Author Archives: Olivier Bonaventure

Thoughts on Load Distribution and the Role of Programmable Switches

James McCauley, Aurojit Panda, Arvind Krishnamurthy, Scott Shenker


The trend towards powerfully programmable network switching hardware has led to much discussion of the exciting new ways in which it can be used. In this paper, we take a step back, and examine how it should be used.

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The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research

Vaibhav Bajpai, Anna Brunstrom, Anja Feldmann, Wolfgang Kellerer , Aiko Pras, Henning Schulzrinne, Georgios Smaragdakis, Matthias Wählisch , Klaus Wehrle


Reproducibility is one of the key characteristics of good science, but hard to achieve for experimental disciplines like Internet measurements and network systems. This guide provides advice to researchers, particularly those new to the field, on designing experiments so that their work is more likely to be reproducible and to serve as a foundation for follow-on work by others.

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Open Collaborative Hyperpapers: A Call to Action

Alberto Dainotti, Ralph Holz, Mirja Kühlewind, Andra Lutu, Joel Sommers, Brian Trammell


Drawing on discussions at various venues, we envision a publishing ecosystem for Internet science, supporting publications that are self-contained, interactive, multi-level, open, and collaborative. These publications, which we dub hyperpapers, not only address issues with reproducibility and verifiability of research in Internet science and measurement, but have the potential to increase the impact of our work and change how collaborations work in the field. This note announces initial experiments with Internet measurement hyperpapers with the help of common, tested technologies in data science and software development, and is a call to action to others to come build out this vision with us.

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The October 2018 issue

SIGCOMM’18 was held in Hungary and the participants were pleased with both the technical program and the social interactions with all the members of the community. During her keynote on Networks Capable of Change, Jennifer Rexford mentioned two papers published in CCR that have had an important impact on our community : OpenFlow: enabling innovation in campus networks and P4: programming protocol- independent packet processors. These two editorial notes paved the way for Software-Defined Networks and then programmable switches. They are a good illustration of the benefits of having a venue like CCR that is open to other types of papers than the scientific papers that usually appear in our conferences and workshops.

This issue contains three regular papers, two editorial notes and the best papers of five recent SIGCOMM’18 workshops. Our first paper, On Max-min Fair Allocation for Multi-source Transmission, co-authored by G. Li et al., proposes and evaluates an algorithm to provide max-min fairness in a network where the same information can be downloaded from multiple sources. In On Collaborative Predictive Blacklisting, Luca Malis and his colleagues study collaborative predictive blacklisting (CPB) wherein differ- ent organizations share information about attacks in real time and use it to update their blacklists. Finally, in Bootstrapping Privacy Services in Today’s Internet, T. Lee et al. propose and analyse different services that could be provided by Internet Service Providers to provide better privacy to their users.

The two editorial notes published in this issue are very different. In Toward Demand- Aware Networking: A Theory for Self- Adjusting Network, C. Avin and S. Schmid propose to initiate the study of the theory of demand-aware, self-adjusting networks. In The 10th Workshop on Active Internet Measurements (AIMS-10) Report, kc Claffy and David Clark report the lessons learned from a recent workshop.

This issue also contains the best pa- pers selected by the organizers of five SIGCOMM’18 workshops:

• Learning IP Network Representations presented by M. Li et al. at BIG- DAMA’18

• Measuring the Impact of a Success- ful DDoS Attack on the Customer Behaviour of Managed DNS Service Providers presented by A. Abhista et al. at the workshop on traffic measure- ments for Cybersecurity

• Making Content Caching Policies ’Smart’ using the DeepCache Framework presented by A. Narayanan et al. at NetAI’18

• Refining Network Intents for Self- Driving Networks presented by A. Jacobs et al. at SelfDN’18

• A Formally Verified NAT Stack presented by S. Pirelli et al. at KBNets’18

The composition of the Editorial Board has also been modified recently. After several years of active participation, Costin Raiciu, Fahad Dogar, Alberto Dainotti and David Choffnes have concluded their term. I would like to thank them on behalf of the authors of all the papers that they handled during the last years. Sergey Gorinsky (IMDEA Networks, Spain) has agreed to join the Editorial board.

I hope that you will enjoy reading this new issue and welcome comments and suggestions on CCR Online (https: // or by email at ccr-editor at

Olivier Bonaventure

CCR Editor

On Max-min Fair Allocation for Multi-source Transmission

Geng Li, Yichen Qian, Y. Richard Yang


Max-min fair is widely used in network traffic engineering to allocate available resources among different traffic transfers. Recently, as data replication technique developed, increasing systems enforce multi-source transmission to maximize network utilization. However, existing TE approaches fail to deal with multi-source transfers because the optimization becomes a joint problem of bandwidth allocation as well as flow assignment among different sources. In this paper, we present a novel allocation approach for multi-source transfers to achieve global max-min fairness. The joint bandwidth allocation and flow assignment optimization problem poses a major challenge due to nonlinearity and multiple objectives. We cope with this by deriving a novel transformation with simple equivalent canonical linear programming to achieve global optimality efficiently. We conduct data-driven simulations, showing that our approach is more max-min fair than other single-source and multi-source allocation approaches, meanwhile it outperforms others with substantial gains in terms of network throughput and transfer completion time.

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On Collaborative Predictive Blacklisting

Luca Melis, Apostolos Pyrgelis, Emiliano De Cristofaro


Collaborative predictive blacklisting (CPB) allows to forecast future attack sources based on logs and alerts contributed by multiple organizations. Unfortunately, however, research on CPB has only focused on increasing the number of predicted attacks but has not considered the impact on false positives and false negatives. Moreover, sharing alerts is often hindered by confidentiality, trust, and liability issues, which motivates the need for privacy-preserving approaches to the problem. In this paper, we present a measurement study of state-of-the-art CPB techniques, aiming to shed light on the actual impact of collaboration. To this end, we reproduce and measure two systems: a non privacy-friendly one that uses a trusted coordinating party with access to all alerts [12] and a peer-to-peer one using privacy-preserving data sharing [8]. We show that, while collaboration boosts the number of predicted attacks, it also yields high false positives, ultimately leading to poor accuracy. This motivates us to present a hybrid approach, using a semi-trusted central entity, aiming to increase utility from collaboration while, at the same time, limiting information disclosure and false positives. This leads to a better trade-off of true and false positive rates, while at the same time addressing privacy concerns.

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Bootstrapping Privacy Services in Today’s Internet

Taeho Lee, Christos Pappas, Adrian Perrig


Internet users today have few solutions to cover a large space of diverse privacy requirements. We introduce the concept of privacy domains, which provide flexibility in expressing users’ privacy requirements. Then, we propose three privacy services that construct meaningful privacy domains and can be offered by ISPs. Furthermore, we illustrate that these services introduce little overhead for communication sessions and that they come with a low deployment barrier for ISPs.

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Toward Demand-Aware Networking: A Theory for Self-Adjusting Networks

Chen Avin, Stefan Schmid


The physical topology is emerging as the next frontier in an ongoing effort to render communication networks more flexible. While first empirical results indicate that these flexibilities can be exploited to reconfigure and optimize the network toward the workload it serves and, e.g., providing the same bandwidth at lower infrastructure cost, only little is known today about the fundamental algorithmic problems underlying the design of reconfigurable networks. This paper initiates the study of the theory of demand-aware, self-adjusting networks. Our main position is that self-adjusting networks should be seen through the lense of self-adjusting datastructures. Accordingly, we present a taxonomy classifying the different algorithmic models of demand-oblivious, fixed demand-aware, and reconfigurable demand-aware networks, introduce a formal model, and identify objectives and evaluation metrics.We also demonstrate, by examples, the inherent advantage of demand-aware networks over state-of-the-art demand-oblivious, fixed networks (such as expanders). We conclude by observing that the usefulness of self-adjusting networks depends on the spatial and temporal locality of the demand; as relevant data is scarce, we call for community action.

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The 10th Workshop on Active Internet Measurements (AIMS-10) Report

kc Claffy, David Clark


On 13-15 March 2018, CAIDA hosted its tenth Workshop on Active Internet Measurements (AIMS-10). This workshop series provides a forum for stakeholders in Internet active measurement projects to communicate their interests and concerns, and explore cooperative approaches to maximizing the collective benefit of deployed infrastructure and gathered data. An overarching theme this year was how to inform new legislation of communications policy in the U.S. Given the continued limited insight into Internet operations by researchers and policymakers, we tried to focus these discussions on what data is or could be measured to shape and support current and emerging policy debates. Materials related to the workshop are at

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Learning IP Network Representations

Mingda Li, Cristian Lumezanu, Bo Zong, Haifeng Chen


We present DIP, a deep learning based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet. In contrast, DIP computes low-dimensional representations of nodes that preserve structural properties and non-linear relationships across multiple, heterogeneous sources of structural information, such as IP, routing, and distance information. Using a large real-world data set, we show that DIP learns representations that preserve the real-world clustering of the associated nodes and predicts distance between them more than 30% better than a meanbased approach. Furthermore, DIP accurately imputes hop count distance to unknown hosts (i.e., not used in training) given only their IP addresses and routable prefixes. Our framework is extensible to new data sources and applicable to a wide range of problems in network monitoring and security

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