arXiv subjects

Orkun İrsoy

Publications and source records attributed to Orkun İrsoy.

3 recordsLinked to original sources

arXiv2026

Improving the Robustness of the XRP Ledger Network via Edge Augmentation Strategies

The XRP Ledger allows its network participants to select a set of trusted peers within the network (i.e., the Unique Node List (UNL)) and communicate with them to reach consensus on which transactions should be included in the next ledger state. However, its consensus protocol requires significant overlap among participants' UNLs, along with a high agreement threshold among the nodes within each UNL (e.g., 80\%). Consequently, an attacker could disrupt the consensus process in such a network by targeting the nodes that form the network's connectivity backbone and reducing the number of trusted participants that can communicate with one another below the required threshold. In this paper, we evaluate strategies to improve the robustness of the XRP Ledger's existing topology, as measured by our formal definitions of quorum and network robustness, and compare them to a second strategy from prior work. The strategy we present is an addition/augmentation approach, in which new edges are added based on different constructions. The second strategy is a rewiring or edge-replacement approach, in which the overall number of edges is preserved but they are rearranged. For each strategy, we consider two different cases: one in which all nodes participate in the edge construction or rewiring process, and another in which only a subset of nodes participates. Our findings demonstrate substantial improvements in robustness when augmentation strategies are used over the default XRP Ledger topology and show that some augmentation strategies achieve robustness metrics equal to or exceeding the rewiring strategy, even when the number of edges added is small (e.g., three edges per node). Additionally, we show that the random K-out-based augmentation strategy maintains higher topological similarity to the original network than rewiring, as measured by Jaccard similarity.

cs.CR↗

arXiv2026

Overload-Based Cascades in Multiplex Flow Networks with Partial Functionality

Cascading failures driven by load or flow redistribution arise in networked systems such as power grids, supply chains, and cloud computing centers. Most flow-network models assume that a node either functions or fails as a whole. In many real systems, however, a node supports several distinct flows that share node-level resources, and failure in one of them does not necessarily imply failure in the others. We study this setting through multiplex flow networks with partial functionality, where a node can remain operational in some functionalities while failing in others. A heavy load on one functionality reduces the capacity available to the others, as quantified by cross-layer influence factors. When a node fails in one layer, its load is redistributed among surviving nodes in that layer, while the node may continue to operate in the others. Using mean-field analysis, we derive recursive equations for the final system sizes, namely the fraction of surviving nodes in each layer after the cascade stops. We validate the analysis through simulations for several load-capacity distributions. We then examine key features of the cascade dynamics, including non-monotone robustness curves, different cascade-outcome regimes, and their relation with cross-layer influence. We map the outcomes to distinct steady-state regimes, including single-layer survival phases absent in joint-functionality models, and show that partial functionality can increase robustness relative to the joint-functionality case. Finally, we study robustness maximization under a fixed total capacity budget by comparing several capacity allocation strategies. We propose a strategy that combines cross-layer influence with local neighborhood information on load and degree, and show that it gives the strongest robustness performance across the configurations considered.

eess.SY↗

arXiv2025

Analysis and Optimization of Robustness in Multiplex Flow Networks Against Cascading Failures

Networked systems are susceptible to cascading failures, where the failure of an initial set of nodes propagates through the network, often leading to system-wide failures. In this work, we propose a multiplex flow network model to study robustness against cascading failures triggered by random failures. The model is inspired by systems where nodes carry or support multiple types of flows, and failures result in the redistribution of flows within the same layer rather than between layers. To represent different types of interdependencies between the layers of the multiplex network, we define two cases of failure conditions: layer-independent overload and layer-influenced overload. We provide recursive equations and their solutions to calculate the steady-state fraction of surviving nodes, validate them through a set of simulation experiments, and discuss optimal load-capacity allocation strategies. Our results demonstrate that allocating the total excess capacity to each layer proportional to the mean effective load in the layer and distributing that excess capacity equally among the nodes within the layer ensures maximum robustness. The proposed framework for different failure conditions allows us to analyze the two overload conditions presented and can be extended to explore more complex interdependent relationships.

eess.SY↗