Wang Chao, Alessandro Finamore, Lixuan Yang, Kevin Fauvel, Dario Rossi

Abstract

The recent success of Artificial Intelligence (AI) is rooted into several concomitant factors, namely theoretical progress coupled to practical availability of data and computing power. Therefore, it is not surprising that the lack of high quality data is often recognized as one of the major factors limiting AI research in several domains, and the networking domain is not excluded. Large companies have access to large data assets, that would constitute interesting benchmarks for algorithmic research in the broader scientific community. However, such datasets are private assets that are generally very difficult to share due to privacy or business sensitivity concerns.

Following numerous requests we received from the scientific community, we release AppClassNet, a commercial-grade dataset for benchmarking traffic classification and management methodologies. AppClassNet is significantly larger than the datasets generally available to the academic community in terms of both the number of samples and classes, and reaches scales similar to the popular ImageNet dataset commonly used in computer vision literature.

To avoid leak of user- and business-sensitive information, we opportunely anonymized the dataset, while empirically showing that it still represents a relevant benchmark for algorithmic research. In this paper, we describe the public dataset as well as the steps we took to avoid leakage of sensitive information while retaining relevance as a benchmark. We hope that AppClassNet can be instrumental for other researchers to address more complex commercial-grade problems in the broad field of traffic classification and management.

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Maxime Piraux, Tom Barbette, Nicolas Rybowski, Louis Navarre, Thomas Alfroy, Cristel Pelsser, François Michel, Olivier Bonaventure

Abstract

The Internet use IP addresses to identify and locate network interfaces of connected devices. IPv4 was introduced more than 40 years ago and specifies 32-bit addresses. As the Internet grew, available IPv4 addresses eventually became exhausted more than ten years ago. The IETF designed IPv6 with a much larger addressing space consisting of 128-bit addresses, pushing back the exhaustion problem much further in the future.

In this paper, we argue that this large addressing space allows reconsidering how IP addresses are used and enables improving, simplifying and scaling the Internet. By revisiting the IPv6 addressing paradigm, we demonstrate that it opens up several research opportunities that can be investigated today. Hosts can benefit from several IPv6 addresses to improve their privacy, defeat network scanning, improve the use of several mobile access networks and their mobility as well as to increase the performance of multicore servers. Network operators can solve the multihoming problem more efficiently and without putting a burden on the BGP RIB, implement Function Chaining with Segment Routing, differentiate routing inside and outside a domain given particular network metrics and offer more fine-grained multicast services.

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This July 2022 issue contains one technical paper and two editorial notes.

The technical paper, The Packet Number Space Debate in Multipath QUIC, by Quentin De Coninck, deals with how QUIC packets should be numbered over multiple paths. This work provides a comparison between the usage of a single (shared) or multiple packet space numbers for QUIC multipath. The main outcome of the evaluation is that using multiple packet number spaces has the advantage that packet losses can be detected while maintaining a significantly lower state at the receiver. Also, it allows using fewer signalling frames at the cost of a more profound modification of the QUIC protocol.

We have two editorial notes. The first one, The multiple roles that IPv6 addresses can play in today’s Internet, by Maxime Piraux and his colleagues, argues that the large IPv6 addressing space allows reconsidering how IP addresses are used and enables improving, simplifying and scaling the Internet. The second, AppClassNet: A commercial-grade dataset for application identification research by Wang Chao and his colleagues, releases a commercial-grade dataset for benchmarking traffic classification and management methodologies. AppClassNet is significantly larger than the datasets generally available to the academic community.

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