Acyclic Preference Systems in P2P Networks

In this work we study preference systems natural for the Peer-to-Peer paradigm. Most of them fall in three categories: global, symmetric and complementary. All these systems share an acyclicity property. As a consequence, they admit a stable (or Pareto efficient) configuration, where no participant can collaborate with better partners than their current ones. We analyze the representation of the such preference systems and show that any acyclic system can be represented with a symmetric mark matrix. This gives a method to merge acyclic preference systems and retain the acyclicity. We also consider such properties of the corresponding collaboration graph, as clustering coefficient and diameter. In particular, studying the example of preferences based on real latency measurements, we observe that its stable configuration is a small-world graph

Are there any nicely structured preference~profiles~nearby?

We investigate the problem of deciding whether a given preference profile is close to having a certain nice structure, as for instance single-peaked, single-caved, single-crossing, value-restricted, best-restricted, worst-restricted, medium-restricted, or group-separable profiles. We measure this distance by the number of voters or alternatives that have to be deleted to make the profile a nicely structured one. Our results classify the problem variants with respect to their computational complexity, and draw a clear line between computationally tractable (polynomial-time solvable) and computationally intractable (NP-hard) questions

Cognitive constraints, contraction consistency, and the satisficing criterion

© 2007, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0

A Reduction-Based Approach Towards Scaling Up Formal Analysis of Internet Configurations

The Border Gateway Protocol (BGP) is the single inter-domain routing protocol that enables network operators within each autonomous system (AS) to influence routing decisions by independently setting local policies on route filtering and selection. This independence leads to fragile networking and makes analysis of policy configurations very complex. To aid the systematic and efficient study of the policy configuration space, this paper presents network reduction, a scalability technique for policy-based routing systems. In network reduction, we provide two types of reduction rules that transform policy configurations by merging duplicate and complementary router configurations to simplify analysis. We show that the reductions are sound, dual of each other and are locally complete. The reductions are also computationally attractive, requiring only local configuration information and modification. We have developed a prototype of network reduction and demonstrated that it is applicable on various BGP systems and enables significant savings in analysis time. In addition to making possible safety analysis on large networks that would otherwise not complete within reasonable time, network reduction is also a useful tool for discovering possible redundancies in BGP systems

Breif Announcement: A Calculus of Policy-Based Routing Systems

The BGP (Border Gateway Protocol) is the single inter-domain routing protocol that enables network operators within each autonomous system (AS) to influence routing decisions by independently setting local policies on route filtering and selection. This independence leads to fragile networking and makes analysis of policy configurations very complex. To aid the systematic and efficient study of the policy configuration space, this paper presents a reduction calculus on policy-based routing systems. In the calculus, we provide two types of reduction rules that transform policy configurations by merging duplicate and complementary router configurations to simplify analysis. We show that the reductions are sound, dual of each other and are locally complete. The reductions are also computationally attractive, requiring only local configuration information and modification. These properties establish our reduction calculus as a sound, efficient, and complete theory for scaling up existing analysis techniques