cs.DC · 2026-08-14 · No. 84
Distributed, Parallel, and Cluster Computing, 2026-08-14.
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
OpScale: Operator-level Provisioning and Autoscaling for LLM Serving
Xingqi Cui, Chieh-Jan Mike Liang, Ziang Tang, Jiarong Xing, Haoran Qiu
cs.DC
Achieving cost efficiency while meeting strict user-facing SLOs (e.g., time-to-first-token) remains a fundamental challenge for cloud GPU clusters serving large language models (LLMs). Autoscaling is the key mechanism for cluster resource management, yet a basic system design question is open for serving LLMs: what should be the unit of scaling? Existing approaches primarily treat the entire model as a monolithic scaling unit--simple but...
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02
Fast Tendermint: Speeding Up a Foundational Consensus Protocol
Preston Vander Vos, Daniel Cason
cs.DC
Tendermint is among the most widely studied and deployed Byzantine fault-tolerant (BFT) consensus protocols, owing in part to its native leader-rotation mechanism that subsumes complex view changes. Like most partially-synchronous BFT protocols, Tendermint tolerates $f < n/3$ Byzantine processes and decides in three communication steps. Motivated by the push for lower-latency blockchains, a recent line of work shows that consensus can be...
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03
Triangle-Free Coloring in LOCAL via Resilient Lovász Local Lemma
Peter Davies-Peck, Xusheng Zhang
cs.DC · cs.DS
The Lovász Local Lemma (LLL) is a probabilistic tool that has been shown to be of central importance in the study of distributed algorithms. For example, the constructive LLL is known to be complete for the class of locally-checkable labeling problems with $o(\log n)$ randomized complexities in the LOCAL model. One classic application of the LLL is in coloring graphs with some sparse structure, such as triangle-free graphs. Triangle-free...
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04
LipCache: A Local Inference Proxy with Certified Caching for Edge Image Classification Service
Zhengzhe Xiang, Yinlin Chen, Fuli Ying, Binbin Zhou, Hailiang Zhao, Schahram Dustdar
cs.DC · cs.AI
As edge-side vision services continue to expand toward low-latency, high-throughput scenarios, reducing the inference cost of vision models without sacrificing reliability has become a central concern. Existing semantic caching methods largely rely on empirical similarity thresholds; while such thresholds improve hit rates, they tend to introduce silent misclassifications near decision boundaries. To address this, we propose...
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05
Validation-Centric AI-Assisted GPU Porting of a 250,000+ Line Legacy Weather Simulation Code
Tetsuya Hoshino, Masaya Kato, Kazuhisa Tsuboki, Daichi Mukunoki, Takahiro Katagiri, Toshihiro Hanawa
cs.DC
Recent advances in large language models have made CLI-based AI agents a practical tool for accelerating GPU porting of large legacy scientific applications. Such applications, however, are not merely old code bases; they are scientific assets whose credibility has been accumulated through long-term development, comparison with observations, and use in domain studies. GPU porting must therefore preserve this scientific validity while adapting...
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06
Meshlib: In-Process Policy Enforcement for Sidecar-less Service Meshes
Habib Mostafaei, Tom van Liempd
cs.DC
Service meshes facilitate service-to-service communication and enforce security policies in microservice architectures. However, they often depend on per-pod sidecar proxies, which introduce significant latency and resource overhead due to redundant application-layer parsing on every request. Eliminating sidecars without compromising security guarantees remains a central challenge. To address this, we introduce Meshlib, a sidecar-less service...
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07
TEMPO: Makespan-Aware Expert-Parallel Load Balancing Across Memory- and Compute-Bound Regimes
Jie Li, Chenxin Jia, Jinliang Shen, Cunzhuang Liu, Ruiyi Ding, Jianwen Xian, Kang He, Chengru Song
cs.DC · cs.AI · cs.CL · cs.GT
In expert-parallel (EP) MoE serving, every layer synchronizes at the slowest GPU. Dispatchers balance token counts (EPLB, LPLB, UltraEP) or activated-expert counts (METRO), assuming expert time is linear in one. Measurements on two datacenter GPU generations show it is neither: below $\nstar\!\approx\!156$--$168$ tokens, HBM weight streaming dominates---cost attaches to \emph{activated replicas}, not tokens; above it, grouped GEMM rounds...
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08
Efficient Randomized LL/SC that Preserves History Independence
Dante Bencivenga, Homa Habashi, Philipp Woelfel
cs.DC · cs.DS
We study the fundamental problem of implementing $m$ linearizable LL/SC objects with constant expected step complexity in a system of $n$ processes, using bounded base objects commonly available in hardware. Assuming that each process may have at most $τ$ outstanding LL operations, the best known deterministic algorithm requires $Ω(n^2τ+ m)$ base objects (CAS and registers) [Blelloch and Wei, DISC 2020]. Previously, no comparable randomized...
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09
InFactPlanner: Planning Sustainable Geo-Distributed LLM Data Centers
Nicoletta Tsiopani, Moysis Symeonides, George Pallis, Marios D. Dikaiakos
cs.DC · cs.AI
The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality. Yet operators often need to compare deployment alternatives before large-scale infrastructure is built, making direct measurement costly, slow, and sometimes infeasible. We present InFactPlanner, a trace-driven...
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