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Chapter 21: The PromQL Oracle

Modern SWI-Prolog (2026 Edition) Second... VOLUME IV: Observability & Telemetry

The Chapter 20 VictoriaMetrics stack is an instrumented system: 14 hypervisors emit 1,200 metrics each every 15 seconds, and all of it lands in a columnar merge-tree on obs-01 with 12 months of retention — but raw ingest is not reasoning, and a time-series dat...

Chapter 22: Time-Series Logic

Modern SWI-Prolog (2026 Edition) Second... VOLUME IV: Observability & Telemetry

The Chapter 21 Oracle can produce injection-immune PromQL strings for any metric in its closed vocabulary, but a string is not a fact — the gap between a 47.9% CPU steal reading stored as a float in a columnar TSDB and a node_health(pve3, critical) clause that...

Chapter 23: Distributed Prolog (Pengines)

Modern SWI-Prolog (2026 Edition) Second... VOLUME IV: Observability & Telemetry

The Chapter 22 Ingestor centralises both the computation and the network traffic on a single WAM, making the orchestrator's per-cycle work proportional to the size of the entire cluster — a ceiling that a 14-node deployment approaches but a 140-node deployment...

VOLUME IV: Strategic Briefing

Modern SWI-Prolog (2026 Edition) Second... VOLUME IV: Observability & Telemetry

Introduction: Closing the Loop – Telemetry, Inference, and Autonomous Remediation Volumes I and II of Modern SWI-Prolog (2026 Edition): Sovereign Infrastructure & Industrial Logic taught the Warren Abstract Machine (WAM) how to read and reason. We modeled data...

Chapter 24: Closed-Loop Remediation and Active Eviction

Modern SWI-Prolog (2026 Edition) Second... VOLUME IV: Observability & Telemetry

The Volumes III and IV stack can observe a dying hypervisor with sub-second latency, classify its failure mode with hysteresis-stabilised certainty, propagate a proof of that failure to a distributed cluster of Pengine agents, and route traffic away from it in...

Chapter 25: The Physics of CLP(FD)

Modern SWI-Prolog (2026 Edition) Second... VOLUME V: Constraint Logic & Optimization

Classical Prolog solves constraint problems by generating candidate values and testing them — a strategy that scales with combinatorial brutality on any problem where the variable count exceeds a handful and the domain is large, because every failed test point...

VOLUME V: Constraint Logic & Optimization

Modern SWI-Prolog (2026 Edition) Second...

Chapter 26: The Proxmox Bin Packer

Modern SWI-Prolog (2026 Edition) Second... VOLUME V: Constraint Logic & Optimization

Assigning fifty virtual machines to fourteen hypervisors subject to RAM and CPU capacity constraints is a multi-dimensional, multi-knapsack NP-hard problem — the same combinatorial class that defeats every classical greedy heuristic at scale, and the class for...

Chapter 28: Local LLMs (16GB GPU VRAM Math)

Modern SWI-Prolog (2026 Edition) Second... VOLUME VI: Neuro-Symbolic AI & Local LLMs

The deterministic sovereign stack built across Volumes III through V can prove that pve3 is critical, compute the optimal migration target, and execute the evacuation — but it cannot tell the on-call engineer at 2:47 AM, in plain language, why the ZFS ARC miss...

VOLUME VI: Neuro-Symbolic AI & Local LLMs

Modern SWI-Prolog (2026 Edition) Second...

Chapter 29: Proxmox PCIe Passthrough and AI Inference

Modern SWI-Prolog (2026 Edition) Second... VOLUME VI: Neuro-Symbolic AI & Local LLMs

Running the LLM inference engine directly on the Proxmox host OS — as Chapter 27's llm-inference.service did — conflates the hypervisor's privilege domain with the GPU workload's privilege domain: a CUDA driver panic or an out-of-memory kernel event in the GPU...

Chapter 27: High Availability Constraints

Modern SWI-Prolog (2026 Edition) Second... VOLUME V: Constraint Logic & Optimization

The Chapter 26 bin-packer produces the densest valid assignment of VMs to hypervisors — and in doing so, it actively creates single points of failure: two database replicas that together fill a host's 85% capacity threshold will be placed on the same physical ...

Chapter 30: Fine-Tuning for Infrastructure

Modern SWI-Prolog (2026 Edition) Second... VOLUME VI: Neuro-Symbolic AI & Local LLMs

The sovereign-analyst model from Chapter 29 knows how to format incident runbooks and reason about Proxmox CLI commands, but it has never encountered arc_miss_plus_io_saturated, does not know that pve7 through pve14 are in Rack C on leaf_c, and cannot produce ...

Chapter 31: RAG via Prolog (The Deterministic Context)

Modern SWI-Prolog (2026 Edition) Second... VOLUME VI: Neuro-Symbolic AI & Local LLMs

Standard Retrieval-Augmented Generation embeds past incident documents into a vector database and retrieves the most semantically similar chunks by cosine similarity when a query arrives — but cosine similarity of historical log entries cannot prove that node_...

Chapter 32: Tool Calling & Autonomy

Modern SWI-Prolog (2026 Edition) Second... VOLUME VI: Neuro-Symbolic AI & Local LLMs

The fine-tuned, RAG-grounded analyst from Chapters 30 and 31 can diagnose a degraded cluster and narrate the correct remediation sequence — but narration is not remediation, and a 3 AM hardware failure demands more than a well-worded runbook delivered to an on...

Chapter 33: The Sovereign Codebase

Modern SWI-Prolog (2026 Edition) Second... VOLUME VI: Neuro-Symbolic AI & Local LLMs

The journey from a working prototype to a hardened production system is not a matter of adding features — it is a matter of eliminating failure modes, enforcing physical constraints, and codifying operational discipline into the infrastructure itself. This fin...

Silvan Cluster

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Silvan Cluster

Silvan Cluster VLANs VLAN Infra = 192.168.110.0/24, Gateway = 192.168.110.10 VLAN MGT = 192.168.99.0/24, Gateway = 192.168.99.10 VLAN Terminals = 192.168.111.0/24, Gateway = 192.168.111.1 VLAN Production = 192.168.100.0/24, Gateway = 192.168.100.1 VLAN VPNnet ...