354 research outputs found
Optimal routing on complex networks
We present a novel heuristic algorithm for routing optimization on complex
networks. Previously proposed routing optimization algorithms aim at avoiding
or reducing link overload. Our algorithm balances traffic on a network by
minimizing the maximum node betweenness with as little path lengthening as
possible, thus being useful in cases when networks are jamming due to queuing
overload. By using the resulting routing table, a network can sustain
significantly higher traffic without jamming than in the case of traditional
shortest path routing.Comment: 4 pages, 5 figure
Towards Feature-based ML-enabled Behaviour Location
Mapping behaviours to the features they relate to is a prerequisite for variability-intensive systems (VIS) reverse engineering. Manually providing this whole mapping is labour-intensive. In black-box scenarios, only execution traces are available (e.g., process mining). In our previous work, we successfully experimented with variant-based mapping using supervised machine learning (ML) to identify the variants responsible of the production of a given execution trace, and demonstrated that recurrent neural networks (RNNs) work well (above 80% accuracy) when trained on datasets in which we label execution traces with variants. However, this mapping (i) may not scale to large VIS because of combinatorial explosion and (ii) makes the internal ML representation hard to understand. In this short paper, we discuss the design of a novel approach: feature-based mapping learning
VaryMinions:Leveraging RNNs to Identify Variants in Event Logs
International audienc
The Large Scale Curvature of Networks
Understanding key structural properties of large scale networks are crucial
for analyzing and optimizing their performance, and improving their reliability
and security. Here we show that these networks possess a previously unnoticed
feature, global curvature, which we argue has a major impact on core
congestion: the load at the core of a network with N nodes scales as N^2 as
compared to N^1.5 for a flat network. We substantiate this claim through
analysis of a collection of real data networks across the globe as measured and
documented by previous researchers.Comment: 4 pages, 5 figure
Network-wide Configuration Synthesis
Computer networks are hard to manage. Given a set of high-level requirements
(e.g., reachability, security), operators have to manually figure out the
individual configuration of potentially hundreds of devices running complex
distributed protocols so that they, collectively, compute a compatible
forwarding state. Not surprisingly, operators often make mistakes which lead to
downtimes. To address this problem, we present a novel synthesis approach that
automatically computes correct network configurations that comply with the
operator's requirements. We capture the behavior of existing routers along with
the distributed protocols they run in stratified Datalog. Our key insight is to
reduce the problem of finding correct input configurations to the task of
synthesizing inputs for a stratified Datalog program. To solve this synthesis
task, we introduce a new algorithm that synthesizes inputs for stratified
Datalog programs. This algorithm is applicable beyond the domain of networks.
We leverage our synthesis algorithm to construct the first network-wide
configuration synthesis system, called SyNET, that support multiple interacting
routing protocols (OSPF and BGP) and static routes. We show that our system is
practical and can infer correct input configurations, in a reasonable amount
time, for networks of realistic size (> 50 routers) that forward packets for
multiple traffic classes.Comment: 24 Pages, short version published in CAV 201
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