cs.DC · 2026-08-20 · No. 90
Distributed, Parallel, and Cluster Computing, 2026-08-20.
9 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
9 entries-
01
A Fast Deterministic Algorithm for $(Δ+1)$-edge coloring in CONGEST
Sebastian Brandt, Ananth Narayanan, Alexandre Nolin
cs.DC
Vizing's theorem states that any graph of maximum degree $Δ$ can be properly edge-colored with $Δ+ 1$ colors (which is optimal in general). A recent breakthrough result by Bernshteyn showed that such a $(Δ+ 1)$-edge coloring can be found deterministically in $poly(Δ,\log n)$ rounds in the LOCAL model of distributed computing, where $n$ denotes the number of vertices of the input graph [J. Comb. Theory 2022]. Since then, the exponent in the...
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02
Upper and Lower Bounds on the Space Complexity of Multi-word Single-Writer Registers
Yuanhao Wei, Yousof Yavari
cs.DC
We prove matching upper and lower bounds on the space complexity of simulating a large shared register using smaller shared registers. We focus on the case where both the simulated and base registers are single-writer, which means they can be accessed concurrently by multiple readers but only by a single writer. To strengthen our lower bounds, we prove that they hold even when the base registers are atomic and the simulated register is...
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03
Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets
Tate Berenbaum, Muthaiah Venkatachalam
cs.DC · cs.AI · cs.SE
Modern Intel AI PCs ship capable integrated GPUs and NPUs with 16+ GB of unified memory, and they spend considerable time idle. That is not enough memory to fit a large model such as a 70B-parameter LLM. We show that a handful of AIPCs, working together over an ordinary network, can serve models beyond the capability of any single one. We use pipeline parallelism: a model is split by layer into per-stage shards, each pre-compiled into an...
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04
Approximating Minimum Dominating Set with Few Awake Rounds
Hongyan Ji, Shreyas Pai, Sriram V. Pemmaraju
cs.DC
We study the Minimum Dominating Set (MDS) problem in the sleeping CONGEST model (Chatterjee, Gmyr, and Pandurangan, PODC 2020), a generalization of the standard CONGEST model, in which a node may sleep in some rounds and can only compute, send messages, or receive messages when it is awake. The awake complexity of an algorithm in this model is the worst case number (over all inputs and all nodes) of rounds a node is awake for during the...
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05
Tight Energy Lower Bounds for Distributed Graph Algorithms
Fabien Dufoulon, Gopal Pandurangan, Peter Robinson
cs.DC · cs.DS
There has been a significant recent interest in designing distributed algorithms in the SLEEPING model that minimize the {energy (a.k.a awake) complexity, which measures the number of rounds a node is awake during the algorithm. A node spends non-trivial resources (messages, energy, etc.) only when it is awake and not while sleeping. Energy complexity has been studied for various fundamental problems with respect to minimizing the maximum...
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06
Enabling Reuse for Data-Sharing Pipelines in Federated Environments
Sepideh Masoudi, Maria C. Borges, Eduardo Brito, Sebastian Werner, Stefan Tai
cs.DC
Data mesh architectures enable decentralized data sharing through domain-owned data products, but supporting diverse consumers in federated settings often requires customized data-sharing pipelines. As the number of consumers grows, this leads to a proliferation of pipelines, increasing design and maintenance complexity. We observe that such pipelines frequently exhibit substantial structural overlap. In this paper, we argue that reuse should...
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07
A Simple Construction of Locally Checkable Problems Filling the LOCAL Complexity Gaps in Graphs with Arbitrary Large Degrees
Filippo Casagrande, Pierre Fraigniaud, Benjamin Jauregui, Mikaël Rabie
cs.DC
We show that the complexity gaps in the round complexities of locally checkable labeling (LCL) problems are not due to the fact that solutions to LCL problems must be locally checkable, but solely to the fact that LCL problems are defined only for graphs of maximum degree upper bounded by some arbitrary yet constant value $Δ$. Specifically, we show that there are infinitely many locally checkable problems (i.e., problems whose solutions can...
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08
SLO-Scaler: Uncertainty-Aware SLO-Driven Autoscaling for Microservices
Shuo Wang, Xiaoxuan Sun, Shao-yu Huang, Bencheng Su, Shuo Xu, Netra Awate
cs.DC
Autoscaling microservice-based applications to satisfy Service Level Objectives (SLOs) remains challenging due to bursty workloads, cascading latency across service dependencies, and cold-start overhead. Existing approaches such as the Kubernetes Horizontal Pod Autoscaler (HPA) rely on threshold-based CPU or memory metrics, which react too slowly to traffic spikes. Recent predictive methods improve responsiveness but generate point forecasts...
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09
When Do Microservices Save Energy? Evidence from Environmental Simulation Workflows
Joshua Rowley, Abdessalam Elhabbash
cs.DC · cs.SE
Environmental simulation models support scenario analysis, calibration, and decision-making, but repeated execution can incur significant energy costs. Microservices offer modularity and scalability, yet their low-carbon impact remains unclear because decomposition introduces orchestration, communication, persistence, and idle-service overheads. This paper evaluates four environmental models as containerised microservice workflows, comparing...
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