A focused digest of distributed computing, cloud-native infrastructure, and engineering research from authoritative sources.
Scope This document describes three failure scenarios that separate having backups from being able to recover, and the guidance that follows from each. Every scenario is reproducible on a laptop from the lab repository above, and...
Read at sourcearXiv:2609.09160v1 Announce Type: new Abstract: We present LBFAST, a GPU-oriented lattice Boltzmann solver based on a lightweight moment-represented formulation, in which post-collision populations are reconstructed on the fly from a reduced set of moments rather than stored explicitly. This approach significantly low…
Read at sourcearXiv:2609.09161v1 Announce Type: new Abstract: The deployment of AI-driven Digital Twins (DTs) in large-scale Internet-of-Things (IoT) ecosystems demands continuous, high-fidelity synchronization between the physical environment and its virtual replica. Conventional approaches rely on dense sensor deployments, which…
Read at sourcearXiv:2609.09164v1 Announce Type: new Abstract: Operating latency-sensitive services across multiple public clouds creates an optimization surface no single provider autoscaler can see: on-demand vCPU prices differ by provider, spot discounts and interruption risks vary by provider and instance type, egress fees penal…
Read at sourcearXiv:2609.09165v1 Announce Type: new Abstract: Scheduling workflow directed acyclic graphs (DAGs) on heterogeneous distributed systems is a classical NP-hard problem, and the list-scheduling heuristic HEFT remains the defacto standard because of its low complexity and strong makespan. In real deployments, however, ma…
Read at sourcearXiv:2609.09348v1 Announce Type: new Abstract: Managing resources across IoT, edge, and cloud layers calls for continuous, context-aware decisions under constraints that rarely stay fixed. Deep reinforcement learning (DRL) handles this class of problems well, and large language models (LLMs) are increasingly used to…
Read at sourcearXiv:2609.09742v1 Announce Type: new Abstract: This paper revisits TEE-assisted BFT under a universal partial-TEE model, where an arbitrary subset of replicas execute inside TEEs while the remaining replicas operate without hardware trust guarantees. We show that heterogeneous trust changes the structure of quorum fo…
Read at sourcearXiv:2609.09748v1 Announce Type: new Abstract: Diffusion language models generate text by refining a fixed-size block of token positions through many forward passes, a loop that does not match the per-forward execution unit used by most LLM serving systems. A dense MoE runtime binds all work to the refinement-iterati…
Read at sourcearXiv:2609.10044v1 Announce Type: new Abstract: We introduce a graph decomposition primitive called introvert clustering, which strengthens standard low-diameter clustering by guaranteeing that every clustered vertex keeps at least a $\left(\frac12-\varepsilon\right)$-fraction of its relevant neighbors in its own clus…
Read at sourcearXiv:2609.10151v1 Announce Type: new Abstract: Centralized learning is a fundamental paradigm in modern AI, in which data are collected from distributed edge devices and aggregated at a central host for model training. However, this pipeline is often bottlenecked by the substantial communication overhead of data coll…
Read at sourcearXiv:2609.10368v1 Announce Type: new Abstract: Tensor compilers such as TinyTC and OpenAI Triton were originally developed for AI workloads, but the same tiling and memory abstractions can be applied to implement efficient high-order stencils for scientific and industrial applications. We demonstrate this for an 8th…
Read at sourcearXiv:2609.10509v1 Announce Type: new Abstract: Scientific workflow management (WMSs) systems automate execution, yet orchestrate using fixed, hand-tuned rules. LLM agents promise more autonomous orchestration, but it remains unclear where to introduce agentic reasoning, how to bound its risk, and when it actually hel…
Read at sourcearXiv:2608.24480v1 Announce Type: cross Abstract: ES-HyperNEAT evolves substrate topology through adaptive quadtree subdivision; to our knowledge, no implementation with full population-level GPU parallelization exists. We present JAX-ESHN, a JAX-based implementation targeting GPU parallelization with batched CPPN que…
Read at sourcearXiv:2608.27612v1 Announce Type: cross Abstract: In neuroevolution, indirect encoding generates neural network connectivity from a compact genome rather than specifying each connection. ES-HyperNEAT automatically discovers where to place hidden nodes by examining CPPN output patterns: it recursively subdivides space…
Read at sourcearXiv:2609.09166v1 Announce Type: cross Abstract: This paper investigates collaborative speculative decoding (CoSD), a distributed large language model (LLM) inference framework in which an on-device small language model (SLM) drafts candidate tokens and a server LLM verifies them. Existing CoSD methods assume a share…
Read at sourcearXiv:2609.09270v1 Announce Type: cross Abstract: Moving from quantum research and development to production-grade, fault-tolerant quantum workload execution remains one of the most significant challenges facing quantum platform builders. While Python frameworks have enabled an easy entry point for quantum algorithm d…
Read at sourcearXiv:2609.09961v1 Announce Type: cross Abstract: We propose a variable and behavior-based node-specific proof-of-work (PoW) model for a directed acyclic graph (DAG)-based distributed ledger technology (DLT) network to mitigate decentralized network congestion control. Network congestion control for centralized commun…
Read at sourcearXiv:2609.10515v1 Announce Type: cross Abstract: In modern AI Accelerators and GPGPUs, many concurrent cores repeatedly access the same shared data. This pattern occurs in attention, where different query tiles share the same K/V block, GEMM, where every tile in a row reads the same panel, and many other operators. W…
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