12 research outputs found
A new design principle of robust onion-like networks self-organized in growth
Today's economy, production activity, and our life are sustained by social
and technological network infrastructures, while new threats of network attacks
by destructing loops have been found recently in network science. We inversely
take into account the weakness, and propose a new design principle for
incrementally growing robust networks. The networks are self-organized by
enhancing interwoven long loops. In particular, we consider the range-limited
approximation of linking by intermediations in a few hops, and show the strong
robustness in the growth without degrading efficiency of paths. Moreover, we
demonstrate that the tolerance of connectivity is reformable even from
extremely vulnerable real networks according to our proposed growing process
with some investment. These results may indicate a prospective direction to the
future growth of our network infrastructures.Comment: 21 pages, 10 figures, 1 tabl
DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning
The increasing reliance upon cloud services entails more flexible networks
that are realized by virtualized network equipment and functions. When such
advanced network systems face a massive failure by natural disasters or
attacks, the recovery of the entire system may be conducted in a progressive
way due to limited repair resources. The prioritization of network equipment in
the recovery phase influences the interim computation and communication
capability of systems, since the systems are operated under partial
functionality. Hence, finding the best recovery order is a critical problem,
which is further complicated by virtualization due to dependency among network
nodes and layers. This paper deals with a progressive recovery problem under
limited resources in networks with VNFs, where some dependent network layers
exist. We prove the NP-hardness of the progressive recovery problem and
approach the optimum solution by introducing DeepPR, a progressive recovery
technique based on Deep Reinforcement Learning (Deep RL). Our simulation
results indicate that DeepPR can achieve the near-optimal solutions in certain
networks and is more robust to adversarial failures, compared to a baseline
heuristic algorithm.Comment: Technical Report, 12 page
More Tolerant Reconstructed Networks by Self-Healing against Attacks in Saving Resource
Complex network infrastructure systems for power-supply, communication, and
transportation support our economical and social activities, however they are
extremely vulnerable against the frequently increasing large disasters or
attacks. Thus, a reconstructing from damaged network is rather advisable than
empirically performed recovering to the original vulnerable one. In order to
reconstruct a sustainable network, we focus on enhancing loops so as not to be
trees as possible by node removals. Although this optimization is corresponded
to an intractable combinatorial problem, we propose self-healing methods based
on enhancing loops in applying an approximate calculation inspired from a
statistical physics approach. We show that both higher robustness and
efficiency are obtained in our proposed methods with saving the resource of
links and ports than ones in the conventional healing methods. Moreover, the
reconstructed network by healing can become more tolerant than the original one
before attacks, when some extent of damaged links are reusable or compensated
as investment of resource. These results will be open up the potential of
network reconstruction by self-healing with adaptive capacity in the meaning of
resilience.Comment: 23 pages, 6 figure
Mitigation of cascading failures in complex networks
Cascading failures in many systems such as infrastructures or financial networks can lead to catastrophic system collapse. We develop here an intuitive, powerful and simple-to-implement approach for mitigation of cascading failures on complex networks based on local network structure. We offer an algorithm to select critical nodes, the protection of which ensures better survival of the network. We demonstrate the strength of our approach compared to various standard mitigation techniques. We show the efficacy of our method on various network structures and failure mechanisms, and finally demonstrate its merit on an example of a real network of financial holdings.Published versio