diff --git a/asyncflow_queue_limit/asyncflow_mm1_notebook.ipynb b/asyncflow_queue_limit/asyncflow_mm1.ipynb
similarity index 99%
rename from asyncflow_queue_limit/asyncflow_mm1_notebook.ipynb
rename to asyncflow_queue_limit/asyncflow_mm1.ipynb
index 4d02abb..5642ee4 100644
--- a/asyncflow_queue_limit/asyncflow_mm1_notebook.ipynb
+++ b/asyncflow_queue_limit/asyncflow_mm1.ipynb
@@ -93,12 +93,10 @@
"\n",
"```mermaid\n",
"graph LR;\n",
- " %% Definiamo i nodi con i loro dettagli\n",
" rqs1[\"RqsGenerator
id: rqs-1\"]\n",
" client1[\"Client
id: client-1\"]\n",
" app1[\"Server
id: app-1
Endpoint: /api\"]\n",
"\n",
- " %% Definiamo le connessioni (edge) con latenza\n",
" rqs1 -- \"Edge: gen-client
Latency: 0.0001\" --> client1;\n",
" client1 -- \"Request
Edge: client-app
Latency: 0.0001\" --> app1;\n",
" app1 -- \"Response
Edge: app-client
Latency: 0.0001\" --> client1;"
@@ -106,7 +104,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {
"tags": [
"build"
@@ -148,7 +146,7 @@
" e_app_client = Edge(id=\"app-client\", source=\"app-1\", target=\"client-1\", latency=0.0001, dropout_rate=0.0)\n",
"\n",
" settings = SimulationSettings(\n",
- " total_simulation_time=900,\n",
+ " total_simulation_time=2400,\n",
" sample_period_s=0.05,\n",
" )\n",
"\n",
@@ -160,9 +158,7 @@
" .add_edges(e_gen_client, e_client_app, e_app_client)\n",
" .add_simulation_settings(settings)\n",
" ).build_payload()\n",
- " return payload\n",
- "\n",
- "payload = build_payload()\n"
+ " return payload\n"
]
},
{
@@ -174,7 +170,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {
"tags": [
"run"
@@ -190,6 +186,7 @@
}
],
"source": [
+ "payload = build_payload()\n",
"env = simpy.Environment()\n",
"runner = SimulationRunner(env=env, simulation_input=payload)\n",
"results: ResultsAnalyzer = runner.run()\n",
@@ -306,7 +303,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 28,
"metadata": {
"tags": [
"mm1"
@@ -322,13 +319,13 @@
"--------------------------------------------------------------------\n",
"sym metric theory observed abs rel%\n",
"--------------------------------------------------------------------\n",
- "λ Arrival rate (1/s) 33.333333 33.204444 -0.128889 -0.39\n",
- "μ Service rate (1/s) 66.666667 66.638004 -0.028663 -0.04\n",
- "rho Utilization 0.500000 0.498281 -0.001719 -0.34\n",
- "L Mean items in system 1.000000 1.034536 0.034536 3.45\n",
- "Lq Mean items in queue 0.500000 0.526294 0.026294 5.26\n",
- "W Mean time in system (s) 0.030000 0.031157 0.001157 3.86\n",
- "Wq Mean waiting time (s) 0.015000 0.015850 0.000850 5.67\n",
+ "λ Arrival rate (1/s) 33.333333 33.150833 -0.182500 -0.55\n",
+ "μ Service rate (1/s) 66.666667 66.556885 -0.109782 -0.16\n",
+ "rho Utilization 0.500000 0.498083 -0.001917 -0.38\n",
+ "L Mean items in system 1.000000 1.012994 0.012994 1.30\n",
+ "Lq Mean items in queue 0.500000 0.504967 0.004967 0.99\n",
+ "W Mean time in system (s) 0.030000 0.030557 0.000557 1.86\n",
+ "Wq Mean waiting time (s) 0.015000 0.015232 0.000232 1.55\n",
"====================================================================\n"
]
}
diff --git a/asyncflow_queue_limit/asyncflow_mmc_split.ipynb b/asyncflow_queue_limit/asyncflow_mmc_split.ipynb
new file mode 100644
index 0000000..b042a1b
--- /dev/null
+++ b/asyncflow_queue_limit/asyncflow_mmc_split.ipynb
@@ -0,0 +1,571 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "870337dc",
+ "metadata": {},
+ "source": [
+ "# AsyncFlow — MMc Theory vs Simulation (Guided Notebook)\n",
+ "\n",
+ "This notebook shows how to:\n",
+ "\n",
+ "1. Make imports work inside a notebook (src-layout or package install)\n",
+ "2. Build a **multi-server** scenario compatible with **M/M/c** assumptions\n",
+ "3. Run the simulation and collect results\n",
+ "4. Compare theory vs observed KPIs (pretty-printed table)\n",
+ "5. Plot the standard dashboards (latency, throughput, server time series)\n",
+ "\n",
+ "> Tip: run this notebook from your project **root folder**.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b8a94d93",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys, importlib\n",
+ "\n",
+ "# 1) Svuota tutto ciò che inizia con 'asyncflow' da sys.modules\n",
+ "for m in list(sys.modules):\n",
+ " if m.startswith(\"asyncflow\"):\n",
+ " del sys.modules[m]\n",
+ "\n",
+ "# 2) Re-importa SOLO le facciate pubbliche (niente import profondi)\n",
+ "from asyncflow import AsyncFlow, SimulationRunner\n",
+ "from asyncflow.analysis import MMc, ResultsAnalyzer\n",
+ "from asyncflow.components import (\n",
+ " Client, Server, Edge, Endpoint, LoadBalancer\n",
+ ")\n",
+ "from asyncflow.settings import SimulationSettings\n",
+ "from asyncflow.workload import RqsGenerator\n",
+ "import simpy\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "d1b7ad7d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Imports OK.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import simpy\n",
+ "\n",
+ "# Public AsyncFlow API\n",
+ "from asyncflow import AsyncFlow, SimulationRunner, Sweep\n",
+ "from asyncflow.components import Client, Server, Edge, Endpoint, LoadBalancer\n",
+ "from asyncflow.settings import SimulationSettings\n",
+ "from asyncflow.workload import RqsGenerator\n",
+ "from asyncflow.analysis import MM1, ResultsAnalyzer, SweepAnalyzer, MMc\n",
+ "\n",
+ "print(\"Imports OK.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d632e4fd",
+ "metadata": {},
+ "source": [
+ "## 1) Build an M/M/c split-friendly scenario\n",
+ "\n",
+ "* **Multiple identical servers with exponential CPU service**\n",
+ " Topology includes **\\$c \\geq 2\\$ identical servers**, each exposing exactly **one endpoint** with exactly **one CPU-bound step**.\n",
+ " Service times follow an **Exponential** distribution with mean \\$E\\[S]\\$ (service rate \\$\\mu = 1/E\\[S]\\$). No RAM/IO steps are included in the pipeline.\n",
+ "\n",
+ "* **Load balancer with round-robin dispatch**\n",
+ " A **single load balancer** is required when \\$c > 1\\$. It splits arrivals **round-robin** across servers, so each server has its own local queue.\n",
+ " This corresponds to a **split M/M/c** model, not the textbook pooled queue.\n",
+ "\n",
+ "* **Deterministic, very small network latency**\n",
+ " All edges have **fixed latency** \\$\\ll 1,\\mathrm{ms}\\$. Queueing behavior is therefore dominated by CPU service, closely matching textbook assumptions.\n",
+ "\n",
+ "* **“Poisson arrivals” via the generator**\n",
+ " Arrivals are produced by the same **two-stage, windowed Poisson sampler**: in each user-sampling window \\$\\Delta\\$, we draw the active users \\$U\\$ (Poisson or Normal, per config).\n",
+ " Within the window, arrivals are a **homogeneous Poisson process** with rate \\$\\Lambda = U \\cdot \\lambda\\_r/60\\$.\n",
+ "\n",
+ " With **small \\$\\Delta\\$**, **Poisson users**, **long runs**, and **tiny edge latency**, the aggregate arrivals seen by the load balancer approximate a global Poisson input, yielding a good empirical match to the M/M/c model.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "```mermaid\n",
+ "graph LR;\n",
+ " rqs1[\"RqsGenerator
id: rqs-1\"]\n",
+ " client1[\"Client
id: client-1\"]\n",
+ " lb1[\"LoadBalancer
id: lb-1
Policy: round_robin\"]\n",
+ " app1[\"Server
id: app-1
Endpoint: /api\"]\n",
+ " app2[\"Server
id: app-2
Endpoint: /api\"]\n",
+ "\n",
+ " rqs1 -- \"Edge: gen-client
Latency: 0.0001\" --> client1;\n",
+ " client1 -- \"Request
Edge: client-lb
Latency: 0.0001\" --> lb1;\n",
+ " lb1 -- \"Dispatch
Edge: lb-app1
Latency: 0.0001\" --> app1;\n",
+ " lb1 -- \"Dispatch
Edge: lb-app2
Latency: 0.0001\" --> app2;\n",
+ " app1 -- \"Response
Edge: app1-client
Latency: 0.0001\" --> client1;\n",
+ " app2 -- \"Response
Edge: app2-client
Latency: 0.0001\" --> client1;\n",
+ "```\n",
+ "\n",
+ "---\n",
+ "\n",
+ "⚠️ **Note on model scope**\n",
+ "This scenario currently represents a **split M/M/c with random dispatch**.\n",
+ "The **textbook M/M/c (Erlang-C)** assumes a **single pooled FCFS queue feeding c servers**, which tends to give lower waiting times (no imbalance across local queues).\n",
+ "\n",
+ "In a future step, we will extend AsyncFlow with a **pooled FCFS dispatcher** at the load balancer, enabling direct comparison against the textbook Erlang-C closed forms.\n",
+ "\n",
+ "---\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "ba93587a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_payload():\n",
+ " generator = RqsGenerator(\n",
+ " id=\"rqs-1\",\n",
+ " avg_active_users={\"mean\": 120},\n",
+ " avg_request_per_minute_per_user={\"mean\": 20},\n",
+ " user_sampling_window=60,\n",
+ " )\n",
+ "\n",
+ " client = Client(id=\"client-1\")\n",
+ "\n",
+ " endpoint = Endpoint(\n",
+ " endpoint_name=\"/api\",\n",
+ " probability=1.0,\n",
+ " steps=[\n",
+ " {\n",
+ " \"kind\": \"initial_parsing\",\n",
+ " \"step_operation\": {\n",
+ " \"cpu_time\": {\"mean\": 0.01, \"distribution\": \"exponential\"},\n",
+ " },\n",
+ " },\n",
+ " ],\n",
+ " )\n",
+ "\n",
+ " srv1 = Server(\n",
+ " id=\"srv-1\",\n",
+ " server_resources={\"cpu_cores\": 1, \"ram_mb\": 2048},\n",
+ " endpoints=[endpoint],\n",
+ " )\n",
+ " srv2 = Server(\n",
+ " id=\"srv-2\",\n",
+ " server_resources={\"cpu_cores\": 1, \"ram_mb\": 2048},\n",
+ " endpoints=[endpoint],\n",
+ " )\n",
+ "\n",
+ " lb = LoadBalancer(\n",
+ " id=\"lb-1\",\n",
+ " algorithms=\"random\", \n",
+ " server_covered={\"srv-1\", \"srv-2\"},\n",
+ " )\n",
+ "\n",
+ " edges = [\n",
+ " Edge(id=\"gen-client\", source=\"rqs-1\", target=\"client-1\", latency=0.00001, dropout_rate=0),\n",
+ " Edge(id=\"client-lb\", source=\"client-1\", target=\"lb-1\", latency=0.00001, dropout_rate=0),\n",
+ " Edge(id=\"lb-srv1\", source=\"lb-1\", target=\"srv-1\", latency=0.00001, dropout_rate=0),\n",
+ " Edge(id=\"lb-srv2\", source=\"lb-1\", target=\"srv-2\", latency=0.00001, dropout_rate=0),\n",
+ " Edge(id=\"srv1-client\", source=\"srv-1\", target=\"client-1\", latency=0.00001, dropout_rate=0),\n",
+ " Edge(id=\"srv2-client\", source=\"srv-2\", target=\"client-1\", latency=0.00001, dropout_rate=0),\n",
+ " ]\n",
+ "\n",
+ " settings = SimulationSettings(\n",
+ " total_simulation_time=2400,\n",
+ " sample_period_s=0.05,\n",
+ " )\n",
+ "\n",
+ " payload = (\n",
+ " AsyncFlow()\n",
+ " .add_generator(generator)\n",
+ " .add_client(client)\n",
+ " .add_servers(srv1, srv2)\n",
+ " .add_load_balancer(lb)\n",
+ " .add_edges(*edges)\n",
+ " .add_simulation_settings(settings)\n",
+ " ).build_payload()\n",
+ "\n",
+ " return payload\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ac2e1687",
+ "metadata": {},
+ "source": [
+ "## 2) Run the simulation\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "79b4e0e2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "payload = build_payload()\n",
+ "env = simpy.Environment()\n",
+ "runner = SimulationRunner(env=env, simulation_input=payload)\n",
+ "results: ResultsAnalyzer = runner.run()\n",
+ "print(\"Done.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da98b8b9",
+ "metadata": {},
+ "source": [
+ "## 3) MMc (Round-Robin) — theory vs observed comparison\n",
+ "\n",
+ "If the payload violates MMc assumptions, a readable error is shown instead.\n",
+ "This section matches exactly what the analyzer computes **now**: a **split random model** (not the pooled FCFS/Erlang-C model). When we add **FCFS** in the LB, we’ll also expose the textbook Erlang-C formulas side-by-side.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## Variables (what they represent)\n",
+ "\n",
+ "* **$c$**: number of *identical* servers (parallel replicas).\n",
+ "* **$\\lambda$**: global **arrival rate** (req/s).\n",
+ "* **$\\mu$**: **per-server** service rate (req/s) $= 1/\\mathbb{E}[S]$.\n",
+ "* **$\\rho$**: **global utilization**, $\\rho = \\lambda/(c\\,\\mu)$ (unitless).\n",
+ "* **$W$**: **mean time in system** (queue + service), seconds.\n",
+ "* **$W_q$**: **mean waiting time in queue**, seconds.\n",
+ "* **$L$**: **mean number in system** (queue + service), unitless.\n",
+ "* **$L_q$**: **mean number in queue**, unitless.\n",
+ "* **$\\mathbb{E}[S]$**: mean **CPU service time**, seconds.\n",
+ "\n",
+ "Derived (random split model):\n",
+ "\n",
+ "* **$\\lambda_i$**: **per-server arrival rate**, $\\lambda_i = \\lambda/c$.\n",
+ "\n",
+ "> In the comparison table you’ll see two columns: **Theory** (closed-form) and **Observed** (estimates from the run).\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## How we compute the **Theory** column (MMc with Round-Robin split)\n",
+ "\n",
+ "1. **Predicted arrival rate**\n",
+ "\n",
+ "$$\n",
+ "\\lambda_{\\text{Theory}} \\;=\\; \n",
+ "\\frac{\\texttt{avg\\_active\\_users.mean}\\times\n",
+ " \\texttt{avg\\_request\\_per\\_minute\\_per\\_user.mean}}{60}\n",
+ "$$\n",
+ "\n",
+ "2. **Predicted service rate** (from the **CPU exponential step**)\n",
+ "\n",
+ "$$\n",
+ "\\mu_{\\text{Theory}} \\;=\\; \\frac{1}{\\mathbb{E}[S]}\n",
+ "$$\n",
+ "\n",
+ "3. **Parallelism & utilization**\n",
+ "\n",
+ "$$\n",
+ "c \\;=\\; \\text{number of servers}, \n",
+ "\\qquad\n",
+ "\\rho_{\\text{Theory}} \\;=\\; \\frac{\\lambda_{\\text{Theory}}}{c\\,\\mu_{\\text{Theory}}}\n",
+ "$$\n",
+ "\n",
+ "4. **RR split closed forms** (used by the analyzer today)\n",
+ "\n",
+ "If $\\rho_{\\text{Theory}} \\ge 1$: the system is **unstable** and\n",
+ "$W, W_q, L, L_q$ **diverge** (displayed as $+\\infty$).\n",
+ "\n",
+ "Otherwise, let $\\lambda_i = \\lambda_{\\text{Theory}}/c$. We use:\n",
+ "\n",
+ "$$\n",
+ "\\begin{aligned}\n",
+ "W_{q,\\text{Theory}} &= \\frac{\\rho_{\\text{Theory}}}\n",
+ " {\\mu_{\\text{Theory}} - \\lambda_i} \\\\\n",
+ "W_{\\text{Theory}} &= \\frac{1}{\\mu_{\\text{Theory}}} + W_{q,\\text{Theory}} \\\\\n",
+ "L_{q,\\text{Theory}} &= \\lambda_{\\text{Theory}} \\, W_{q,\\text{Theory}} \\\\\n",
+ "L_{\\text{Theory}} &= \\lambda_{\\text{Theory}} \\, W_{\\text{Theory}}\n",
+ "\\end{aligned}\n",
+ "$$\n",
+ "\n",
+ "> 🔎 **Note:** These formulas reflect a **random split** into *c* identical M/M/1 queues (no central pool). They are **not** the Erlang-C (pooled FCFS) formulas. Once we add **FCFS** at the LB, we’ll surface the **textbook pooled M/M/c** KPIs (including $P_0$, $P_w$, etc.) alongside this random split model.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## How we compute the **Observed** column (from the run)\n",
+ "\n",
+ "After `ResultsAnalyzer.process_all_metrics()`:\n",
+ "\n",
+ "1. **Observed arrival rate** (system throughput)\n",
+ "\n",
+ "$$\n",
+ "\\lambda_{\\text{Observed}} \\;=\\; \n",
+ "\\text{mean}\\big(\\text{windowed RPS series (client completions)}\\big)\n",
+ "$$\n",
+ "\n",
+ "*(This is end-to-end, i.e., what exits all servers and reaches the client.)*\n",
+ "\n",
+ "2. **Observed time in system** (client E2E latency)\n",
+ "\n",
+ "$$\n",
+ "W_{\\text{Observed}} \\;=\\; \\text{mean}\\big(\\text{client latencies}\\big)\n",
+ "$$\n",
+ "\n",
+ "3. **Observed service rate** (aggregate across servers)\n",
+ " Let $\\overline{S} = \\text{mean}(\\texttt{service\\_time})$ *over all servers, weighted by number of jobs*:\n",
+ "\n",
+ "$$\n",
+ "\\mu_{\\text{Observed}} \\;=\\;\n",
+ "\\begin{cases}\n",
+ "1/\\overline{S}, & \\overline{S} > 0 \\\\\n",
+ "+\\infty, & \\overline{S} = 0\n",
+ "\\end{cases}\n",
+ "$$\n",
+ "\n",
+ "4. **Observed waiting time in queue** (aggregate across servers)\n",
+ "\n",
+ "$$\n",
+ "W_{q,\\text{Observed}} \\;=\\; \n",
+ "\\text{mean}\\big(\\texttt{waiting\\_time}\\big)\n",
+ "$$\n",
+ "\n",
+ "5. **Little’s law (observed)**\n",
+ "\n",
+ "$$\n",
+ "L_{\\text{Observed}}=\\lambda_{\\text{Observed}}\\, W_{\\text{Observed}},\n",
+ "\\qquad\n",
+ "L_{q,\\text{Observed}}=\\lambda_{\\text{Observed}}\\, W_{q,\\text{Observed}}\n",
+ "$$\n",
+ "\n",
+ "6. **Observed utilization (global)**\n",
+ "\n",
+ "$$\n",
+ "\\rho_{\\text{Observed}} \\;=\\;\n",
+ "\\begin{cases}\n",
+ "\\lambda_{\\text{Observed}} / (c\\,\\mu_{\\text{Observed}}),\n",
+ " & \\mu_{\\text{Observed}} \\not\\in \\{0, +\\infty\\} \\\\\n",
+ "0, & \\text{otherwise}\n",
+ "\\end{cases}\n",
+ "$$\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### Why small deltas appear\n",
+ "\n",
+ "* **Windowed arrivals:** The generator uses a **windowed** active-user sampler; across windows the rate is piecewise-constant (Cox process), not one global Poisson.\n",
+ "* **Finite horizon / warm-up:** Short runs and lack of warm-up bias early windows.\n",
+ "* **Network latency:** Small but non-zero link delays add a constant component to $W$.\n",
+ "\n",
+ "\n",
+ "Increasing the run length, keeping edges tiny and deterministic, and ensuring identical servers will usually shrink Theory–Observed gaps. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "1975945b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "==================================================================\n",
+ "MMc (RR) — Theory vs Observed\n",
+ "------------------------------------------------------------------\n",
+ "sym metric theory observed abs rel%\n",
+ "------------------------------------------------------------------\n",
+ "λ Arrival rate (1/s) 40.000000 39.497083 -0.502917 -1.26\n",
+ "μ Service rate (1/s) 100.000000 99.946044 -0.053956 -0.05\n",
+ "c Servers 2.000000 2.000000 0.000000 0.00\n",
+ "rho Utilization 0.200000 0.197592 -0.002408 -1.20\n",
+ "L Mean items in sys 0.500000 0.493409 -0.006591 -1.32\n",
+ "Lq Mean items in queue 0.100000 0.096645 -0.003355 -3.36\n",
+ "W Mean time in sys (s) 0.012500 0.012492 -0.000008 -0.06\n",
+ "Wq Mean waiting (s) 0.002500 0.002447 -0.000053 -2.12\n",
+ "==================================================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "mmc = MMc()\n",
+ "if mmc.is_compatible(payload):\n",
+ " mmc.print_comparison(payload, results) \n",
+ "else:\n",
+ " print(\"Payload is not compatible with M/M/c:\")\n",
+ " for reason in mmc.explain_incompatibilities(payload):\n",
+ " print(\" -\", reason)\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9f940d1e",
+ "metadata": {},
+ "source": [
+ "### 4) Plot dashboards\n",
+ "\n",
+ "**System-level and per-server charts**\n",
+ "\n",
+ "Beyond the two main panels (latency histogram + throughput time series), AsyncFlow records **rich time series and per-request distributions** that make the system behavior easy to read. In your scenario (single server, exponential CPU only, no I/O/RAM), you’ll see:\n",
+ "\n",
+ "* **System dashboard**\n",
+ "\n",
+ " * **Request Latency Distribution**: end-to-end histogram (client→server→client) with **mean, P50, P95, P99** markers. Here latency is dominated by CPU service + short queue; vertical lines highlight tail behavior.\n",
+ " * **Throughput (RPS)**: windowed time series with **mean, P95, max**. Great for spotting stability, oscillations, and warm-up.\n",
+ "\n",
+ "* **Server time-series dashboard (for `app-1`)**\n",
+ "\n",
+ " * **Ready queue length**: CPU queue over time with **mean/min/max**. With ρ≈0.5 the mean queue ≈0.5, consistent with M/M/1.\n",
+ " * **I/O queue length**: flat at zero (no I/O step in the pipeline).\n",
+ " * **RAM in use**: flat at zero (no RAM step in the pipeline).\n",
+ "\n",
+ "* **Server event-metrics dashboard**\n",
+ "\n",
+ " * **Server-side latency**: histogram of (waiting + service) at the server.\n",
+ " * **CPU service time**: histogram of **service\\_time** (Exp \\~15 ms) with P95/P99.\n",
+ " * **CPU waiting time**: histogram of queue **waiting\\_time**; shows the heavy tail under bursts.\n",
+ " * **I/O time**: flat at zero (no I/O).\n",
+ "\n",
+ "#### What you “get for free” from the collected data\n",
+ "\n",
+ "* **Distributions** (per-request arrays): end-to-end latency, server latency, **service\\_time**, **waiting\\_time**, (optional) **io\\_time** ⇒ percentiles, variance, pre/post comparisons.\n",
+ "* **Time series** (periodic sampling): **ready\\_queue\\_len**, **event\\_loop\\_io\\_sleep** (if I/O exists), **ram\\_in\\_use**, **edge\\_concurrent\\_connection**, plus **throughput series** to estimate observed λ.\n",
+ "* **Derived checks**: automatic **Little’s Law** sanity (L≈λW, Lq≈λWq), observed utilization **ρ̂ = λ̂/μ̂**, and the **MM1 theory vs observed** comparison table you printed.\n",
+ "\n",
+ "> In this specific setup, I/O and RAM panels are flat by design; add I/O or RAM steps and those plots will populate accordingly.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "d0ccfc68",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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07bx1Rg+uXLdpf/csrgoIeM6iLSw0Shc37LE5htKli+tG9GlJ8feCJG27cVph1/6xaf8sWd0UEFDDou1+SIQubtxncwxlypRQqRp5Ldq2XDul8H9u2LR/1mzuCgiobtEW+m+4Lm3eb3MMZcuWUomqfhZtmy6dUMTNf23a39vbQwEB1SzaQq4/0OWtB22OoVy50ipW2deibUPwcUXeumXT/tmzZ1FAQBWLtltX7+vK9kM27R8VFSXPLFnM94HJX+f+VtTt2zaNkSOHlwICnrVou3nxnq7uPGzT/pJUsUI5FSqX06Jt7amjunLnjk3758yZTQEBlk+B/HM+VNd2H7E5hkoVn1GB0jks2lYfD9K10Ls27Z8rl7cCAiyfwL925o6u7zlqcwyVK5VXvhI+Fm33jh7RjfuhNu3vmzu7AgIqWLRdORmif/YdS3K/qKj4mQZubm569tmKylPU22L73cOH9W/YPZti8PP1UUBAeYu2S3/f1o0Df9u0vyRVqVJJvoWzWbSFHDgogyF+aapHZ+g8ytnZSe7ullM/YqJjFRdj+zKsbq6uCZbFC4+Okmxcms/ZKfUxuKYwhrnzA/VWn15a+dsqHTh4QLMDZ+ny5UsqWrSohg0bpg4dOuhi8CUN+miQ1q//S/cf3FftWnU0cfwkFS9eXNJ/M3wMBmnez3M1a/ZPCjoapLi4OJUtW1Z9+/ZVu1Yd9fAUmL/Wr9Pc+YHav3+frl2/JldXV1WsUFH93hugZq++Knf3/3597datm+bMmaPrl2/q89Ej9evSJbp9+5aKFy+hIYOGqlXL1gmvg5tbguUB46KiZLRxKpCTs7NFDFL8MouPfi0WL1mo2NhYvf1WH7m6/te/apWqCggI0LJlyzRjxgx5enpKkmKjomRtKtDChfOVJUsWvflGLxn+/1o6Oztr0KBBmjJlihYuXKgXX3xR0ZGxio6Jib+fDZLRGKd79+4pW7ZscnJKuGyrm6ubVq9erdDQUA0YMEC+vr7/P0vIqMKFC6tF89c0/+d5ungxWEWK+CfY39nFWW7/fx0MBoM8PDxUokQ+3TkdnKDvwz8TzNfRSkwAAADAkzZ79mx1795d69ev1759+zR9+nRdumSZ91y9elWDBg3SmjVrdP/+fdWrV08//PCD1Vd8zJkzRz/++KOCgizznm7duln0W7t2rWbNmqU9e/bo6tWrcnV1VeXKlfXhhx+qWbNmFn1NeU9oaKiGDRumRYsW6datWypZsqSGDRumtm3bpuclStLPP/+s2NhYvffeexZLtlWrVk0BAQFasmSJpk6das57EhMYGKgsWbIk+Pv4wIED9d133ykwMNBcBAoODtbWrVvVoEEDcwFIis9533vvPXXv3l3Lly83X/MVK1aY8x5TAUiSChcurNatW2vOnDkKDg5OsGS3I6EI9ARkzx6/1EW1atXMBSCT8uXLq1ixYjpz5ozu3LljrlCmVBZvd3X6rKbN/R/9g5fBoBTt/+g7aCSpcqPCqtAg+el9SY2Z2hiqNCmiSo0KWeltG1d351THUO1//nq2SeHHjsHd0yXVMVR/paiq/s//sWPwzOqashhcnXRj+2mLtprNi6nGK0UfOwav7Cm7p629l6h2y+Kq2bzYY8eQNadHqmOo26akarey7b1fj75bQZKy+2VJdQwB7Uul6H08j8qR18vmGLZv3271nUANOpZOVQy5CmZN9XVo2KVsit4J9CjfwimLwcVKDI26l0vR+3gelaeod6pjaPLmM6mKIV+J7MnGsH17/EMFderUtBrDi73Kp+idQI/KX9onZdfBLeFN+dLbFXX85DEZDAZ5Zkv8IYqQ+T8rZMECi+XDCn3/vVwLF7a6X/S1a7rUs5dFm0/rVvJ85Bd+SfLwii/qXB00SBF/Hze3O+fMqSKBcxKNKXT1at387juLekG+zz+X57OVE90nMaYYrDG9J+2zL4br/v376tHjDWXJkkXTpk1Tp06dzIWI2rVr67ORn+nChQv69ttv1aFzWx0+fFhOTk7mP/737dtX06dP18svv6zPP/9crq6uWrVqlbp3765Tp05p1KhR5uMuXPyzbt3+V693fl0FCxbUzZs3FRgYqDbtW2nBggVq3759glhfa/OqsmTJokGDPlRsbGz8DJnur6t0uZKqXr16gv6PilGkIiIjbLtokZKvp2Wx3cXVSS6ulvfDgUPxDxE0eD4gwb1Sq1Ytbd68WUFBQapRI/4hCM+sCb8W//zzjy5euqjqz9WUvDwULqNyZ3OXJPn7+ytfvnzasyf+oQ0XNye5uLnJydlJV69elV/+XAoPDzevdf3pp5+qfv36FuPv3h3/0Ent2vEPxXhm+y+GegF1Nf/neTry9yGVKV/KpktTrm5+la6ZN0H7wz8TTKZuymrTmAAAAEhft+fPj895HlLo++/llsi7XKKvXdPFnj0t2nxatVau7t0SPcaVDwcp4vh/D/K55MipInMTnx1iznkeku/zz5Xl2WcT2SP1hgwZ8v95Tw95enpazXtGjhypCxcuaOLEiWrRooWOHLF8ULR3796aNm2a1bzn9OnTFnnP7Nmz9c8//+j11//Le+bMmaNXX31VCxcuVLt27RLE2LRp0//PewYpNjZWP/zwg9q1a6eiRYvalPfcv39fERE25j2ScufOnWyfR3OKh9WuXTtB3mPNP//8owsXLqh27doJikWP5j22HFOS9uzZYy4CJdd/zpw52rNnD0UgpE7p0qUlKdECj6k9PDz8sYtABkPCl9qnbH9DqvaX/v9F7Fb+2EcMKYzBKfUxuLg6S4n/XY8YbOSUFjFY+eOzI8fg7Gp9SsXTcB1cUxuDs5PcnFP35HpmicF0HyT2NXN1T10Mzs5Ock7tdXB3Nhf7rBX9TGJDQhR19qxFmzEqKvF9YmMT9I+9dctqf1Nb9JWrFvu4hIYmHdPdUEWdeSSmiPAk90lMUvuYtoWFhWn//v3y8Ih/iUzbtm3l7++v9u3ba9SoURoyZIh5nzx58mjgwIHasGGDGjduLEn6888/NX36dH355Zf66KOPzH3fffdd9enTR1999ZV69uypokXjHxyYPn26vLy8LGLp37+/KleurM8//9xqEahMmTIWS6u1adNGJUuW1Lfffqv58+cnex369u2rOXMSL7w96tEiprXrePly/OzoQoUKJdheqFAhcx9TMmRtjCtX4mc358tfwFzze7hfwYIFdeLECYt2f39/1axZUxUrVlSWLFl05MgRffvtt2rUqJHmz59vcf1MMZrW6H54bFOMV65csfnecnZxsvpwirWfCdYKvAAAAHjyYm+HJMwvohJ/b6oxJiZB/9hbSa+AEn3lisU+cb5Jr05hNecJD09yn9RKSd7j5+engQMHav369eaVUP744w9NmzYt0bxn7NixevPNN5PMe/r166fKlStr5MiRVotAj+Y9rVu3VsmSJTVx4kSb8p533303VXmPNY/mFA8ztT2c96R0DFO7Ke9JyTEft78jogj0BJjWaT9+/HiCbdHR0Tpz5oy8vLzk6+ubYDsAAIA99e3b15wISVK+fPlUunRpHTt2TP3797fo26BBA0nSyZMnzUWgefPmyd3dXZ06ddK//1omh6+99pqmTJmiv/76Sz3//2nChxOhBw8eKCIiQkajUc8//7ymTp1qXuLsYQ8nWZJUpEgRlSpVSqdOnbLpHAcNGqTXX3/dpr62CgsLkyS5u7sn2Ga6nqY+jzOGaZxHx5g9e7bF5y1atFD37t1VsWJFvfPOO3r11fhZU2kVIwAAAOAIHjfvMRWB5syZQ97ziPTKe1J6TPIeikBPRPHixdWkSROtXbtWM2bM0Jtvvmne9uWXX+rOnTt6/fXXLdZNBAAAeBqY3u/zsJw5cyp//vwWSZKpXZJuPfQesuPHjysyMtI8s8Sa69f/e49dcHCwPv30U/3555+6beVdYiEhIQmSIWsx5s6dWxcuXEj0mA8rV66cypUrZ1NfW5kKLZGRkQmWNDAtwWDqY8sY1kRERCQ7hhQ/q6dHjx4aP368duzYoRdeeCHZ8W2NEQAAAHAEqc17/v77b/KeJ5T3pDSPIe+hCPTE/PDDD6pdu7Z69uyp5cuXq0yZMjp48KA2bNigIkWKaNy4cfYOEQAAPMI5Zw65lbD8Rdvglvg7hAwuLgn6O+dKeh1l1wIFFBt61/y5S46cSceU3TthTMm8ZDM1nJ2tL9+XWLtkuWxAXFycsmbNqmXLliXav1ix+Pe33b9/XwEBAbp7967ef/99VaxYUd7e3nJyctLMmTO1YMECxcXFJdg/sQdpbH331N27dxWeguUl8uZN+N6bRxUsWFBHjx7V5cuXVbJkSYttyS138PAYknTt6hWr2y9fvpzsGCamZSdu3LiRYPzLly+rbNmyjxUjAAAAMjZynv8/JnlPAk8670lsSbZH856k+ls7JnkPRaAnpnjx4tq3b5+GDRum1atXa+3atcqbN6/eeecdDRs2TH5+fvYOEQAAPCJnp07K2amTzf1d8+VT8T/+SNExCoz7KkX9vV98Ud4vvpiifeypZMmSOnnypCpUqKA8efIk2XfDhg26dOmSfvrpJ73xxhsW26ZPn55uMb7//vtpvjZ2jRo1tHr1au3cuTNBMrRz5055enqqfPnySY6RJ08eFSxUSEeDjig8PNziyboLFy7o2rVratKkiU0xm5aIeDiRq1Gjhn788Uft3LnTvHzfwzFKsukFswAAAMi4yHnSRqlSpXTixAnynoekJO8pXLiwDh06ZFPeY3q/kClnefSYD/cx/Tuz5z2pe6syUqRQoUKaNWuWrl27pqioKF28eFHfffcdBSAAAOCwOnfuLEn64IMPrD7NdvfuXfO0fNNTdo8mG0eOHNHy5cvTLcZBgwZp3bp1Nn/YomPHjnJ2dta3336rmJgYc/u+ffu0efNmtWrVymLJgbt37+rEiRMJ1g9v3a6jwsPCNOenaRbt48ePl/Tf9ZWkO3fuKDY2NkEsJ06c0E8//SQ/Pz/Vrl3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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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+ ""
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+ },
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+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# 4.1 System dashboard: latency + throughput\n",
+ "fig_sys, axes_sys = plt.subplots(1, 2, figsize=(12, 4.5), dpi=140)\n",
+ "results.plot_latency_distribution(axes_sys[0])\n",
+ "results.plot_throughput(axes_sys[1])\n",
+ "fig_sys.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "# 4.2 Server time-series and event-metric dashboards\n",
+ "sids = results.list_server_ids()\n",
+ "for sid in sids:\n",
+ " # --- dashboards ---\n",
+ " fig_ts, axes_ts = plt.subplots(2, 2, figsize=(12, 8), dpi=140)\n",
+ " axes_ts[1, 1].axis(\"off\")\n",
+ " results.plot_server_timeseries_dashboard(\n",
+ " ax_ready=axes_ts[0, 0],\n",
+ " ax_io=axes_ts[0, 1],\n",
+ " ax_ram=axes_ts[1, 0],\n",
+ " server_id=sid,\n",
+ " )\n",
+ " fig_ts.suptitle(f\"Time-series — {sid}\")\n",
+ " fig_ts.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ " fig_ev, axes_ev = plt.subplots(2, 2, figsize=(12, 8), dpi=140)\n",
+ " results.plot_server_event_metrics_dashboard(\n",
+ " ax_latency_hist=axes_ev[0, 0],\n",
+ " ax_service_hist=axes_ev[0, 1],\n",
+ " ax_io_hist=axes_ev[1, 0],\n",
+ " ax_wait_hist=axes_ev[1, 1],\n",
+ " server_id=sid,\n",
+ " )\n",
+ " fig_ev.suptitle(f\"Event metrics — {sid}\")\n",
+ " fig_ev.tight_layout()\n",
+ " plt.show()\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "asyncflow-sim-py3.12 (3.12.3)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/asyncflow/analysis/__init__.py b/src/asyncflow/analysis/__init__.py
index e7338af..5a47802 100644
--- a/src/asyncflow/analysis/__init__.py
+++ b/src/asyncflow/analysis/__init__.py
@@ -3,5 +3,6 @@
from asyncflow.metrics.simulation_analyzer import ResultsAnalyzer
from asyncflow.metrics.sweep_analyzer import SweepAnalyzer
from asyncflow.queue_theory_analysis.mm1 import MM1
+from asyncflow.queue_theory_analysis.mmc import MMc
-__all__ = ["MM1", "ResultsAnalyzer", "SweepAnalyzer"]
+__all__ = ["MM1", "MMc", "ResultsAnalyzer", "SweepAnalyzer"]
diff --git a/src/asyncflow/config/constants.py b/src/asyncflow/config/constants.py
index a1a8e8b..2e78581 100644
--- a/src/asyncflow/config/constants.py
+++ b/src/asyncflow/config/constants.py
@@ -146,6 +146,7 @@ class LbAlgorithmsName(StrEnum):
ROUND_ROBIN = "round_robin"
LEAST_CONNECTIONS = "least_connection"
+ RANDOM = "random"
# ======================================================================
diff --git a/src/asyncflow/queue_theory_analysis/mm1.py b/src/asyncflow/queue_theory_analysis/mm1.py
index 6c22b64..f993904 100644
--- a/src/asyncflow/queue_theory_analysis/mm1.py
+++ b/src/asyncflow/queue_theory_analysis/mm1.py
@@ -111,10 +111,7 @@ def _check_server_model(self, payload: SimulationPayload) -> list[str]:
errs.append("the single step must be CPU-bound.")
return errs
- op_key, op_data = next(iter(step.step_operation.items()))
- if op_key is not StepOperation.CPU_TIME:
- errs.append("CPU step must use CPU_TIME as its operation.")
- return errs
+ _, op_data = next(iter(step.step_operation.items()))
# Must be exponential RV (not deterministic)
if not isinstance(op_data, RVConfig):
diff --git a/src/asyncflow/queue_theory_analysis/mmc.py b/src/asyncflow/queue_theory_analysis/mmc.py
new file mode 100644
index 0000000..e7564e3
--- /dev/null
+++ b/src/asyncflow/queue_theory_analysis/mmc.py
@@ -0,0 +1,597 @@
+"""
+Check if asyncflow under the hypothesis of a MMc queue
+(c >= 1), reproduce the theory.
+"""
+
+from __future__ import annotations
+
+import sys
+from typing import TYPE_CHECKING, Literal, TextIO, TypedDict, cast
+from weakref import WeakSet
+
+from asyncflow.config.constants import (
+ Distribution,
+ EndpointStepCPU,
+ LatencyKey,
+ LbAlgorithmsName,
+)
+from asyncflow.queue_theory_analysis.base import QueueTheoryBase
+from asyncflow.schemas.common.random_variables import RVConfig
+
+if TYPE_CHECKING:
+
+ from asyncflow.metrics.simulation_analyzer import ResultsAnalyzer
+ from asyncflow.schemas.payload import SimulationPayload
+
+
+class MMcParams(TypedDict):
+ """
+ Minimal global parameters for the M/M/c split model (round-robin into
+ per-server queues). These are either directly specified by the payload
+ or trivially derived and also observable from runs.
+
+ - lambda_rate (λ): arrival rate [1/s]
+ - mu_rate (μ): per-server service rate [1/s]
+ - c: number of parallel servers (c >= 1)
+ - rho (rho = λ/(cμ)): global utilization (stability requires rho < 1)
+ - a (λ/μ): offered load (expected busy servers); derivable and observable
+ - capacity_rate (cμ): total service capacity [1/s]
+ """
+
+ lambda_rate: float
+ mu_rate: float
+ c: int # c >= 1
+ rho: float # rho = λ / (c * μ)
+ a: float # λ / μ
+ capacity_rate: float # c * μ
+
+MMcResultKey = Literal["lambda_rate", "mu_rate", "c", "rho", "L", "Lq", "W", "Wq"]
+
+class MMcResults(TypedDict):
+ """
+ Closed-form KPIs for the M/M/c split model (round-robin), restricted to
+ quantities you can also measure from the simulator (no Erlang-C terms).
+
+ Exposes:
+ - (λ, μ, c, rho) for clarity/logging and cross-checks
+ - L, Lq via Little's Law (L = λW, Lq = λWq)
+ - W, Wq (mean system time and mean waiting time)
+ """
+
+ # Parameters (echoed for convenience)
+ lambda_rate: float
+ mu_rate: float
+ c: int
+ rho: float
+
+ # Queue sizes and times (all observable)
+ L: float # mean jobs in system
+ Lq: float # mean jobs in queue
+ W: float # mean time in system [s]
+ Wq: float # mean waiting time [s]
+
+
+
+class MMcKPIRow(TypedDict):
+ """
+ One formatted row for theory vs observed comparison (M/M/c).
+ Same shape as the MM1 KPIRow; separate name to avoid collisions.
+ """
+
+ symbol: str
+ name: str
+ theory: float | str
+ observed: float | str
+ abs_diff: float | str
+ rel_diff_pct: float | str # percentage (e.g., "3.2" means +3.2%)
+
+
+class MMcCompatGlobalRow(TypedDict):
+ """Row for global compatibility checks."""
+
+ scope: str # "topology" | "generator" | "edges"
+ issue: str # human-readable message
+
+
+class MMcCompatServerRow(TypedDict):
+ """Row for per-server compatibility checks."""
+
+ server_index: int
+ server_id: str
+ issue: str
+
+
+class MMc(QueueTheoryBase):
+ """Analyzer for the M/M/c split model (Round-Robin), c>=1, with strict checks."""
+
+ # Upper bound for "negligible" deterministic network latency
+ MAX_EDGE_LATENCY_S: float = 1e-3 # 1 ms
+
+ def __init__(self) -> None:
+ """Track analyzers we've already processed; weak refs avoid leaks."""
+ self._processed_ras: WeakSet[ResultsAnalyzer] = WeakSet()
+
+ # ──────────────────────────────────────────────────────────────────
+ # Compatibility checks split into helpers to keep cyclomatic low
+ # ──────────────────────────────────────────────────────────────────
+ def _check_topology(self, payload: SimulationPayload) -> list[str]:
+ errs: list[str] = []
+ nodes = payload.topology_graph.nodes
+ c = len(nodes.servers)
+
+ if c == 0:
+ errs.append("requires at least one server.")
+ return errs
+
+ lb = nodes.load_balancer
+ if c == 1:
+ if lb is not None:
+ errs.append("for c=1 the load balancer must be absent.")
+ elif lb is None:
+ errs.append("for c>1 a load balancer is required.")
+ elif lb.algorithms != LbAlgorithmsName.RANDOM:
+ errs.append("only round_robin is supported for the split M/M/c model.")
+
+ return errs
+
+ def _check_generator(self, payload: SimulationPayload) -> list[str]:
+ errs: list[str] = []
+ gen = payload.rqs_input
+ if gen.avg_active_users.distribution != Distribution.POISSON:
+ errs.append("avg_active_users must be Poisson.")
+ if gen.avg_request_per_minute_per_user.distribution != Distribution.POISSON:
+ errs.append("avg_request_per_minute_per_user must be Poisson.")
+ if gen.avg_active_users.mean <= 0:
+ errs.append("avg_active_users.mean must be > 0.")
+ if gen.avg_request_per_minute_per_user.mean <= 0:
+ errs.append("avg_request_per_minute_per_user.mean must be > 0.")
+ return errs
+
+ def _check_edges(self, payload: SimulationPayload) -> list[str]:
+ errs: list[str] = []
+ for edge in payload.topology_graph.edges:
+ latency = edge.latency
+ if isinstance(latency, RVConfig):
+ errs.append(
+ f"edge '{edge.id}' latency must be deterministic (<=1ms), "
+ "not a random variable.",
+ )
+ continue
+ if float(latency) > self.MAX_EDGE_LATENCY_S:
+ errs.append(
+ f"edge '{edge.id}' deterministic latency must be <= 1 ms.",
+ )
+ return errs
+
+ def _check_server_model(self, payload: SimulationPayload) -> list[str]:
+ errs: list[str] = []
+ servers = payload.topology_graph.nodes.servers
+
+ mu_ref: float | None = None
+ for idx, server in enumerate(servers):
+ if len(server.endpoints) != 1:
+ errs.append(f"server[{idx}] must expose exactly one endpoint.")
+ continue
+
+ steps = server.endpoints[0].steps
+ if len(steps) != 1:
+ errs.append(f"server[{idx}] endpoint must contain exactly one step.")
+ continue
+
+ step = steps[0]
+ if not isinstance(step.kind, EndpointStepCPU):
+ errs.append(f"server[{idx}] single step must be CPU-bound.")
+ continue
+
+ # Pydantic già fa CPU_TIME → qui prendiamo solo i parametri RV
+ _, op_data = next(iter(step.step_operation.items()))
+
+ if not isinstance(op_data, RVConfig):
+ errs.append(
+ f"server[{idx}] service time must be an exponential RVConfig.")
+ continue
+ if op_data.distribution != Distribution.EXPONENTIAL:
+ errs.append(
+ f"server[{idx}] service time distribution must be exponential.")
+ continue
+ if op_data.mean <= 0:
+ errs.append(
+ f"server[{idx}] service time mean must be > 0.")
+ continue
+
+ mu_i = 1.0 / float(op_data.mean)
+ if mu_ref is None:
+ mu_ref = mu_i
+ # identicità dei server (tolleranza numerica)
+ elif abs(mu_i - mu_ref) > 1e-12 * max(1.0, mu_ref):
+ errs.append(
+ f"all servers must be identical; "
+ f"found different μ at server[{idx}].",
+ )
+
+ return errs
+
+ # ------------- Compatibility (public) --------------------------------
+ def explain_incompatibilities(
+ self, payload: SimulationPayload,
+ ) -> list[str]:
+ """Collect and return all MMc assumption violations."""
+ errors: list[str] = []
+ errors.extend(self._check_topology(payload))
+ errors.extend(self._check_generator(payload))
+ errors.extend(self._check_edges(payload))
+ # Only check server model if we do have servers
+ if payload.topology_graph.nodes.servers:
+ errors.extend(self._check_server_model(payload))
+ return errors
+
+ # ------------- private method to build dict from theory --------------
+
+ def _arrival_rate_lambda_rate(self, payload: SimulationPayload) -> float:
+ """λ = users_mean * rpm_per_user / 60."""
+ gen = payload.rqs_input
+ users_mean = float(gen.avg_active_users.mean)
+ rpm_per_user = float(gen.avg_request_per_minute_per_user.mean)
+ return users_mean * rpm_per_user / 60.0
+
+
+ def _service_rate_mu_rate(self, payload: SimulationPayload) -> float:
+ """μ = 1 / E[S] from the (identical) CPU exponential step."""
+ server0 = payload.topology_graph.nodes.servers[0]
+ step0 = server0.endpoints[0].steps[0]
+ _, rv = next(iter(step0.step_operation.items()))
+ rv_cfg = cast("RVConfig", rv)
+ return 1.0 / float(rv_cfg.mean)
+
+
+ def _server_count(self, payload: SimulationPayload) -> int:
+ """C = number of parallel servers."""
+ return len(payload.topology_graph.nodes.servers)
+
+
+ def _total_capacity_rate(self, server_count: int, mu_rate: float) -> float:
+ """Total capacity = c * μ [1/s]."""
+ return server_count * mu_rate
+
+
+ def _rho_from(
+ self,
+ lambda_rate: float,
+ server_count: int,
+ mu_rate: float,
+ ) -> float:
+ """Rho = λ / (c * μ)."""
+ capacity = server_count * mu_rate
+ return (lambda_rate / capacity) if capacity > 0.0 else float("inf")
+
+
+ def _offered_load_a(self, lambda_rate: float, mu_rate: float) -> float:
+ """A = λ / μ (expected busy servers)."""
+ return (lambda_rate / mu_rate) if mu_rate > 0.0 else float("inf")
+
+
+ def _build_params(self, payload: SimulationPayload) -> MMcParams:
+ """Build MMcParams (theory) from payload."""
+ lambda_rate = self._arrival_rate_lambda_rate(payload)
+ mu_rate = self._service_rate_mu_rate(payload)
+ server_count = self._server_count(payload)
+ capacity_rate = self._total_capacity_rate(server_count, mu_rate)
+ rho = self._rho_from(lambda_rate, server_count, mu_rate)
+ offered_load = self._offered_load_a(lambda_rate, mu_rate)
+ return MMcParams(
+ lambda_rate=lambda_rate,
+ mu_rate=mu_rate,
+ c=server_count,
+ rho=rho,
+ a=offered_load,
+ capacity_rate=capacity_rate,
+ )
+
+ # ────────────────────────────────────────────────────────────────────
+ # Centralized RA processing (avoid repeated processing)
+ # ────────────────────────────────────────────────────────────────────
+
+ def _ensure_metrics_processed(
+ self,
+ results_analyzer: ResultsAnalyzer,
+ ) -> None:
+ """Call process_all_metrics() at most once per ResultsAnalyzer."""
+ if results_analyzer not in self._processed_ras:
+ results_analyzer.process_all_metrics()
+ self._processed_ras.add(results_analyzer)
+
+
+ # ────────────────────────────────────────────────────────────────────
+ # Helpers: observed (from ResultsAnalyzer)
+ # ────────────────────────────────────────────────────────────────────
+
+ def _observed_lambda_rate(self, results_analyzer: ResultsAnalyzer) -> float:
+ """Estimate λ̂ from throughput series (mean RPS)."""
+ self._ensure_metrics_processed(results_analyzer)
+ _, rps_series = results_analyzer.get_throughput_series()
+ return (sum(rps_series) / len(rps_series)) if rps_series else 0.0
+
+
+ def _observed_mu_rate(self, results_analyzer: ResultsAnalyzer) -> float:
+ """
+ Estimate μ̂ = 1 / mean(service_time) aggregating all servers
+ (weighted by number of jobs per server).
+ """
+ self._ensure_metrics_processed(results_analyzer)
+ arrays_map = results_analyzer.get_server_event_arrays()
+
+ service_time_sum: float = 0.0
+ service_time_count: int = 0
+ for arrays in arrays_map.values():
+ values = arrays.get("service_time") or []
+ service_time_sum += float(sum(values))
+ service_time_count += len(values)
+
+ mean_service_time = (
+ service_time_sum / service_time_count if service_time_count > 0 else 0.0
+ )
+ return (1.0 / mean_service_time) if mean_service_time > 0.0 else float("inf")
+
+
+ def _build_observed_params(
+ self,
+ payload: SimulationPayload,
+ results_analyzer: ResultsAnalyzer,
+ ) -> MMcParams:
+ """Build MMcParams using observed λ̂, μ̂ and c from payload."""
+ observed_lambda_rate = self._observed_lambda_rate(results_analyzer)
+ observed_mu_rate = self._observed_mu_rate(results_analyzer)
+ server_count = self._server_count(payload)
+ capacity_rate = self._total_capacity_rate(server_count, observed_mu_rate)
+ observed_rho = self._rho_from(
+ observed_lambda_rate,
+ server_count,
+ observed_mu_rate,
+ )
+ observed_a = self._offered_load_a(
+ observed_lambda_rate,
+ observed_mu_rate,
+ )
+ return MMcParams(
+ lambda_rate=observed_lambda_rate,
+ mu_rate=observed_mu_rate,
+ c=server_count,
+ rho=observed_rho,
+ a=observed_a,
+ capacity_rate=capacity_rate,
+ )
+
+ # ────────────────────────────────────────────────────────────────────
+ # Closed form (split RR): theory → MMcResults
+ # ────────────────────────────────────────────────────────────────────
+
+ def _theoretical_kpis_split(self, payload: SimulationPayload) -> MMcResults:
+ """Closed forms for RR split: λ_i=λ/c; Wq=rho/(μ-λ_i); W=1/μ+Wq; Lq=λWq; L=λW"""
+ self.validate_or_raise(payload)
+ params = self._build_params(payload)
+
+ lambda_rate = params["lambda_rate"]
+ mu_rate = params["mu_rate"]
+ server_count = params["c"]
+ rho = params["rho"]
+
+ if rho >= 1.0:
+ inf = float("inf")
+ return MMcResults(
+ lambda_rate=lambda_rate,
+ mu_rate=mu_rate,
+ c=server_count,
+ rho=rho,
+ L=inf,
+ Lq=inf,
+ W=inf,
+ Wq=inf,
+ )
+
+ per_server_lambda = lambda_rate / server_count
+ denom = (mu_rate - per_server_lambda)
+ wq = rho / denom if denom > 0.0 else float("inf")
+ w = (1.0 / mu_rate) + wq
+ lq = lambda_rate * wq
+ l_sys = lambda_rate * w
+
+ return MMcResults(
+ lambda_rate=lambda_rate,
+ mu_rate=mu_rate,
+ c=server_count,
+ rho=rho,
+ L=l_sys,
+ Lq=lq,
+ W=w,
+ Wq=wq,
+ )
+
+ def evaluate(self, payload: SimulationPayload) -> MMcResults:
+ """Public entry-point: return closed-form KPIs for split RR."""
+ return self._theoretical_kpis_split(payload)
+
+ # ────────────────────────────────────────────────────────────────────
+ # Observed KPIs → MMcResults (coerenti con definizioni sopra)
+ # ────────────────────────────────────────────────────────────────────
+
+ def _observed_kpis(
+ self,
+ payload: SimulationPayload,
+ results_analyzer: ResultsAnalyzer,
+ ) -> MMcResults:
+ """
+ Empirical KPIs:
+ - λ̂: mean throughput
+ - μ̂: 1 / mean(service_time)
+ - Ŵ: mean client latency
+ - Wq̂: mean waiting_time (server arrays)
+ - L̂: λ̂ * Ŵ
+ - Lq̂: λ̂ * Wq̂
+ - rhô: λ̂ / (c μ̂)
+ """
+ self._ensure_metrics_processed(results_analyzer)
+
+ lambda_hat = self._observed_lambda_rate(results_analyzer)
+ mu_hat = self._observed_mu_rate(results_analyzer)
+ server_count = self._server_count(payload)
+
+ # Ŵ from latency stats (client-side); edges are constrained to ≤1ms.
+ lat_stats = results_analyzer.get_latency_stats()
+ w_hat = float(lat_stats.get(LatencyKey.MEAN, 0.0))
+
+ # Wq̂ from per-server arrays (aggregate across servers)
+ arrays_map = results_analyzer.get_server_event_arrays()
+ wait_sum: float = 0.0
+ wait_count: int = 0
+ for arrays in arrays_map.values():
+ vals = arrays.get("waiting_time") or []
+ wait_sum += float(sum(vals))
+ wait_count += len(vals)
+ wq_hat = (wait_sum / wait_count) if wait_count > 0 else 0.0
+
+ l_hat = lambda_hat * w_hat
+ lq_hat = lambda_hat * wq_hat
+ rho_hat = (
+ lambda_hat / (server_count * mu_hat)
+ if mu_hat not in (0.0, float("inf")) else 0.0
+ )
+
+ return MMcResults(
+ lambda_rate=lambda_hat,
+ mu_rate=mu_hat,
+ c=server_count,
+ rho=rho_hat,
+ L=l_hat,
+ Lq=lq_hat,
+ W=w_hat,
+ Wq=wq_hat,
+ )
+
+ # ────────────────────────────────────────────────────────────────────
+ # Comparison table (same shape as MM1, senza Erlang terms)
+ # ────────────────────────────────────────────────────────────────────
+
+ @staticmethod
+ def _safe_delta(theory_value: float, observed_value: float) -> tuple[str, str, str]:
+ """Return (theory_str, abs_diff_str, rel_diff_str) with inf-safe logic."""
+ def fmt(x: float) -> str:
+ return "∞" if x == float("inf") else f"{x:.6f}"
+ theory_str = fmt(theory_value)
+ if theory_value == float("inf"):
+ return theory_str, "—", "—"
+ abs_diff = observed_value - theory_value
+ rel_pct = (
+ abs_diff / theory_value * 100.0
+ ) if theory_value != 0.0 else float("inf")
+ rel_str = "∞" if rel_pct == float("inf") else f"{rel_pct:.2f}"
+ return theory_str, f"{abs_diff:.6f}", rel_str
+
+
+ def compare_against_run(
+ self,
+ payload: SimulationPayload,
+ results_analyzer: ResultsAnalyzer,
+ ) -> list[MMcKPIRow]:
+ """Build a table with theory vs observed and deltas."""
+ self.validate_or_raise(payload)
+
+ theory = self._theoretical_kpis_split(payload)
+ observed = self._observed_kpis(payload, results_analyzer)
+
+ rows: list[MMcKPIRow] = []
+
+ def add(symbol: str, name: str, key: MMcResultKey) -> None:
+ theory_value = float(theory[key])
+ observed_value = float(observed[key])
+ th_s, abs_s, rel_s = self._safe_delta(theory_value, observed_value)
+ rows.append(
+ MMcKPIRow(
+ symbol=symbol,
+ name=name,
+ theory=th_s,
+ observed=f"{observed_value:.6f}",
+ abs_diff=abs_s,
+ rel_diff_pct=rel_s,
+ ),
+ )
+
+ add("λ", "Arrival rate (1/s)", "lambda_rate")
+ add("μ", "Service rate (1/s)", "mu_rate")
+ add("c", "Servers", "c")
+ add("rho", "Utilization", "rho")
+ add("L", "Mean items in sys", "L")
+ add("Lq", "Mean items in queue", "Lq")
+ add("W", "Mean time in sys (s)", "W")
+ add("Wq", "Mean waiting (s)", "Wq")
+
+ return rows
+
+
+ # --------------- PRETTY PRINT -------------------
+
+ # ────────────────────────────────────────────────────────────────────
+ # Pretty table (KPI):
+ # ────────────────────────────────────────────────────────────────────
+ @staticmethod
+ def _format_kpi_table(
+ rows: list[MMcKPIRow],
+ title: str = "MMc (RR) — Theory vs Observed",
+ ) -> str:
+ data = [
+ (
+ r["symbol"],
+ r["name"],
+ str(r["theory"]),
+ str(r["observed"]),
+ str(r["abs_diff"]),
+ str(r["rel_diff_pct"]),
+ )
+ for r in rows
+ ]
+
+ headers = ("sym", "metric", "theory", "observed", "abs", "rel%")
+ w_sym = max(len(headers[0]), *(len(d[0]) for d in data))
+ w_met = max(len(headers[1]), *(len(d[1]) for d in data))
+ w_th = max(len(headers[2]), *(len(d[2]) for d in data))
+ w_ob = max(len(headers[3]), *(len(d[3]) for d in data))
+ w_abs = max(len(headers[4]), *(len(d[4]) for d in data))
+ w_rel = max(len(headers[5]), *(len(d[5]) for d in data))
+
+ header = (
+ f"{headers[0]:<{w_sym}} {headers[1]:<{w_met}} "
+ f"{headers[2]:>{w_th}} {headers[3]:>{w_ob}} "
+ f"{headers[4]:>{w_abs}} {headers[5]:>{w_rel}}"
+ )
+ sep = "-" * len(header)
+ top = "=" * max(len(title), len(header))
+
+ lines = [top, title, sep, header, sep]
+ for sym, met, th, ob, ad, rd in data:
+ lines.append(
+ f"{sym:<{w_sym}} {met:<{w_met}} "
+ f"{th:>{w_th}} {ob:>{w_ob}} {ad:>{w_abs}} {rd:>{w_rel}}",
+ )
+ lines.append(top)
+ return "\n".join(lines)
+
+ def compare_and_format(
+ self,
+ payload: SimulationPayload,
+ results_analyzer: ResultsAnalyzer,
+ ) -> str:
+ """Return a formatted KPI table for theory vs observed."""
+ rows = self.compare_against_run(payload, results_analyzer)
+ return self._format_kpi_table(rows)
+
+ def print_comparison(
+ self,
+ payload: SimulationPayload,
+ results_analyzer: ResultsAnalyzer,
+ *,
+ file: TextIO | None = None,
+ ) -> None:
+ """Print the MMc KPI table (theory vs observed)."""
+ out = self.compare_and_format(payload, results_analyzer)
+ stream: TextIO = sys.stdout if file is None else file
+ print(out, file=stream)
+
diff --git a/src/asyncflow/runtime/actors/routing/lb_algorithms.py b/src/asyncflow/runtime/actors/routing/lb_algorithms.py
index 49763ba..cfcaefe 100644
--- a/src/asyncflow/runtime/actors/routing/lb_algorithms.py
+++ b/src/asyncflow/runtime/actors/routing/lb_algorithms.py
@@ -1,5 +1,5 @@
"""algorithms to simulate the load balancer during the simulation"""
-
+import random
from collections import OrderedDict
from collections.abc import Callable
@@ -35,10 +35,23 @@ def round_robin(
return value
+def random_choice(
+ edges: OrderedDict[str, EdgeRuntime],
+) -> EdgeRuntime:
+ """Pick a random outgoing edge uniformly"""
+ idx = random.randrange(len(edges)) # noqa: S311
+ for i, edge in enumerate(edges.values()):
+ if i == idx:
+ return edge
+
+ return next(iter(edges.values()))
+
LB_TABLE: dict[LbAlgorithmsName,
Callable[[OrderedDict[str, EdgeRuntime]], EdgeRuntime]] = {
LbAlgorithmsName.LEAST_CONNECTIONS: least_connections,
LbAlgorithmsName.ROUND_ROBIN: round_robin,
+ LbAlgorithmsName.RANDOM: random_choice,
}
+
diff --git a/tests/unit/queue_theory_analysis/test_mmc.py b/tests/unit/queue_theory_analysis/test_mmc.py
new file mode 100644
index 0000000..98dd22e
--- /dev/null
+++ b/tests/unit/queue_theory_analysis/test_mmc.py
@@ -0,0 +1,594 @@
+"""Basic golden test for the MMc analyzer (RR split model).
+
+This file intentionally contains both helpers and the first test,
+so you can copy-paste a single file and extend incrementally.
+
+It uses only public facades (like a pip-installed user would),
+plus `LatencyKey` which MMc reads from `get_latency_stats()`.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, cast
+
+import pytest
+
+# Public facades (end-user API)
+from asyncflow import AsyncFlow
+from asyncflow.analysis import MMc
+from asyncflow.components import Client, Edge, Endpoint, LoadBalancer, Server
+from asyncflow.config.constants import LatencyKey # used by get_latency_stats()
+from asyncflow.settings import SimulationSettings
+from asyncflow.workload import RqsGenerator
+
+if TYPE_CHECKING:
+ # Types used only for static checking (mypy), not imported at runtime.
+ from collections.abc import Iterable, Mapping
+
+ from asyncflow.metrics.simulation_analyzer import ResultsAnalyzer
+ from asyncflow.schemas.payload import SimulationPayload
+
+
+class _FakeResultsAnalyzer:
+ """Minimal, typed stub of ResultsAnalyzer for unit testing MMc.
+
+ Provide exactly what MMc calls:
+ - process_all_metrics()
+ - get_throughput_series()
+ - get_server_event_arrays()
+ - get_latency_stats()
+ """
+
+ def __init__(
+ self,
+ *,
+ lambda_rate: float,
+ mu_rate: float,
+ w_mean: float,
+ wq_mean: float,
+ server_ids: Iterable[str],
+ ) -> None:
+ self._lambda_rate = float(lambda_rate)
+ self._mu_rate = float(mu_rate)
+ self._w_mean = float(w_mean)
+ self._wq_mean = float(wq_mean)
+ self._server_ids = list(server_ids)
+
+ def process_all_metrics(self) -> None:
+ """No-op in the stub; real analyzer would pre-compute metrics."""
+ return
+
+ def get_throughput_series(self) -> tuple[list[float], list[float]]:
+ """Return a constant-RPS series with mean equal to lambda."""
+ times = [float(i) for i in range(10)]
+ rps = [self._lambda_rate for _ in times]
+ return times, rps
+
+ def get_server_event_arrays(self) -> Mapping[str, Mapping[str, list[float]]]:
+ """Return arrays per server for service and waiting times.
+
+ Means are set so the observed mu and Wq match the theoretical ones.
+ """
+ es = 1.0 / self._mu_rate if self._mu_rate > 0.0 else 0.0
+ out: dict[str, dict[str, list[float]]] = {}
+ for sid in self._server_ids:
+ out[sid] = {
+ "service_time": [es] * 50,
+ "waiting_time": [self._wq_mean] * 50,
+ }
+ return out
+
+ def get_latency_stats(self) -> Mapping[LatencyKey, float]:
+ """Expose the mean latency using the LatencyKey expected by MMc."""
+ return {LatencyKey.MEAN: self._w_mean}
+
+
+def _build_payload_mmc_split(
+ *,
+ users_mean: int = 120,
+ rpm_per_user: int = 20,
+ cpu_mean_s: float = 0.01,
+ c: int = 2,
+) -> SimulationPayload:
+ """Build a minimal payload compatible with MMc split (RR/random) assumptions.
+
+ - c identical servers
+ - 1 exponential CPU step per server
+ - deterministic tiny latencies (≤ 1 ms)
+ - load balancer with algorithms="random" (matches current MMc check)
+ """
+ gen = RqsGenerator(
+ id="rqs-1",
+ avg_active_users={"mean": users_mean},
+ avg_request_per_minute_per_user={"mean": rpm_per_user},
+ user_sampling_window=60,
+ )
+
+ client = Client(id="client-1")
+
+ endpoint = Endpoint(
+ endpoint_name="/api",
+ probability=1.0,
+ steps=[
+ {
+ "kind": "initial_parsing",
+ "step_operation": {
+ "cpu_time": {"mean": cpu_mean_s, "distribution": "exponential"},
+ },
+ },
+ ],
+ )
+
+ servers = [
+ Server(
+ id=f"srv-{i+1}",
+ server_resources={"cpu_cores": 1, "ram_mb": 2048},
+ endpoints=[endpoint],
+ )
+ for i in range(c)
+ ]
+
+ lb = LoadBalancer(
+ id="lb-1",
+ algorithms="random",
+ server_covered={s.id for s in servers},
+ )
+
+ edges = [
+ Edge(
+ id="gen-client",
+ source="rqs-1",
+ target="client-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="client-lb",
+ source="client-1",
+ target="lb-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ ]
+
+ for s in servers:
+ edges.append(
+ Edge(
+ id=f"lb-{s.id}",
+ source="lb-1",
+ target=s.id,
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ )
+ edges.append(
+ Edge(
+ id=f"{s.id}-client",
+ source=s.id,
+ target="client-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ )
+
+ settings = SimulationSettings(
+ total_simulation_time=60,
+ sample_period_s=0.05,
+ )
+
+ return (
+ AsyncFlow()
+ .add_generator(gen)
+ .add_client(client)
+ .add_servers(*servers)
+ .add_load_balancer(lb)
+ .add_edges(*edges)
+ .add_simulation_settings(settings)
+ ).build_payload()
+
+
+def _theory_kpis(payload: SimulationPayload) -> dict[str, float]:
+ """Ask MMc once for its closed-form KPIs and return a numeric snapshot."""
+ mmc = MMc()
+ res = mmc.evaluate(payload)
+ return {
+ "lambda": float(res["lambda_rate"]),
+ "mu": float(res["mu_rate"]),
+ "W": float(res["W"]),
+ "Wq": float(res["Wq"]),
+ }
+
+
+def test_mmc_compare_matches_theory() -> None:
+ """When Observed == Theory, compare_against_run deltas must be zero."""
+ payload = _build_payload_mmc_split(c=2)
+
+ # Build a stub RA that feeds back the theoretical values.
+ k = _theory_kpis(payload)
+ server_ids = [f"srv-{i+1}" for i in range(2)]
+ ra = _FakeResultsAnalyzer(
+ lambda_rate=k["lambda"],
+ mu_rate=k["mu"],
+ w_mean=k["W"],
+ wq_mean=k["Wq"],
+ server_ids=server_ids,
+ )
+
+ mmc = MMc()
+ assert mmc.is_compatible(payload), "Payload should be MMc-compatible."
+
+ rows = mmc.compare_against_run(payload, cast("ResultsAnalyzer", ra))
+ assert len(rows) == 8, "Expected 8 KPI rows."
+
+ # All absolute deltas should be 0.000000 (printed as strings).
+ zero = pytest.approx(0.0, abs=1e-9)
+ for r in rows:
+ msg = f"Non-zero delta for {r['symbol']} ({r['name']})"
+ assert float(r["abs_diff"]) == zero, msg
+
+# ---------------------------------------------------------------------------
+# Extra tests for MMc
+# ---------------------------------------------------------------------------
+
+def test_mmc_instability_returns_infinities() -> None:
+ """If rho >= 1, closed-form KPIs must be +inf (W, Wq, L, Lq)."""
+ # c=2, mu=100 -> capacity 200. Set lambda >= 200.
+ payload = _build_payload_mmc_split(
+ users_mean=1200,
+ rpm_per_user=10,
+ cpu_mean_s=0.01,
+ c=2,
+ )
+ mmc = MMc()
+ res = mmc.evaluate(payload)
+
+ assert res["rho"] >= 1.0
+ assert res["W"] == float("inf")
+ assert res["Wq"] == float("inf")
+ assert res["L"] == float("inf")
+ assert res["Lq"] == float("inf")
+
+def test_mmc_incompatible_wrong_lb_algorithm() -> None:
+ """Any LB algorithm different from the expected one should fail."""
+ # Build like helper but force a mismatching algorithm.
+ gen = RqsGenerator(
+ id="rqs-1",
+ avg_active_users={"mean": 120},
+ avg_request_per_minute_per_user={"mean": 20},
+ user_sampling_window=60,
+ )
+ client = Client(id="client-1")
+ endpoint = Endpoint(
+ endpoint_name="/api",
+ probability=1.0,
+ steps=[
+ {
+ "kind": "initial_parsing",
+ "step_operation": {
+ "cpu_time": {"mean": 0.01, "distribution": "exponential"},
+ },
+ },
+ ],
+ )
+ srv1 = Server(
+ id="srv-1",
+ server_resources={"cpu_cores": 1, "ram_mb": 2048},
+ endpoints=[endpoint],
+ )
+ srv2 = Server(
+ id="srv-2",
+ server_resources={"cpu_cores": 1, "ram_mb": 2048},
+ endpoints=[endpoint],
+ )
+ lb = LoadBalancer(
+ id="lb-1",
+ algorithms="least_connection", # intentionally wrong here
+ server_covered={"srv-1", "srv-2"},
+ )
+ edges = [
+ Edge(
+ id="gen-client",
+ source="rqs-1",
+ target="client-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="client-lb",
+ source="client-1",
+ target="lb-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="lb-srv1",
+ source="lb-1",
+ target="srv-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="lb-srv2",
+ source="lb-1",
+ target="srv-2",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="srv1-client",
+ source="srv-1",
+ target="client-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="srv2-client",
+ source="srv-2",
+ target="client-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ ]
+ settings = SimulationSettings(
+ total_simulation_time=60,
+ sample_period_s=0.05,
+ )
+ payload = (
+ AsyncFlow()
+ .add_generator(gen)
+ .add_client(client)
+ .add_servers(srv1, srv2)
+ .add_load_balancer(lb)
+ .add_edges(*edges)
+ .add_simulation_settings(settings)
+ ).build_payload()
+
+ mmc = MMc()
+ assert not mmc.is_compatible(payload)
+ reasons = mmc.explain_incompatibilities(payload)
+ # Do not rely on exact string; just ensure we flag the LB algo.
+ assert any("supported" in r or "round_robin" in r or "algorithm" in r
+ for r in reasons)
+
+
+def test_mmc_incompatible_edge_latency_too_large() -> None:
+ """Latency must be deterministic and <= 1 ms."""
+ gen = RqsGenerator(
+ id="rqs-1",
+ avg_active_users={"mean": 120},
+ avg_request_per_minute_per_user={"mean": 20},
+ user_sampling_window=60,
+ )
+ client = Client(id="client-1")
+ endpoint = Endpoint(
+ endpoint_name="/api",
+ probability=1.0,
+ steps=[
+ {
+ "kind": "initial_parsing",
+ "step_operation": {
+ "cpu_time": {"mean": 0.01, "distribution": "exponential"},
+ },
+ },
+ ],
+ )
+ srv1 = Server(
+ id="srv-1",
+ server_resources={"cpu_cores": 1, "ram_mb": 2048},
+ endpoints=[endpoint],
+ )
+ srv2 = Server(
+ id="srv-2",
+ server_resources={"cpu_cores": 1, "ram_mb": 2048},
+ endpoints=[endpoint],
+ )
+ lb = LoadBalancer(
+ id="lb-1",
+ algorithms="random",
+ server_covered={"srv-1", "srv-2"},
+ )
+ edges = [
+ Edge(
+ id="gen-client",
+ source="rqs-1",
+ target="client-1",
+ latency=0.005, # 5 ms -> too large
+ dropout_rate=0,
+ ),
+ Edge(
+ id="client-lb",
+ source="client-1",
+ target="lb-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="lb-srv1",
+ source="lb-1",
+ target="srv-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="lb-srv2",
+ source="lb-1",
+ target="srv-2",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="srv1-client",
+ source="srv-1",
+ target="client-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="srv2-client",
+ source="srv-2",
+ target="client-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ ]
+ settings = SimulationSettings(
+ total_simulation_time=60,
+ sample_period_s=0.05,
+ )
+ payload = (
+ AsyncFlow()
+ .add_generator(gen)
+ .add_client(client)
+ .add_servers(srv1, srv2)
+ .add_load_balancer(lb)
+ .add_edges(*edges)
+ .add_simulation_settings(settings)
+ ).build_payload()
+
+ mmc = MMc()
+ assert not mmc.is_compatible(payload)
+ reasons = mmc.explain_incompatibilities(payload)
+ assert any("<= 1 ms" in r for r in reasons)
+
+
+def test_mmc_incompatible_server_model_requires_single_cpu_step() -> None:
+ """Each server endpoint must have exactly one CPU step."""
+ gen = RqsGenerator(
+ id="rqs-1",
+ avg_active_users={"mean": 120},
+ avg_request_per_minute_per_user={"mean": 20},
+ user_sampling_window=60,
+ )
+ client = Client(id="client-1")
+
+ # Valid endpoint (1 CPU step)
+ endpoint_ok = Endpoint(
+ endpoint_name="/api",
+ probability=1.0,
+ steps=[
+ {
+ "kind": "initial_parsing",
+ "step_operation": {
+ "cpu_time": {"mean": 0.01, "distribution": "exponential"},
+ },
+ },
+ ],
+ )
+ # Invalid endpoint (2 CPU steps)
+ endpoint_bad = Endpoint(
+ endpoint_name="/api",
+ probability=1.0,
+ steps=[
+ {
+ "kind": "initial_parsing",
+ "step_operation": {
+ "cpu_time": {"mean": 0.01, "distribution": "exponential"},
+ },
+ },
+ {
+ "kind": "initial_parsing",
+ "step_operation": {
+ "cpu_time": {"mean": 0.01, "distribution": "exponential"},
+ },
+ },
+ ],
+ )
+
+ srv1 = Server(
+ id="srv-1",
+ server_resources={"cpu_cores": 1, "ram_mb": 2048},
+ endpoints=[endpoint_ok],
+ )
+ srv2 = Server(
+ id="srv-2",
+ server_resources={"cpu_cores": 1, "ram_mb": 2048},
+ endpoints=[endpoint_bad],
+ )
+ lb = LoadBalancer(
+ id="lb-1",
+ algorithms="random",
+ server_covered={"srv-1", "srv-2"},
+ )
+ edges = [
+ Edge(
+ id="gen-client",
+ source="rqs-1",
+ target="client-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="client-lb",
+ source="client-1",
+ target="lb-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="lb-srv1",
+ source="lb-1",
+ target="srv-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="lb-srv2",
+ source="lb-1",
+ target="srv-2",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="srv1-client",
+ source="srv-1",
+ target="client-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ Edge(
+ id="srv2-client",
+ source="srv-2",
+ target="client-1",
+ latency=0.00001,
+ dropout_rate=0,
+ ),
+ ]
+ settings = SimulationSettings(
+ total_simulation_time=60,
+ sample_period_s=0.05,
+ )
+ payload = (
+ AsyncFlow()
+ .add_generator(gen)
+ .add_client(client)
+ .add_servers(srv1, srv2)
+ .add_load_balancer(lb)
+ .add_edges(*edges)
+ .add_simulation_settings(settings)
+ ).build_payload()
+
+ mmc = MMc()
+ assert not mmc.is_compatible(payload)
+ reasons = mmc.explain_incompatibilities(payload)
+ assert any("exactly one step" in r for r in reasons)
+
+
+def test_mmc_compare_and_format_smoke() -> None:
+ """compare_and_format() should return a readable ASCII table."""
+ payload = _build_payload_mmc_split(c=2)
+ k = _theory_kpis(payload)
+ server_ids = [f"srv-{i+1}" for i in range(2)]
+ ra = _FakeResultsAnalyzer(
+ lambda_rate=k["lambda"],
+ mu_rate=k["mu"],
+ w_mean=k["W"],
+ wq_mean=k["Wq"],
+ server_ids=server_ids,
+ )
+ mmc = MMc()
+ table = mmc.compare_and_format(payload, cast("ResultsAnalyzer", ra))
+ assert "MMc (RR) — Theory vs Observed" in table
+ assert "Arrival rate" in table
+ assert "Mean waiting (s)" in table
+
+