Fluctuation-driven capacity distribution in complex networks

Abstract

Maximizing robustness and minimizing cost are common objectives in the design of infrastructure networks. However, most infrastructure networks evolve and operate in a highly decentralized fashion, which may significantly impact the allocation of resources across the system. Here, we investigate this question by focusing on the relation between capacity and load in different types of real-world communication and transportation networks. We find strong empirical evidence that the actual capacity of the network elements tends to be similar to the maximum available capacity, if the cost is not strongly constraining. As more weight is given to the cost, however, the capacity approaches the load nonlinearly. In particular, all systems analyzed show larger unoccupied portions of the capacities on network elements subjected to smaller loads, which is in sharp contrast with the assumptions involved in (linear) models proposed in previous theoretical studies. We describe the observed behavior of the capacity-load relation as a function of the relative importance of the cost by using a model that optimizes capacities to cope with network traffic fluctuations. These results suggest that infrastructure systems have evolved under pressure to minimize local failures, but not necessarily global failures that can be caused by the spread of local damage through cascading processes.

References in corpus (34)

• Error and attack tolerance of complex networks
• The architecture of complex weighted networks
• Who is the best connected scientist? A study of scientific coauthorship networks
• Network robustness and fragility: Percolation on random graphs
• Resilience of the Internet to random breakdowns
• Cascade-based attacks on complex networks
• The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles
• Breakdown of the Internet under intentional attack
• Structural Vulnerability of the North American Power Grid
• A model for cascading failures in complex networks
• Cascade control and defense in complex networks
• Modeling Cascading Failures in the North American Power Grid
• Weighted evolving networks: coupling topology and weights dynamics
• Weighted Evolving Networks
• Communication in networks with hierarchical branching
• Classification of scale-free networks
• Stability and topology of scale-free networks under attack and defense strategies
• Robustness of the European power grids under intentional attack
• Fluctuations in network dynamics
• Vertex overload breakdown in evolving networks
• Congestion and centrality in traffic flow on complex networks
• Critical load and congestion instabilities in scale-free networks
• Communication Bottlenecks in Scale-Free Networks
• Separating internal and external dynamics of complex systems
• Scale-free trees: the skeletons of complex networks
• On the universality of the scaling of fluctuations in traffic on complex networks
• A High Robustness and Low Cost Model for Cascading Failures
• Efficient Response to Cascading Disaster Spreading
• A limited resource model of fault-tolerant capability against cascading failure of complex network
• Robustness of the avalanche dynamics in data packet transport on scale-free networks
• Universality Class of Fiber Bundle Model on Complex Networks
• Complete Condensation in a Zero Range Process on Scale-Free Networks
• Extreme fluctuations in noisy task-completion landscapes on scale-free networks
• Optimal spatial transportation networks where link-costs are sublinear in link-capacity

Cited by in corpus (9)

• Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications
• Power grids vulnerability: a complex network approach
• Efficient routing strategies in scale-free networks with limited bandwidth
• Collectively optimal routing for congested traffic limited by link capacity
• Protecting infrastructure networks from cost-based attacks
• Robustness of flow networks against cascading failures under partial load redistribution
• Manipulation of extreme events on scale-free networks
• Integrating fluctuations into distribution of resources in transportation networks
• Study of dynamic and static routing for improvement of the transportation efficiency on small complex networks