arXiv:2607. 25995v1 Announce Type: cross Abstract: Kubernetes is central to the cloud-native ecosystem, orchestrating containerised workloads.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
TriFleetRCA is an on‑premise pipeline that performs root‑cause analysis for Kubernetes using a single GPU. It gathers evidence at pod, namespace, or cluster scope, deduplicates and ranks it with BM25, filters runbooks through an ingest guard, and returns a root cause with supporting evidence lines. In a live cluster with four injected faults, the system achieved hit rates of 0.85–0.95 across scopes, improved accuracy with deduplication, and demonstrated robust defense against poisoned runbooks.
arXiv:2606. 08590v1 Announce Type: cross Abstract: Kubernetes incidents are diagnosed reliably only when a root-cause system's reported gains come from incident evidence rather than scenario-specific shortcuts.
The paper introduces a Kubernetes Dynamic Resource Allocation driver that treats composable CXL memory as a schedulable cluster resource, enabling cross-node shared memory for large language model (LLM) serving. By composing CXL regions on demand, materializing them as DAX devices, and exposing them via a single Container Device Interface name, pods on different nodes can access the same physical memory region. A shared‑memory connector for vLLM/llm‑d uses this region as a KV‑cache tier, eliminating external metadata services and achieving significant reductions in time‑to‑first‑token (TTFT) with minimal additional latency compared to same‑node reuse.
arXiv:2606. 03323v2 Announce Type: replace-cross Abstract: The rise of LLM-as-a-Service and other confidential cloud workloads demands cryptographic proof that user data is processed in a trusted, untampered environment.
arXiv:2606. 03323v1 Announce Type: cross Abstract: The rise of LLM-as-a-Service and other confidential cloud workloads demands cryptographic proof that user data is processed in a trusted, untampered environment.
FDE-Bench is a benchmark that tests large language model agents on 136 deployment‑configuration tasks involving Docker, Compose, and Kubernetes, in both greenfield and diagnose‑and‑repair scenarios. Agents submit declarative artifacts that are rebuilt and redeployed in a clean environment, and four binary check layers evaluate build, readiness, behavior, and specification conformance without an LLM judge. The benchmark includes a release gate, detailed check annotations, adversarial strategies, and reports that state‑of‑the‑art models resolve 52.9–75.0 % of tasks, while zero‑intelligence baselines solve none.