AGENT-BASED MODEL · 2026

REEL 01 / 09

DISCRETE-EVENT SIMULATION

FIZA AMJAD

CACHENET

Agent-based distributed cache simulator

PYTHON ENGINEFASTAPIWEBSOCKETNEXT.JS 16

System Modeling Engine

Mastering Distributed Cache Dynamics

A high-fidelity system modeling framework designed to visualize distributed complexity, testing fault tolerance and consistency in real-time.

Execute Engine Technical Specs

MODULE 01

Core Mechanics

Monitor L1/L2 hits and misses with visual trackers and eviction alerts.

MODULE 02

Stress Testing

Inject “Chaos Monkey” events to observe system resilience and hashing.

MODULE 03

Live Telemetry

Deep analytics on latency and throughput with clean, academic charts.

MODULE 04

AI Tutoring

Ask the analyst for real-time explanations of emergent behaviours.

SYSTEM SCHEMATIC

Core Architecture

System Optimization

Parallel ProcessingMulti-threaded worker nodes for high-throughput modeling.

Affinity RoutingMathematical distribution for node data consistency.

Resource FlushingIndustrial memory management and eviction.

L-Balancer

Node-01

Node-02

Database

0.0%HIT RATIO

0TOTAL READS

0CACHE MISSES

0.0sSIM TIME

Parameters

MODEL NODES4

CACHE STORAGE150MB

Chaos Injection

CYCLE PROGRESS

Execution Logs

Telemetry Plot

System Topology

SYSTEM ANALYST

Environment ready. Analyzing emergent properties.

Ask about system modeling…

WEBSOCKET STREAM

0.0%

CACHE HIT RATIO

The observer agent samples the event queue every 20 simulated milliseconds and pushes telemetry over a single socket. The plot is the live feed, not a replay.

Requests served0

Cache misses0

Unique keys0

Avg latency0.0ms

Telemetry Plot

STREAMING

System Topology

Every message crosses the network agent. Latency, loss and partition are simulated, not assumed.

4 NODES 150 MB LRU CHAOS ARMED

HIT RATIO 87.4%

CHAOS MONKEY · NODE_2 TERMINATED

Load Balancer

Cache-00

Cache-01

Cache-02

Cache-03

Database

Execution Logs

EVENT QUEUE · HEAPQ

Agent Registry

load_balancerrouting

node_0142 / 18

node_1168 / 21

node_2terminated

node_3151 / 24

database163 reads

observersampling

Queue State

Events processed0

Pending events0

Invalidations0

Sim clock0.0s

Emergent, not scripted Thundering herds and cascading misses arise from agent rules alone — nothing in the log is authored.

System Analyst

The dashboard posts the live metrics envelope to /ai/analyze. The analyst reads the same numbers you do and explains what the agents actually did.

POST /ai/analyze

groq · llama-3.3-70b

{
  "query": "why did the hit ratio drop?",
  "metrics": {
    "hits": 1121, "misses": 163,
    "unique_keys": 240,
    "agent_stats": {
      "node_2": { "alive": false }
    }
  }
}

SYSTEM ANALYST

CONNECTED

Ask about system modeling…

Modeling Analytics

SYSTEM MODEL VALIDATED

AVERAGE HIT RATIO0.0%

TOTAL REQUESTS0

UNIQUE KEYS0

ACTIVE NODES0

NODE PERFORMANCE DISTRIBUTION

TRAFFIC COMPOSITION

Hits87.3%

Misses12.7%

SYSTEM CAPABILITY RADAR

PYTHON · DISCRETE-EVENT ENGINE FASTAPI WEBSOCKET TELEMETRY LRU CACHE · O(1) NEXT.JS 16 REACT 19 MATERIAL UI RECHARTS FRAMER MOTION GROQ LLAMA-3.3-70B

CACHENET

Agent-based distributed cache simulator

abm-project-gray.vercel.app