Pith Number

pith:TIGZDVKQ

pith:2026:TIGZDVKQSAGFLB3AQ3WE4OR7PR

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Near-optimal Online Traffic Engineering

Arvin Ghavidel, Nikolai Matni, Pooria Namyar, Ramesh Govindan, Walter Willinger

OnlineTE uses optimization decomposition for distributed solvers that deliver near-optimal WAN traffic engineering within seconds of changes.

arxiv:2605.16187 v1 · 2026-05-15 · cs.NI

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

OnlineTE delivers near-optimal solutions within seconds of a change, scales to large WANs, and can outperform the state-of-the-art by up to an order of magnitude on a 750-node WAN topology emulation.

C2weakest assumption

That the theory of optimization decomposition can be turned into a practical distributed solver whose local subproblems, when coordinated, remain near-optimal under real WAN dynamics, failures, and measurement noise.

C3one line summary

OnlineTE uses optimization decomposition to enable distributed, near-optimal traffic engineering that reacts in seconds to changes in large WANs and outperforms prior centralized approaches in emulation.

References

48 extracted · 48 resolved · 1 Pith anchors

[1] [n. d.]. Arista 7800 Series. https://www.arista.com/assets/data/pdf/ Datasheets/7800R4-Series-AI-Spine-Datasheet.pdf. ([n. d.])

[2] [n. d.]. Internet Topology Zoo. http://www.topology-zoo.org/. ([n. d.])

[3] Firas Abuzaid, Srikanth Kandula, Behnaz Arzani, Ishai Menache, Matei Zaharia, and Peter Bailis. 2021. Contracting Wide-area Network Topologies to Solve Flow Problems Quickly. InNSDI 2021

[5] Rdma over ethernet for distributed training at meta scale 2024 · doi:10.1145/3651890.3672237

[6] David Applegate, Mateo Díaz, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O’Donoghue, and Warren Schudy. 2022. Practical Large- Scale Linear Programming using Primal-Dual Hybrid Gradient. (2022). ar 2022

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:56.988941Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9a0d91d550900c55876086ec4e3a3f7c676ea47296b7df4f646862bdf6e6a3ef

Aliases

arxiv: 2605.16187 · arxiv_version: 2605.16187v1 · doi: 10.48550/arxiv.2605.16187 · pith_short_12: TIGZDVKQSAGF · pith_short_16: TIGZDVKQSAGFLB3A · pith_short_8: TIGZDVKQ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TIGZDVKQSAGFLB3AQ3WE4OR7PR \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9a0d91d550900c55876086ec4e3a3f7c676ea47296b7df4f646862bdf6e6a3ef

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "9e50c8402138589f74e54c88ec006ac92a153550bdb178a6ad73007213cbd980",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.NI",
    "submitted_at": "2026-05-15T17:06:56Z",
    "title_canon_sha256": "6a055dc9ef877d7b6cbbf9384b877c07a6f0f86e99d52a42af2b2226d375dd5d"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16187",
    "kind": "arxiv",
    "version": 1
  }
}