cs.DC · 2026-09-17 · No. 116
Distributed, Parallel, and Cluster Computing, 2026-09-17.
16 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
16 entries-
01
Replication-Aware Placement of Functions and Data in the Edge-Cloud Continuum
Dario d'Abate, Matteo Cenzato, Matteo Briscini, Arianna Dragoni, Alessandro Margara
cs.DC
Function-as-a-Service (FaaS) has emerged as the prominent programming model for the edge-cloud continuum. FaaS inherently decouples stateless functions from their persistent state. We study how to jointly schedule functions and place data to minimize client latency, considering data replication under heterogeneous consistency requirements. We introduce a Binary Linear Programming (BLP) model to compute optimal placements, establishing a...
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02
Ermes: a Stateful Serverless Platform for the Edge-to-Cloud Continuum
Matteo Cenzato, Dario d'Abate, Arianna Dragoni, Giacomo Orsenigo, Luca Tosetti, Matteo Briscini, Alessandro Margara
cs.DC
Function-as-a-Service (FaaS) is a widely adopted paradigm to simplify application deployment across the edge-to-cloud continuum. However, its stateless nature forces functions to retrieve their state from external, typically cloud-centric, data stores, reintroducing the very latency that edge computing aims to eliminate. This issue is further exacerbated by location-agnostic schedulers and rigid, one-size-fits-all consistency models that fail...
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03
Fluid Notarization: Verifiable Evolution of Concurrently Edited Structured Documents
Amos Brocco, Giuliano Gremlich, Roberto Guidi
cs.DC · cs.CR
Traditional blockchain-based document notarization follows a snapshot-oriented model in which each document revision is represented as an independent state anchored on-chain through a cryptographic reference. While effective for immutable artifacts, this approach becomes inadequate when documents evolve through collaborative editing. Concurrent modifications create divergent document versions that must be reconciled outside the notarization...
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04
Ask the Tool, Don't Guess: Agent Tool Calls Hold Their Progress, and the Serving System Should Read It
Yipeng Liu, Yingqiang Zhang, Feifei Li, Huanchen Zhang
cs.DC · cs.AI · cs.OS
An agentic request spends substantial wall-clock time waiting for tools, and its KV cache holds GPU memory the whole time. Serving systems decide whether that cache stays, leaves, or comes back by guessing how long the tool will run, from the tool's name, its history, a duration declared before the call, or the engine's own occupancy. We show that no estimate fixed before a call starts can know its duration, and such estimates may not even...
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05
A Distributed Computing Framework for Satellite Swarms
Ezra Fielding, Clement Demazure, Guthemberg Silvestre, Felipe Alves Suana, Philippe Quéinnec
cs.DC · cs.NI
The rise of large satellite constellations and Distributed Space Systems (DSS) demands generalized frameworks that enable fault-tolerant, autonomous distributed space applications. Conventional ground-centric command and control does not scale to systems of tens or hundreds of satellites, motivating the adoption of distributed computing. This paper introduces a conceptual distributed computing framework for satellite swarms, covering...
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06
Vigil: Accountable Liveness against Selective Silence
Jiawei Cheng, Huiping Sun, Rui Zhou, Jinjue Zhou, Zhong Chen
cs.DC
BFT accountability is well understood for safety violations, and recent work attributes global liveness violations; \emph{recipient-selective} silence remains unresolved. A selectively silent adversary withholds messages from some honest nodes while behaving correctly toward others. It can stall consensus yet evade every existing mechanism. We initiate a systematic study of accountability against selective silence. Negatively, a lone attacker...
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07
RayOrch: Programming and Executing Lineage-Controlled Multi-Grain Dataflows for Foundation-Model Data Preparation
Xiaochen Ma, Zimo Meng, Junzhu Liang, Youhe Jiang, Yue Cheng, Hao Liang, Bohan Zeng, Dengchun Li, Lu Ma, Zhengyang...
cs.DC
Preparing high quality training data for foundation models requires scalable pipelines that transform heterogeneous documents and videos into structured records. Such pipelines expand each parent item into an ordered and input dependent sequence of children, whose counts may be long tailed. GPUs should batch children across parents while preserving parent relationships, child order, completion status, and result routing. Existing systems...
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08
AUPE: Collaborative byzantine fault-tolerant peer-sampling
Augusta Mukam, Joachim Bruneau-Queyreix, Laurent Réveillère
cs.DC
Peer sampling is a crucial primitive in distributed systems, used to manage overlays and disseminate information in large-scale scenarios such as permissionless blockchain systems. Its purpose is to maintain and regularly update a local and partial snapshot, or view, of the complete system's membership. These protocols are often targeted by malicious actors who aim to disrupt higher-level protocols. Typically, an adversary who controls a set...
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09
COMPASS-ABS: Reducing Fragmentation in Shared GPU Clusters for Deep Learning Training Workloads
Yukai Zhou, Hongfan Wu
cs.DC · cs.LG
With the rapid advancement of deep learning technology, shared GPU clusters receive an increasing number of deep learning training (DLT) jobs. Yet resource fragmentation make such clusters underutilized and forces the DLT jobs running on them to endure long turnaround times. Extensive research has been devoted to quantifying fragmentation and developing scheduling algorithms that alleviate its impact. However, existing fragmentation measures...
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10
DiverseFT: Diversifying Fault-Tolerant Systems using LLMs
Arne Vogel, Christian Berger, Rüdiger Kapitza
cs.DC
Fault-tolerant agreement protocols fail if replicas share a common flaw that simultaneously affects more replicas than the tolerable threshold. Therefore replicas should ideally fail independently, which can be achieved through diversification. However, in practice, often the same protocol implementation is shared by all replicas which is not surprising given that the provision of multiple diverse implementations is difficult and highly...
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11
From Pixels to Semantics: Edge AI for UAV-Based Critical Infrastructure Inspection
Reza Farahani, Naser Hossein Motlagh, Zoha Azimi, Christian Timmerer, Lorenzo Carnevale, Sasu Tarkoma, Schahram Dustdar
cs.DC
Critical infrastructure assets such as bridges, tunnels, dams, and power line networks require timely and scalable inspection. While conventional manual inspection remains costly and hazardous, unmanned aerial vehicle (UAV)-based inspection has emerged as an efficient alternative for monitoring difficult-to-access structures. Existing UAV inspection pipelines have evolved from cloud-centric offline processing toward edge-based perception...
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12
GeoMesh: Workload-Balanced and Sign-Compressed Geo-Distributed LLM Training
Changyong Shin, Jaerim Park, Minchul Kang, Younghun Go, Zhixiong Niu, Yongqiang Xiong, Gyeongsik Yang, Chuck Yoo
cs.DC
Large language models are increasingly trained on GPUs distributed across multiple regions, but geo-distributed training is challenging in practice. Real clusters often contain GPUs with different speeds and memory capacities, and they communicate over slow wide-area networks. Our analysis shows that this creates serious problems: existing synchronous methods preserve stable updates, but fast GPUs wait up to 20.9% of their runtime for slower...
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13
Zero-I/O Fault Recovery for Sharded Deep Learning via Dynamic Framework Dependency Rebinding
Genlang Chen, Junyi Zhu
cs.DC
Distributed model training at scale is frequently interrupted by transient network failures, conventionally forcing cluster managers to abort all processes and roll back to the latest checkpoint. While periodic checkpointing provides durability, frequent snapshotting introduces severe storage backpressure: our measurements on a 1.216B-parameter decoder reveal that per-update asynchronous checkpointing incurs up to a +656.7% latency overhead,...
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14
Token Latency Fairness: Performance Isolation for Multi-Tenant LLM Serving
Dev Bali, Soujanya Ponnapalli, Yichuan Wang, Natacha Crooks, Scott Shenker, Matei Zaharia
cs.DC · cs.LG
LLM serving is typically offered as a shared, multi-tenant service, where high-demand workloads from one client can cause latency SLO violations for others. Existing solutions for performance isolation equalize client throughput in the long run, for example through queueing and batching fairness. However, these approaches do not provide latency isolation guarantees; as a result, well-behaved clients can still experience significant...
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15
SSD-LLaMA: SSD-Native Inference for Trillion-Parameter MoE at 1+ Token/s on a Consumer PC
Fangzhou Liang, Yibin Shen, Jianmin Hu, Jiayang Xu, Hanchi Gao, Minxian Xu, Zili Meng
cs.DC
Frontier open-weight language models increasingly use Mixture-of-Experts (MoE) architectures to expand model capacity while activating only a small subset of experts per token. Local inference must nevertheless keep the complete expert pool available, which remains far beyond consumer-grade RAM and VRAM capacity even after quantization. SSDs provide practical capacity at this scale, but turning that capacity into executable model memory...
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16
Towards Training Private LLMs: Exploring Fine-Tuning Language Models on Apple Silicon with RDMA over Thunderbolt
En-Ming Huang, Yao-Ting Hsieh, Hsiang-Yu Tsou, Mu-Chi Chen, Shih-Hao Hung, H. T. Kung
cs.DC
Private large language model (LLM) fine-tuning is increasingly important for organizations that need to adapt models using sensitive data, but it often exceeds the memory capacity of commodity datacenter accelerators. Apple Silicon offers a different design point through large unified memory and lower complete-system cost, while recent Apple software support enables distributed execution over RDMA-over-Thunderbolt (TB). This paper studies...
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