Catastrophic Event Phenomena in Communication Networks: A Survey
Christopher E. Dabrowski
With the rise of the Internet, there has been increased interest in the use of computer models to study the dynamics of communication networks. An important aspect of this trend has been the study of dramatic, but relatively infrequent, events that result in abrupt and often catastrophic changes in network state. In the research literature, such catastrophic events have been commonly referred to as phase transitions. As interest in phase transitions in communication networks has grown, different approaches to the study of such phenomena have arisen. These approaches are based on differing goals of the researchers, differing investigative methods, and selection of different causal agents to study. While researchers using various approaches have made progress in understanding phase transition phenomena in communication networks, today there is only an incomplete understanding of the overall state of knowledge on this topic and no agreement on a common explanation of how such events occur in communication networks. To provide better understanding of the work done so far, this paper surveys research on phase transitions in communication networks and summarizes what has been learned. The paper identifies four different approaches taken by researchers studying this topic, describes the scope of the work done, identifies the contributions that have thus far been made, and characterizes differences in views on the nature of phase transitions in communication networks. An assessment is also made of weaknesses in the work that has been done, most notably lack of realism in network models used to date. This survey discusses characteristics of real-world communication networks that need to be included in such models to improve their realism, so that ultimately it will be possible to use such models to develop methods that can signal incipient catastrophic events in networks.
Catastrophic Event Phenomena in Communication Networks: A Survey, Computer Science Review, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=914947
(Accessed May 28, 2023)