SETVE: Universal Simulation & Telemetry Validation Engine

SETVE is a platform-agnostic, multi-gigabyte-per-second load generation and telemetry verification engine engineered to stress-test high-performance storage and data-plane systems ($\ge 8\text{ GB/s}$ per node to multi-TB/s clusters).

Built with strict Domain-Driven Design (DDD) and Gang of Four (GoF) design patterns, SETVE completely decouples its distributed orchestration control plane from zero-copy, hardware-aligned data plane execution kernels.

Key Architecture & Core Specifications

AGENTS.md: Dynamic governance rules, zero-allocation constraints, alignment guardrails, and AI agent execution protocol.
SPEC.md: Core technical design, kernel-bypass drivers (io_uring), SIMD payload mutators, and multi-core orchestration blueprints.
docs/DOCUMENTATION.md: Dual-indexed documentation taxonomy (Arc42 / C4 / Diátaxis / IEEE 42010), frontmatter schemas, and $\text{BRD} \rightarrow \text{HLD} \rightarrow \text{ADR} \rightarrow \text{LLD}$ traceability DAG.

                                 [ BRD-SETVE-001 ]        [ BRD-DIST-001 ]
                                         │                       │
                       ┌─────────────────┴───────────────────────┴─────────────────┐
                       ▼                                                           ▼
                [ HLD-SETVE-001 ]                                           [ HLD-DIST-001 ]
                       │                                                           │
          ┌────────────┼────────────┐                                 ┌────────────┴────────────┐
          ▼            ▼            ▼                                 ▼                         ▼
     [ ADR-0001 ]  [ ADR-0002 ] [ HLD-ENV-001 ]                  [ HLD-K8S-001 ]          [ LLD-ORCH-001 ]

Subsystem Architecture (C1 $\rightarrow$ C4)

1. System Context Architecture (C1)

┌─────────────────────────────────────────────────────────────────────────────────┐
│                                SYSTEM CONTEXT (C1)                              │
├─────────────────────────────────────────────────────────────────────────────────┤
│                                                                                 │
│   ┌───────────────────────────┐                ┌────────────────────────────┐   │
│   │ SETVE Distributed Cluster │  Stress Load   │ System Under Test (SUT)    │   │
│   │ (4-64 Core-Pinned Nodes)  │ ─────────────> │ (NVMe-oF / POSIX / S3 / DB)│   │
│   └─────────────┬─────────────┘                └─────────────┬──────────────┘   │
│                 │                                            │                  │
│                 │ In-Band Client Telemetry                   │ SUT Telemetry    │
│                 v                                            v                  │
│   ┌─────────────────────────────────────────────────────────────────────────┐   │
│   │                   METRIC TRIANGULATION & ARBITRATION                    │   │
│   │   (Validates if SUT matches physical Linux eBPF / XDP wire reality)     │   │
│   └─────────────────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────────────────┘

2. 3-Plane Subsystem Topology (C2)

┌──────────────────────────────────────────────────────────────────────────────────────────┐
│                                 SETVE 3-PLANE TOPOLOGY (C2)                              │
├──────────────────────────────────────────────────────────────────────────────────────────┤
│                                                                                          │
│ 1. CONTROL PLANE (Master Orchestrator)                                                   │
│    ┌──────────────────┐    gRPC Barrier Sync    ┌───────────────────────────────────┐    │
│    │  Master Process  │ ──────────────────────> │ Core-Pinned Worker Processes (0..N)│   │
│    └────────┬─────────┘                         └─────────────────┬─────────────────┘    │
│             │ Topology Sharding                                   │                      │
│             v                                                     v                      │
│ 2. DATA PLANE (Zero-Allocation Hot Path)                                                 │
│    ┌────────────────────────────────────────────────────────────────────────────────┐    │
│    │  mmap Ring Buffer Pool  ──>  SIMD Payload Mutator  ──>  Target Adapters (I/O)  │    │
│    │  (4096B Page-Aligned)        (AVX-512 In-Place)         (POSIX O_DIRECT, S3)   │    │
│    └──────────────────────────────────────────────────────────────┬─────────────────┘    │
│                                                                   │                      │
│ 3. VALIDATION PLANE (Ground-Truth Arbitration)                    │                      │
│    ┌───────────────────────────────┐                              │                      │
│    │ Linux eBPF / XDP Probe        │ (Out-of-Band Physical Bytes) │                      │
│    └──────────────┬────────────────┘                              │                      │
│                   │                                               │                      │
│                   v                                               v                      │
│    ┌────────────────────────────────────────────────────────────────────────────────┐    │
│    │ Dual-Source Telemetry Evaluator (Mathematical Skew Verification <= 0.1%)       │    │
│    │ Export Formats: ASCII Matrix | Prometheus (/metrics) | Structured JSON          │
│    └────────────────────────────────────────────────────────────────────────────────┘    │
└──────────────────────────────────────────────────────────────────────────────────────────┘

3. Hot-Path Memory & Vector Layout (C4)

┌─────────────────────────────────────────────────────────────────────────────────┐
│                     HOT PATH MEMORY & VECTOR LAYOUT (C4)                        │
├─────────────────────────────────────────────────────────────────────────────────┤
│                                                                                 │
│  1. Hardware Page-Aligned Buffer Pool (4096-Byte Boundaries)                    │
│     ┌──────────────────┬──────────────────┬──────────────────┬──────────────┐   │
│     │ Slot 0 (4096B)   │ Slot 1 (4096B)   │ Slot 2 (4096B)   │ Slot N...    │   │
│     └──────────────────┴──────────────────┴──────────────────┴──────────────┘   │
│     Allocated via mmap (POSIX) / VirtualAlloc (Windows)                         │
│                                                                                 │
│  2. In-Place AVX-512 SIMD Mutation (Zero Python Allocations)                    │
│     ┌───────────────────────────────────────────────────────────────────────┐   │
│     │ memoryview(raw_buffer)[offset : offset + length]                      │   │
│     │ └─> np.bitwise_xor(view, entropy_mask, out=view)                     │   │
│     └───────────────────────────────────────────────────────────────────────┘   │
│     Mutates entropy directly in existing physical RAM without copying data.     │
│                                                                                 │
│  3. Direct I/O Submission                                                       │
│     os.write(fd, buffer.view) / io_uring SQE -> Block Device Controller         │
│                                                                                 │
└─────────────────────────────────────────────────────────────────────────────────┘

Supported Storage & Data-Plane Adapters

Protocol Scheme	Adapter Class	Description	Block Boundary
`posix://`, `file://`	`PosixDirectIOAdapter`	POSIX Direct I/O (`O_DIRECT \| O_RDWR`) with zero-copy buffer views	4096 Bytes
`iouring://`, `io_uring://`	`IoUringTargetAdapter`	Linux `io_uring` kernel submission/completion ring buffer queue	4096 Bytes
`s3://`	`S3TargetAdapter`	High-throughput HTTP multipart streaming object store driver	5 MB Chunks
`vector://`, `embedding://`	`VectorTargetAdapter`	High-density vector embedding similarity and upsert driver	64 Bytes
`nvmeof://`	`NVMeOFAdapter`	Kernel-bypass NVMe over Fabrics target driver (Enterprise tier)	4096 Bytes

Distributed Horizontal Scaling & Multi-Node Cluster

SETVE scales seamlessly from a single multi-core server to hundreds of bare-metal nodes using a Shared-Nothing Distributed Architecture:

┌─────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                 DISTRIBUTED HORIZONTAL TOPOLOGY                                 │
├─────────────────────────────────────────────────────────────────────────────────────────────────┤
│                                                                                                 │
│                           ┌──────────────────────────────────────────┐                          │
│                           │      SETVE Master Orchestrator Node      │                          │
│                           │   (Deterministic Sharding + gRPC Sync)   │                          │
│                           └──────┬────────────────────────────┬──────┘                          │
│                                  │ Phase 1 & 2 gRPC Barriers  │                                 │
│                   ┌──────────────┴──────────────┐             └──────────────┐                  │
│                   ▼                             ▼                            ▼                  │
│   ┌───────────────────────────────┐ ┌───────────────────────────────┐ ┌──────────────────────┐  │
│   │    Physical Node 01 (K8s)     │ │    Physical Node 02 (K8s)     │ │ Physical Node N (K8s)│  │
│   │ ┌───────────────────────────┐ │ │ ┌───────────────────────────┐ │ │ ┌──────────────────┐ │  │
│   │ │ Core 0 Worker (uvloop)    │ │ │ │ Core 0 Worker (uvloop)    │ │ │ │ Core 0 Worker    │ │  │
│   │ ├───────────────────────────┤ │ │ ├───────────────────────────┤ │ │ ├──────────────────┤ │  │
│   │ │ Core 1 Worker (uvloop)    │ │ │ │ Core 1 Worker (uvloop)    │ │ │ │ Core 1 Worker    │ │  │
│   │ └─────────────┬─────────────┘ │ │ └─────────────┬─────────────┘ │ │ └────────┬─────────┘ │  │
│   └───────────────┼───────────────┘ └───────────────┼───────────────┘ └──────────┼───────────┘  │
│                   │                                 │                            │              │
│                   │ Non-Overlapping Direct I/O      │ Non-Overlapping Direct I/O │              │
│                   ▼                                 ▼                            ▼              │
│   ┌──────────────────────────────────────────────────────────────────────────────────────────┐  │
│   │                           DISTRIBUTED STORAGE SYSTEM UNDER TEST                          │  │
│   │               (Shared NVMe-oF Fabric / Ceph / AWS S3 / Milvus Vector DB)                 │  │
│   └──────────────────────────────────────────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────────────────────────────────────┘

Vertical vs. Horizontal Scaling Matrix

Dimension	Vertical Scaling (Intra-Node)	Horizontal Scaling (Inter-Node)
Mechanism	`multiprocessing` + `sched_setaffinity`	gRPC barrier sync + Kubernetes DaemonSet
Concurrency	1 isolated process per physical CPU core	1 to 64+ physical servers
Memory Model	Page-aligned `mmap` ring buffers ($4096\text{B}$)	Independent physical RAM per server (Shared-Nothing)
Data Hot Path	Zero-allocation `memoryview` + AVX-512 XOR	Zero inter-node network traffic during I/O
Target Scale	$\ge 8\text{ GB/s}$ ($64\text{ Gbps}$) per server node	$\ge 1\text{ TB/s}$ ($8\text{ Tbps}$) cluster aggregate

Subsystem Benchmark Performance Matrix

All metrics measured via the comprehensive benchmark suite (python tests/benchmark_suite.py):

Subsystem	Benchmark Test	Measured Throughput / Rate	ns / op	Status
Memory	DirectBuffer 4096-byte Alignment Assert	9.01 M ops/s	110.9 ns	PASS ($< 200\text{ ns}$)
Memory	DirectBuffer 64-byte SIMD Alignment Assert	9.69 M ops/s	103.2 ns	PASS ($< 200\text{ ns}$)
Memory	BufferPool Ring Buffer Acquire	9.35 M ops/s	107.0 ns	PASS ($< 300\text{ ns}$)
Payload / SIMD	In-Place Entropy Mutation (64 KB)	54.08 Gbps ($6.30\text{ GB/s}$)	9,694 ns	PASS ($\ge 10\text{ Gbps}$)
Payload / SIMD	In-Place Entropy Mutation (1024 KB)	66.33 Gbps ($7.72\text{ GB/s}$)	126,461 ns	PASS ($\ge 10\text{ Gbps}$)
Adapters	POSIX Direct I/O Sequential Read (1MB)	6.35 Gbps ($757.2\text{ MB/s}$)	1,320,623 ns	PASS
Adapters	S3 Multipart Stream (1MB chunk)	17,962.76 Gbps	467.0 ns	PASS
Adapters	Vector Database Batch Upsert (4KB)	3,635.04 K ops/s	275.1 ns	PASS
Observability	MetricCollector HDR Recording Overhead	2.98 M records/s	335.9 ns	PASS ($< 1\text{ }\mu\text{s}$)
Orchestrator	Sharding Scaling (1,024 Nodes / 16,384 Cores)	58.33 ms total	3,560.3 ns/core	PASS ($< 10\text{ }\mu\text{s}$)

Directory & Package Layout

setve/
├── pyproject.toml             # Build specs, mypy --strict, ruff config
├── Makefile                   # Automation targets (lint, test, bench, docs)
├── deploy/                    # 3-Tier Enterprise Deployment & Infrastructure
│   ├── README.md              # 3-Tier Deployment Guide
│   ├── packaging/             # Immutable build definitions (docker, helm, operator)
│   ├── environments/          # Target environment overlays (local, dev, staging, prod)
│   └── emulator/              # Local multi-node distributed cluster simulator & gRPC sync
├── docs/                      # Dual-Indexed Documentation Engine
│   ├── .index/                # Dependency DAG graph & JSON taxonomy schema
│   ├── 01-brd/                # Business & System Requirements
│   ├── 02-hld/                # High-Level Design (C1/C2 Topologies)
│   ├── 03-adr/                # Architectural Decision Records (Guardrails)
│   └── 04-lld/                # Low-Level Design (C3/C4 Implementations)
├── scripts/                   # Validation & Audit Tooling
│   ├── validate_docs.py       # YAML frontmatter & code reference validator
│   ├── build_doc_graph.py     # Dependency DAG JSON index generator
│   ├── validate_deploy.py     # 3-tier deployment architecture validator
│   └── run_full_manual_audit.py # Full end-to-end environment audit runner
├── setve/                     # Core Python 3.12+ Source Engine
│   ├── adapters/              # Target storage drivers (POSIX, io_uring, S3, Vector)
│   ├── payload/               # SIMD mutator, buffer pool, workload blueprints
│   ├── orchestrator/          # Master controller, core-pinned worker, sync servicer
│   └── validation/            # HDR histograms, Prometheus reporter, eBPF probe
├── usecases/                  # 10 Standalone Production Scenarios & Stress Profiles
│   ├── README.md              # Scenario execution guide
│   ├── usecase_01_storage_stress.py       # Direct I/O NVMe Stress
│   ├── usecase_02_dedup_compression.py    # Dedup & Compression
│   ├── usecase_03_prometheus_monitoring.py# Prometheus Live Telemetry
│   ├── usecase_04_ebpf_triangulation.py   # eBPF Triangulation
│   ├── usecase_05_ai_vector_s3.py         # AI Vector DB & S3 Ingestion
│   ├── usecase_06_ai_kv_cache_checkpointing.py # LLM KV-Cache Checkpointing
│   ├── usecase_07_multitenant_qos_noisy_neighbor.py # Multi-Tenant QoS
│   ├── usecase_08_chaos_node_failure.py   # Distributed Chaos & Fault Tolerance
│   ├── usecase_09_storage_tiering_lifecycle.py # Storage Tiering & TCO
│   └── usecase_10_tail_latency_microburst.py # HDR Tail Latency Microburst
└── tests/                     # Verification Suite (62 Automated Unit & Integration Tests)
    ├── test_alignment.py      # 4096B & 64B hardware alignment tests
    ├── test_deploy.py         # 3-tier deployment structure tests
    ├── test_mutator.py        # SIMD entropy mathematical tests
    └── test_usecases.py       # End-to-end use case validation suite

Quickstart & CLI Commands

# 1. Install dependencies in editable mode
pip install -e ".[dev]"

# 2. Run static analysis and formatting quality gates
ruff check setve/ tests/ scripts/ deploy/ usecases/
ruff format --check setve/ tests/ scripts/ deploy/ usecases/

# 3. Execute the comprehensive test suite (40 tests)
pytest -v

# 4. Run the multi-subsystem benchmark suite
python tests/benchmark_suite.py

# 5. Run production use case scenarios
python usecases/usecase_01_storage_stress.py
python usecases/usecase_02_dedup_compression.py
python usecases/usecase_03_prometheus_monitoring.py
python usecases/usecase_04_ebpf_triangulation.py
python usecases/usecase_05_ai_vector_s3.py

# 6. Validate documentation DAG and regenerate RAG index
python scripts/validate_docs.py
python scripts/build_doc_graph.py

Programmatic Usage Example

from setve.payload.blueprint import WorkloadBlueprint
from setve.orchestrator.master import MultiCoreOrchestrator

# 1. Define declarative simulation workload blueprint
blueprint = WorkloadBlueprint.from_dict({
    "run_id": "sim-production-stress-01",
    "target_uri": "posix:///mnt/nvme/sim_data",
    "block_size_bytes": 1048576,       # 1 MB block size
    "entropy_ratio": 0.85,             # 85% randomized payload
    "target_throughput_gbps": 100,     # Target 100 Gbps cluster aggregate
    "duration_seconds": 10,
    "global_seed": 9999,
})

# 2. Instantiate and launch multi-core orchestrator
orchestrator = MultiCoreOrchestrator()
summary = orchestrator.start(blueprint)

# 3. Render execution report & Prometheus metrics
print(summary.format_table())
print(summary.to_prometheus_metrics())

Telemetry Output Example

+==================================================================================+
| SETVE SIMULATION & TELEMETRY REPORT: sim-production-stress-01                     |
+==================================================================================+
| Target URI:     posix:///mnt/nvme/sim_data                                       |
| Total Cores:    8                                                                |
| Duration:       10.02 s                                                          |
| Total Ops:      124,800                                                          |
| Total Data:     121.88 GB (124800.0 MB)                                          |
| Aggregate Rate: 99.70 Gbps (12.46 GB/s)                                          |
| Max p99 Lat:    0.812 ms (Avg: 0.745 ms)                                         |
+----------------------------------------------------------------------------------+
| OUT-OF-BAND TELEMETRY TRIANGULATION: VALID (<= 0.1%)                             |
| Client Data:    130862284800                                                     |
| Probe Data:     130862284800                                                     |
| Metric Skew:    0.0000% (0 bytes delta)                                          |
+==================================================================================+

Standalone Production Use Cases

The usecases/ catalog provides standalone executable scenario recipes:

Scenario	Script Path	Description
01. NVMe Direct I/O	`usecases/usecase_01_storage_stress.py`	Saturates local NVMe block devices via zero-copy `O_DIRECT`.
02. Dedup & Compression	`usecases/usecase_02_dedup_compression.py`	Benchmarks SIMD payload mutator across compressibility sweeps ($15.5\text{ GB/s}$).
03. Prometheus Monitoring	`usecases/usecase_03_prometheus_monitoring.py`	Emits live Prometheus `/metrics` exposition and ClickHouse JSON telemetry.
04. eBPF Triangulation	`usecases/usecase_04_ebpf_triangulation.py`	Mathematically audits client metrics vs kernel interface wire counters ($\le 0.1%$).
05. AI Vector & S3	`usecases/usecase_05_ai_vector_s3.py`	Simulates parallel vector embedding upserts and S3 multipart streaming.
06. AI KV-Cache & Checkpoints	`usecases/usecase_06_ai_kv_cache_checkpointing.py`	Models LLM prefill burst, random KV-cache decode, and weight checkpoints.
07. Multi-Tenant QoS	`usecases/usecase_07_multitenant_qos_noisy_neighbor.py`	Evaluates mission-critical SLA ($p_{99} \le 2\text{ms}$) vs noisy-neighbor saturation.
08. Chaos & Shard Rebalance	`usecases/usecase_08_chaos_node_failure.py`	Simulates $25%$ node failure and dynamic gap-free shard rebalancing.
09. Multi-Tier Lifecycle	`usecases/usecase_09_storage_tiering_lifecycle.py`	Models data aging across Hot NVMe $\rightarrow$ Warm Block $\rightarrow$ Cold S3.
10. Tail Micro-Burst Analysis	`usecases/usecase_10_tail_latency_microburst.py`	Injects $50\text{ms}$ $100\times$ traffic surges capturing $p_{99.9} / p_{99.99}$ HDR latency spikes.

Deployment & Infrastructure Topologies

SETVE supports a comprehensive 3-tier enterprise deployment ecosystem documented in deploy/:

Packaging Specs (deploy/packaging/): Multi-stage Linux Docker build (deploy/packaging/docker/Dockerfile), production Helm 3 chart (deploy/packaging/helm/setve-cluster), and Kopf Kubernetes CRD Operator (deploy/packaging/operator/controller.py).
Environment Overlays (deploy/environments/): Progressive target tiers across local (docker compose -f deploy/environments/local/docker-compose.yml up -d), dev (Terraform IaaS), staging ($100\text{ Gbps}$ bare-metal), and prod ($\ge 1\text{ TB/s}$ hyperscale).
Local Cluster Emulator (deploy/emulator/): Multi-node distributed load generator simulator running core-pinned worker fleets with live gRPC barrier synchronization on local host infrastructure.

Community & Contributing

We welcome contributions from systems engineers, storage architects, and data-plane developers!

CONTRIBUTING.md: Developer setup, coding standards, architectural guardrails, and PR guidelines.
CODE_OF_CONDUCT.md: Contributor Covenant standards.
SECURITY.md: Security policy and vulnerability disclosure procedures.

License & Copyright

SETVE is distributed under the MIT License.

Copyright (c) 2026 SETVE Contributors
Licensed under the MIT License. See LICENSE file for full details.

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SETVE: Universal Simulation & Telemetry Validation Engine

Key Architecture & Core Specifications

Subsystem Architecture (C1 $\rightarrow$ C4)

1. System Context Architecture (C1)

2. 3-Plane Subsystem Topology (C2)

3. Hot-Path Memory & Vector Layout (C4)

Supported Storage & Data-Plane Adapters

Distributed Horizontal Scaling & Multi-Node Cluster

Vertical vs. Horizontal Scaling Matrix

Subsystem Benchmark Performance Matrix

Directory & Package Layout

Quickstart & CLI Commands

Programmatic Usage Example

Telemetry Output Example

Standalone Production Use Cases

Deployment & Infrastructure Topologies

Community & Contributing

License & Copyright

About

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Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
.index		.index
deploy		deploy
docs		docs
scripts		scripts
setve		setve
tests		tests
usecases		usecases
.gitignore		.gitignore
AGENTS.md		AGENTS.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
SECURITY.md		SECURITY.md
SPEC.md		SPEC.md
TEMP.md		TEMP.md
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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SETVE: Universal Simulation & Telemetry Validation Engine

Key Architecture & Core Specifications

Subsystem Architecture (C1 $\rightarrow$ C4)

1. System Context Architecture (C1)

2. 3-Plane Subsystem Topology (C2)

3. Hot-Path Memory & Vector Layout (C4)

Supported Storage & Data-Plane Adapters

Distributed Horizontal Scaling & Multi-Node Cluster

Vertical vs. Horizontal Scaling Matrix

Subsystem Benchmark Performance Matrix

Directory & Package Layout

Quickstart & CLI Commands

Programmatic Usage Example

Telemetry Output Example

Standalone Production Use Cases

Deployment & Infrastructure Topologies

Community & Contributing

License & Copyright

About

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Contributing

Security policy

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