cs.DC · 2026-05-22 · No. 7

Distributed, Parallel, and Cluster Computing, 2026-05-22.

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

01 — The papers

6 entries
  1. 01

    AI-Driven Multi-Region Provisioning for Cloud Services Using Spot Fleets

    Javier Fabra, Enrique Molina-Giménez, Pedro García-López

    cs.DC

    Cloud service platforms increasingly rely on elastic infrastructures to support dynamic workloads. Spot instances provide discounted computing resources but introduce uncertainty due to dynamic pricing, resource availability, and interruption risks that vary across geographical regions. In Amazon Web Services, the EC2 Spot Service simplifies fleet provisioning through allocation strategies, but it cannot estimate fleet costs before deployment...

    arxiv.org/abs/2605.22778 · PDF

  2. 02

    Relay-Based Synchronization of Replicated Data Types in Opportunistic Networks

    Frédéric Guidec, Yves Mahéo

    cs.DC · cs.NI

    In Opportunistic Networks (OppNets), the dissemination of information can only rely on transient pairwise radio contacts between mobile devices (peers). Designing distributed applications that can run in such conditions is a challenge, but replicated data types, and in particular Conflict-free Replicated Data Types (CRDTs), can help meet this challenge. A CRDT is inherently replicated data type whose replicas can be updated locally, yet...

    arxiv.org/abs/2605.22491 · PDF

  3. 03

    Exploiting Multicast for Accelerating Collective Communication

    Chao Xu, Xu Zhang, Zihang Luo, Yuyan Wu, Guoxin Qian, Yufeng Yao, Chihyung Wang, Jingbin Zhou

    cs.DC

    Reducing collective communication latency is a critical goal for large model training and inference in both academia and industry. Many-to-many communications, such as AllGather and AlltoAll (dispatch), are core components of modern parallelization strategies. State-of-the-art implementations of these communications rely on unicast-based writes and transmit duplicate copies of the same data across physical links for multiple receivers. This...

    arxiv.org/abs/2605.22428 · PDF

  4. 04

    Nf-PEAK: Process-Based Energy Attribution for Nextflow Workflows on Kubernetes Clusters

    Philipp Thamm, Somayeh Mohammadi, Kathleen West, Knut Reinert, Lauritz Thamsen, Ulf Leser

    cs.DC

    Scientific workflows are pipelines of interdependent tasks. They are increasingly executed on shared Kubernetes clusters via workflow engines such as Nextflow. Their energy consumption matters for both cost and sustainability. It is necessary to examine and optimize workflow tasks individually, because they can be very heterogeneous. However, estimating task-level energy on clusters is difficult: Intel RAPL counters report only node-level...

    arxiv.org/abs/2605.22393 · PDF

  5. 05

    Secure and Parallel Determinant Computation for Large-Scale Matrices in Edge Environments

    Prajwal Panth

    cs.DC · cs.AI · cs.CR · cs.MS

    The advent of edge computing has enabled resource-constrained clients to delegate intensive computational tasks to distributed edge servers, especially within Internet of Things (IoT) environments. Among such tasks, Matrix Determinant Computation (MDC) remains critical for applications in control systems, cryptography, and machine learning. However, the cubic complexity of traditional determinant algorithms makes them unsuitable for real-time...

    arxiv.org/abs/2605.22039 · PDF

  6. 06

    LiveR: Fine-Grained Elasticity via Live Reconfiguration for Model Training

    Haoyuan Liu, Kairui Zhou, Shuyao Qi, Qinwei Yang, Shengkai Lin, Shizhen Zhao, Wei Zhang

    cs.DC

    To reduce user costs and maximize cluster utilization, large model training increasingly leverages volatile but inexpensive GPU capacity, such as spot instances and reclaimable resources in shared clusters. Yet, capitalizing on these economic benefits requires jobs to adapt within the short warning windows that many such environments provide. Existing elastic training systems still treat reconfiguration as stop-and-restart: they externalize...

    arxiv.org/abs/2605.22014 · PDF

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