Research Internship — Network Traffic Compression, towards Sustainable Networks

Publication date : Oct 05, 2026, 5:25PM

Currently, a large fraction of network traffic is sent as-is: headers transmitted in the clear, payloads encrypted but not compressed. Compressing such traffic is an opportunity to reduce network resource usage, with a direct environmental benefit: less bandwidth and energy for the same service.
Network traffic compression has regained interest in recent months, with the IETF and 3GPP investigating the integration of Static Context Header Compression (SCHC) — a compression mechanism developed at the IETF — into 6G. This raises several questions about the performance of SCHC compared to other mechanisms, including general-purpose data compression.

Data compression mechanisms are designed to compress data for storage or for bulk transfers, such as large files over the Internet. They come with a range of trade-offs between compression ratio and decompression/recompression complexity and speed, memory footprint, and latency. In these mechanisms, each compressed archive is self-contained: the compressed stream carries its own compression/decompression context. This makes the stream portable, at the cost of per-archive overhead, and assumes a one-shot compression of a known dataset.

Network traffic is a different setting. Packets are structured binary records — protocols, headers, fields — that are processed individually and in real time by network nodes. Within a traffic flow or session, fields repeat across packets over time, and this redundancy is what makes compression possible. However, the compressor and the decompressor cannot rely on a per-file context.

Traditional data compression algorithms therefore do not apply directly to network traffic: they produce and consume their context per file or per archive. Applying them to traffic requires adaptations.

The research intern will work on the following:

Review existing data compression mechanisms (zstd, LZ4, brotli, gzip, and others) and identify their operating requirements — shared dictionaries or buffers between the compressor and the decompressor, state synchronization, memory and speed budgets — and their applicability to per-packet network traffic compression. zstd, with its shared-dictionary and streaming modes, is the primary candidate for adaptation.

Build a benchmark tool to compare such traffic compression mechanisms along the axes that matter for networks: compression ratio, compression/decompression throughput, memory footprint, and latency.

Evaluate the performance of applicable mechanisms on typical network traffic of interest using network capture datasets as reference and synthetic traffic generation to fill coverage gaps.

Share the results with the Orange delegates at the IETF and the 3GPP. Selected results will be shared with the IETF SCHC working group, where the need for a reference baseline against general-purpose compression is actively discussed.

This work complements that of a PhD candidate developing machine-learning-based compression approaches.

• Master's degree in Computer Science or Telecommunications (final year)
  Proficiency in Python and/or Rust
• Curious, autonomous, comfortable working in a research team
• Willingness to contribute to standardization work (IETF SCHC working group)
• Excellent written and spoken English skills

The Innovation Division's ambition is to push the boundaries of Orange's innovation and strengthen its technological leadership by leveraging our research capabilities to foster responsible innovation serving humanity, inform the Group's long-term strategic choices, and influence the global digital ecosystem.

We train experts in today's and tomorrow's technologies and ensure continuous improvement in the performance of our services and efficiency. The Innovation Division brings together, worldwide, 6,000 employees dedicated to research and innovation, including 740 researchers. With a global vision and a diverse range of profiles (researchers, engineers, designers, developers, data scientists, sociologists, graphic designers, marketers, cybersecurity experts…), the women and men of Innovation listen and serve the countries, regions, and business units to make Orange a trusted multiservice operator.

Within Orange Innovation, you will be part of a research team with dual expertise in artificial intelligence and signal processing. You will work alongside researchers engaged in innovative and foundational topics shaping the future of telecommunications, particularly for 6G.

Desired start date : Mar 01, 2027, 12:00AM

At Orange, only your skills matter.

Regardless of your age, gender, background, origin, religion, sexual orientation, disability, neurodiversity, or appearance, we actively encourage diversity within our teams, as it is a collective strength and a driver of innovation.
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