cs.DC · 2026-05-27 · No. 10

Distributed, Parallel, and Cluster Computing, 2026-05-27.

3 new papers in cs.DC. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Autonomic Federated-Market Orchestration for the Edge-Cloud Continuum

    Lauri Lovén, Roberto Morabito, Abhishek Kumar, Susanna Pirttikangas, Jukka Riekki, Sasu Tarkoma

    cs.DC · cs.GT · cs.MA · cs.NI

    The edge-cloud computing continuum demands self-management mechanisms that scale across autonomous administrative domains while honouring tenant- and operator-specified data sovereignty. We present Neural Pub/Sub, a federated-broker autonomic substrate whose self-organising behaviour emerges from market-based price signals rather than centralised control. Its MAPE-K control loop closes over per-broker health and load monitoring, marginal-cost...

    arxiv.org/abs/2605.27106 · PDF

  2. 02

    Nonlinear spectral clustering with C++ GraphBLAS

    Dimosthenis Pasadakis, Olaf Schenk, Verner Vlacic, Albert-Jan Yzelman

    cs.DC

    Nonlinear reformulations of the spectral clustering method have gained a lot of recent attention due to their increased numerical benefits and their solid mathematical background. However, the estimation of the multiple nonlinear eigenvectors is associated with an increased computational cost. We present an implementation of a direct multiway spectral clustering algorithm in the $p$-norm, for $p\in(1,2]$, using a novel C++ GraphBLAS API. The...

    arxiv.org/abs/2605.26975 · PDF

  3. 03

    Revisiting Bruck: Phase-Efficient All-to-All Communication in Reconfigurable Networks

    Anton Juerss, Stefan Schmid

    cs.DC · cs.NI

    All-to-All communication is a key performance bottleneck for distributed machine learning (ML) and high-performance computing (HPC) workloads, where dense traffic increasingly stresses scale-up interconnects. While these ML and HPC workloads have driven unprecedented infrastructure demand, optical reconfigurable networks (ORNs) offer a promising path forward. By adapting the physical topology to the active workload, they improve communication...

    arxiv.org/abs/2605.26930 · PDF

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