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

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

On the impact of aggregation on the performance of traffic aware routing

Abstract — This paper investigates the impact of traffic aggregation on the performance of routing algorithms that incorporate traffic information. The focus is on two main issues. Firstly, we explore the relationship between the average performance of the network and the level of granularity at which traffic can be assigned to routes. More specifically, we are interested in how average network performance improves as the ability of the routing protocol to split traffic arbitrarily across multiple paths increases. Secondly, we focus on the impact of traffic aggregation on short-term routing behavior. In particular, we explore the effects of traffic aggregation on traffic variability, which directly affects short-term routing performance. Our analysis is based on traffic traces collected from an operational network. The results of this study provide insights into the cost-performance trade-offs associated with deploying routing protocols that incorporate traffic awareness

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

Topology-Constrained Network Design

International audienc

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 Variability-intensive Systems’ Logs

From business processes to course management, variability-intensive software systems (VIS) are now ubiquitous. One can configure these systems’behaviour by activating options, e.g., to derive variants handling building permits across municipalities or implementing different functionalities (quizzes, forums) for a given course. These customisation facilities allow VIS to support distinct relevant customer requirements while taking advantage of reuse for common parts. Customisation thus allows realising both scope and scale economies. Behavioural differences amongst variants manifest themselves in event logs. To re-engineer this kind of system, one must know which variant(s) have produced which behaviour. Since variant information is barely present in logs, this paper supports this task by employing machine learning techniques to classify behaviours (event sequences) among variants. Specifically, we train Long Short Term Memory (LSTMs) and Gated Recurrent Units (GRUs) recurrent neural networks to relate event sequences with the variants they belong to on six different datasets issued from the configurable process and VIS domains. After having evaluated 20 different architectures of LSTM/GRU, our results demonstrate that it is possible to effectively learn the trace-to-variant mapping with high accuracy (at least 80% and up to 99%) and at scale, i.e., identifying 50 variants using 5000+ traces for each variant