cs.DC · 2026-08-21 · No. 91

Distributed, Parallel, and Cluster Computing, 2026-08-21.

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

    Design and Empirical Evaluation of a Network-Centric, On-Premises Architecture for Earth Observation Data Access

    João Pinelo, João Gonçalves, Denis Willett, Amit Ruhela, Derek Steinmoeller, Uriel Mendoza, Pelumi S. Alao, Ronald...

    cs.DC · cs.NI · cs.PF

    Earth observation (EO) programmes generate data at volumes that exceed the transfer and storage capacity of most institutional networks. Public cloud platforms address this for well-resourced organisations, but institutions across the Atlantic basin face constraints in connectivity, sovereignty and funding that make on-premises infrastructure the only viable path. Cloud-native data formats enable efficient partial reads, yet their performance...

    arxiv.org/abs/2608.20283 · PDF

  2. 02

    ParaWeb: Parallel Programming Patterns for Web Development

    Suejb Memeti

    cs.DC

    Modern web applications increasingly require computationally intensive processing, yet JavaScript, the dominant language of the web, has traditionally been limited to a single-threaded execution model. Node.js Worker Threads and browser Web Workers provide low-level mechanisms for parallel execution, but developers lack high-level abstractions that capture recurring parallel structures as reusable patterns. In this paper, we present ParaWeb,...

    arxiv.org/abs/2608.19935 · PDF

  3. 03

    CacheRoute: Planned Prefix-Affinity Routing for Large-Scale LLM Serving

    Huang Cheng

    cs.DC · cs.LG

    Prefix caching avoids prefill only when a repeated request returns to a server that still holds the prefix KV. Cache-blind balancing disperses that reuse; fixed affinity preserves it but can overload a server. CacheRoute resolves this tradeoff with a periodic routing plan. It admits high-rate keys to a stable warm set and places their assignments by expected load. Hot keys may use more than one destination, although every key in our primary...

    arxiv.org/abs/2608.19677 · PDF

  4. 04

    Optimality and Trade-offs in Fast BFT SMR (Extended Version)

    Neil Giridharan, Minh Tung Nguyen, Ittai Abraham, Allen Clement, Natacha Crooks, Pierre Sutra

    cs.DC

    Fast state-machine replication (SMR) protocols in the crash-fault setting have attracted significant interest in both academia and industry. This interest stems from their advantages over leader-based protocols, including low execution latency for non-conflicting commands, high throughput, improved availability, and increased fairness. While Fast SMR is well studied in the crash-fault setting, the Byzantine fault-tolerant (BFT) setting...

    arxiv.org/abs/2608.19629 · PDF

  5. 05

    When Do LLM Agents Help? Deadline-Aware Mixed-Criticality Task Scheduling at the Autonomous-Vehicle Edge

    Reza Zakerian

    cs.DC · cs.MA

    Autonomous vehicles offload latency-sensitive perception tasks to nearby mobile edge computing (MEC) servers, where a missed safety-critical task is unsafe rather than merely degraded. Large language models (LLMs) are increasingly proposed as adaptive, explainable schedulers, yet evidence of when they help is scarce. We study deadline-aware, mixed-criticality scheduling on heterogeneous MEC servers, where time-critical (TC) tasks must be...

    arxiv.org/abs/2608.19557 · PDF

  6. 06

    The Lazy Pod That Lies: Deferred Cost and Failure Semantics of Lazy Container Image Pulling for Model Serving on Kubernetes

    Georgii Kliukovkin

    cs.DC

    Lazy container-image pulling promises to eliminate the dominant cost of starting a model-serving pod by mounting the image immediately and fetching content on demand. We evaluate this promise for model delivery on Kubernetes, using KServe with two production lazy-pulling systems -- eStargz/stargz-snapshotter and AWS SOCI -- against eager baselines, on artifacts from 2 to 140 GB including real fp16 weights. Lazy pulling delivers its headline:...

    arxiv.org/abs/2608.19412 · PDF

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