MAPO: Multi-Agent Predictive Optimizer

# MAPO: Multi-Agent Predictive Optimizer

A multi-agent LLM framework for real-time web service alert attribution and distributed deployment optimization.

## Overview

MAPO (Multi-Agent Predictive Optimizer) is a multi-agent framework designed for modern distributed web service environments. It combines large language models (LLMs), probabilistic reasoning, and multi-agent coordination to solve two critical problems:

- **Real-time alert attribution**: identifying the most likely root cause of system alerts- **Distributed deployment optimization**: improving service placement and resource allocation under constraints

The framework is designed to be scalable, adaptive, and effective in dynamic and resource-constrained environments.

## Key Features

- **LLM-enabled multi-agent architecture**- **Real-time alert attribution**- **Constraint-aware deployment planning**- **Probabilistic alert prioritization**- **Adaptive exploration and uncertainty-aware refinement**- **Scalable distributed decision-making**

## Architecture

MAPO consists of three core modules:

### 1. Constraint-Aware Deployment PlannerOptimizes service deployment across distributed nodes while respecting system constraints such as resource capacity, latency requirements, and dependency structure.

### 2. Event-Driven Attribution RouterDynamically maps incoming alerts and events to probable root causes using probabilistic inference and event-driven routing.

### 3. Probabilistic Alert Prioritization UnitAssigns priority scores to alerts based on severity and uncertainty, helping the system focus on the most critical incidents first.

## Methodology

The MAPO framework models alert attribution and deployment optimization as a joint multi-objective problem. It introduces:

- **Autonomous Agent Exploration Mechanism**- **Uncertainty-Aware Refinement**- **Hierarchical multi-agent coordination**- **Probabilistic scoring and routing**

These components work together to improve operational reliability, resource efficiency, and response time.

## Experimental Results

According to the reported experiments, MAPO achieves strong performance across multiple benchmarks, including:

- Multi-Agent System Performance Dataset- Realtime Web Service Alert Logs- Distributed Deployment Optimization Metrics- LLM-Based Alert Attribution Dataset

### Highlights

- Higher accuracy, precision, recall, and F1-score than several baseline methods- Improved real-time performance- Better scalability as the number of agents increases- Strong robustness under high-load conditions

### Real-Time Performance

| Method | Avg Latency (ms) | Throughput (alerts/s) | High-load Latency (ms) ||--------|------------------:|----------------------:|-----------------------:|| ResNet-based Method | 45.3 | 220 | 78.6 || Transformer-based Method | 52.7 | 195 | 91.2 || Reinforcement Learning Baseline | 60.4 | 180 | 105.5 || **MAPO (Ours)** | **28.6** | **340** | **49.8** |

### Scalability

| Number of Agents | Accuracy (%) | Avg Latency (ms) | Throughput (alerts/s) ||-----------------:|-------------:|-----------------:|----------------------:|| 10 | 88.7 | 26.1 | 360 || 50 | 89.2 | 27.8 | 350 || 100 | 89.4 | 28.6 | 340 || 200 | 89.3 | 30.2 | 325 || 500 | 89.1 | 34.7 | 300 |

## Project Structure

```bashMAPO/├── README.md├── docs/├── src/├── configs/├── scripts/├── data/├── experiments/└── tests/