Author Archives: Olivier Bonaventure

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

Chen Avin, Stefan Schmid

Abstract

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

Abstract

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 http://www.caida.org/workshops/aims/1803/.

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Learning IP Network Representations

Mingda Li, Cristian Lumezanu, Bo Zong, Haifeng Chen

Abstract

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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Refining Network Intents for Self-Driving Networks

Arthur Selle Jacobs, Ricardo José Pfitscher , Ronaldo Alves Ferreira, Lisandro Zambenedetti Granville

Abstract

Recent advances in artificial intelligence (AI) offer an opportunity for the adoption of self-driving networks. However, network operators or home-network users still do not have the right tools to exploit these new advancements in AI, since they have to rely on low-level languages to specify network policies. Intent-based networking (IBN) allows operators to specify high-level policies that dictate how the network should behave without worrying how they are translated into configuration commands in the network devices. However, the existing research proposals for IBN fail to exploit the knowledge and feedback from the network operator to validate or improve the translation of intents. In this paper, we introduce a novel intent-refinement process that uses machine learning and feedback from the operator to translate the operator’s utterances into network configurations. Our refinement process uses a sequence-to-sequence learning model to extract intents from natural language and the feedback from the operator to improve learning. The key insight of our process is an intermediate representation that resembles natural language that is suitable to collect feedback from the operator but is structured enough to facilitate precise translations. Our prototype interacts with a network operator using natural language and translates the operator input to the intermediate representation before translating to SDN rules. Our experimental results show that our process achieves a correlation coefficient squared (i.e., R-squared of 0.99 for a dataset with 5000 entries and the operator feedback significantly improves the accuracy of our model.

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Making Content Caching Policies ‘Smart’ using the DeepCache Framework

Arvind Narayanan, Saurabh Verma, Eman Ramadan, Pariya Babaie, Zhi-Li Zhang

Abstract

In this paper, we present DeepCache a novel framework for content caching, which can significantly boost cache performance. Our framework is based on powerful deep recurrent neural network models. It comprises of two main components: i) Object Characteristics Predictor, which builds upon deep LSTM Encoder-Decoder model to predict the future characteristics of an object (such as object popularity) — to the best of our knowledge, we are the first to propose LSTM Encoder-Decoder model for content caching; ii) a caching policy component, which accounts for predicted information of objects to make smart caching decisions. In our thorough experiments, we show that applying DeepCache Framework to existing cache policies, such as LRU and k-LRU, significantly boosts the number of cache hits.

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Measuring the Impact of a Successful DDoS Attack on the Customer Behaviour of Managed DNS Service Providers

Abhishta Abhishta, Roland van Rijswijk-Deij
and Lambert J. M. Nieuwenhuis
Abstract

Distributed Denial-of-Service (DDoS) attacks continue to pose a serious threat to the availability of Internet services. The Domain Name System (DNS) is part of the core of the Internet and a crucial factor in the successful delivery of Internet services. Because of the importance of DNS, specialist service providers have sprung up in the market, that provide managed DNS services. One of their key selling points is that they protect DNS for a domain against DDoS attacks. But what if such a service becomes the target of a DDoS attack, and that attack succeeds?

In this paper we analyse two such events, an attack on NS1 in May 2016, and an attack on Dyn in October 2016. We do this by analysing the change in the behaviour of the service’s customers. For our analysis we leverage data from the OpenINTEL active DNS measurement system, which covers large parts of the global DNS over time. Our results show an almost immediate and statistically significant change in the behaviour of domains that use NS1 or Dyn as a DNS service provider. We observe a decline in the number of domains that exclusively use NS1 or Dyn as a managed DNS service provider, and see a shift toward risk spreading by using multiple providers. While a large managed DNS provider may be better equipped to protect against attacks, these two case studies show they are not impervious to them. This calls into question the wisdom of using a single provider for managed DNS. Our results show that spreading risk by using multiple providers is an effective countermeasure, albeit probably at a higher cost.

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A Formally Verified NAT Stack

Solal Pirelli, Arseniy Zaostrovnykh, George Candea
Abstract

Prior work proved a stateful NAT network function to be semantically correct, crash-free, and memory safe. Their toolchain verifies the network function code while assuming the underlying kernel-bypass framework, drivers, operating system, and hardware to be correct. We extend the toolchain to verify the kernel-bypass framework and a NIC driver in the context of the NAT. We uncover bugs in both the framework and the driver. Our code is publicly available.

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The July 2018 issue

In May, the CCR Editorial board selects the two best papers that were published in the four previous issues (i.e. July 2017, October 2017, January 2018 and April 2018). For 2018, two measurement papers were chosen:

These two papers will be presented during the CCR session at SIGCOMM’18. Both papers have proposed a methodology, collected measurements and released artifacts to allow other researchers to reproduce and extend the paper results. CCR continues to encourage papers to release their artifacts by allowing them to be longer than six pages. SIGCOMM will do one further step to encourage the release of paper artifacts by the creation of an Artifacts Evaluation Committe that will organise the evaluation of the artifacts associated with papers accepted in CCR and the SIGCOMM sponsored conferences in 2018. The final details are still being discussed. They will be announced during SIGCOMM’18 and posted on https://www.sigcomm.org.

This issue starts with three technical articles. In Accelerating Network Measurement in Software, Y. Zhou, O. Alipourfard, M. Yu and T. Yang propose a new technique that leverages caching to improve network measurement software. They release the software
developed for the paper at https://github.com/zhouyangpkuer/Agg-Evict.

Our second technical paper looks at the BGP peerings and more precisely those maintained by the so called Hypergiants, i.e. the larget content providers and CDNs. T. Bottger, F. Cuadrado and S. Uhlig analyse in Looking for Hypergiants in PeeringDB the interconnections of those networks from IXP data. The authors also release the code and the dataset used to write their paper.

The third technical paper of this issue fo- cuses on the Domain Name System. R. AlDalky, M. Rabinovich and M. Allman propose and evaluate in Practical Challenge-Response for DNS a new technique that relies on challenge-responses to validate the authenticity of DNS requests.

In addition to the technical papers, this issue also contains three editorial notes. In Mosaic5G: Agile and Flexible Service Platforms for 5G Research, N. Nikaein, C. Chang and K. Alexandris describe Mosaic5G, an open-source software platform that can be used to create 5G networks. Given the buzz around 5G networks, I expect that many researchers will be interested by this platform. In NDN Host Model, H. Zhang, Y. Li, Z. Zhang, A. Afanasyev and L. Zhang discuss how the traditionnal host model must be reconsidered with Named Data Networking (NDN). Finally, KC Claffy, G. Huston and D. Clark summarise in Workshop on Internet Economics (WIE2017) Final Report the conclusions of a recent workshop that they organised.

I hope that you will enjoy reading this new issue and welcome comments and suggestions on CCR Online  or by email at ccr-editor at sigcomm.org.

Olivier Bonaventure

CCR Editor

Accelerating Network Measurement in Software

Yang ZhouOmid Alipourfard, Minlan YuTong Yang
Abstract

Network measurement plays an important role for many network functions such as detecting network anomalies and identifying big flows. However, most existing measurement solutions fail to achieve high performance in software as they often incorporate heavy computations and a large number of random memory accesses. We present Agg-Evict, a generic framework for accelerating network measurement in software. Agg-Evict aggregates the incoming packets on the same flows and sends them as a batch, reducing the number of computations and random memory accesses in the subsequent measurement solutions. We perform extensive experiments on top of DPDK with 10G NIC and observe that almost all the tested measurement solutions under Agg-Evict can achieve 14.88 Mpps throughput and see up to 5.7× lower average processing latency per packet.

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Looking for Hypergiants in PeeringDB

Timm Böttger, Felix Cuadrado, Steve Uhlig
Abstract

Hypergiants, such as Google or Netflix, are important organisations in the Internet ecosystem, due to their sheer impact in terms of traffic volume exchanged. However, the research community still lacks a sufficiently crisp definition for them, beyond naming specific instances of them. In this paper we analyse PeeringDB data and identify features that differentiate hypergiants from the other organisations. To this end, we first characterise the organisations present in PeeringDB, allowing us to identify discriminating properties of these organisations. We then use these properties to separate the data in two clusters, differentiating hypergiants from other organisations. We conclude this paper by investigating how hypergiants and other organisations exploit the IXP ecosystem to reach the global IPv4 space.

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