assign to each state a weight describing a performance attribute of the element in that state. The weights may assume different physical meanings so that different Quality of Service (QoS) indicators may be evaluated. We show that the QoS analysis of a multi-state weighted probabilistic network can be performed by resorting to data structures called Multi-valued Decision Diagrams. Several examples illustrate the methodology.
AB - Network reliability analysis is usually carried out under the simplified hypothesis that the elements of the network are binary entities that can be in one of two mutually exclusive states, perfect functioning or failed. The present paper enlarges this view from two points of view. The elements of the networks are described by multiple states that can represent a variety of different situations, like degradation levels or multiple failure modes. Furthermore, in order to increase the description power of the model, we assign to each state a weight describing a performance attribute of the element in that state. The weights may assume different physical meanings so that different Quality of Service (QoS) indicators may be evaluated. We show that the QoS analysis of a multi-state weighted probabilistic network can be performed by resorting to data structures called Multi-valued Decision Diagrams. Several examples illustrate the methodology.
UR - https://www.scopus.com/pages/publications/77956584517
U2 - 10.1007/978-3-642-15651-9_4
DO - 10.1007/978-3-642-15651-9_4
M3 - Conference contribution
SN - 3642156509
SN - 9783642156502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 54
BT - Computer Safety, Reliability, and Security - 29th International Conference, SAFECOMP 2010, Proceedings
T2 - 29th International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2010
Y2 - 14 September 2010 through 17 September 2010
ER -