Lessons from Giant-Scale Services
IEEE Internet Computing
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
Fundamentals of Wireless Sensor Networks: Theory and Practice
Fundamentals of Wireless Sensor Networks: Theory and Practice
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The power of networks manifests itself in a highly non-linear amplification of a number of effects, and their weakness---in propagation of cascading failures. The potential systemic risk effects can be either exacerbated or mitigated, depending on the resilience characteristics of the network. The goals of this paper are to study some characteristics of network amplification and resilience. We simulate random Erdős--Rényi networks and measure amplification by varying node capacity, transaction volume, and expected failure rates. We discover that network throughput scales almost quadratically with respect to the node capacity and that the effects of excessive network load and random and irreparable node faults are equivalent and almost perfectly anticor-related. This knowledge can be used by capacity planners to determine optimal reliability requirements that maximize the optimal operational regions.