A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning (DRL) for Mobile Edge Computing (MEC) | This algorithm captures the dynamics of the MEC environment by integrating the Dueling Double Deep Q-Network (D3QN) model with Long Short-Term Memory (LSTM) networks.

This advanced and complex project implements an AI-powered optimization system for 5G Open RAN networks. Using machine learning and deep learning, the system optimizes network performance by detecting anomalies, predicting network traffic, and dynamically allocating resources.

Neural Network Hyper-parameter Tuning with Genetic Algorithms 🤖 🧬

A simple guide to optimize your ping in CS2 through Windows Registry tweaks and in-game settings adjustments by @OhLasFar on Twitter.

🔍To choose the best CloudFront IPs for achieving fast⚡ and low-latency connections

A tool box for deploy time structure optimization for some modern CNN structures

Supply chain network optimization for oilseed processing (bio-diesel application)

🌐 Scripts to configure persistent MTU settings for Tailscale on Linux and Windows, enhancing network performance with automated and customizable setups.

Network Optimisation solver

GPU implementation of Floyd-Warshall and R-Kleene algorithms to solve the All-Pairs-Shortest-Paths(APSP) problem on Graphs. Code includes random graph generators and benchmarking/plotting scripts.

Constrained optimization problems, including linear,network, dynamic,integer, and nonlinear programming, decision trees, queueing theory and Markov decision processes.

Master's Thesis: Dynamic Quality Estimation of Wireless Links with Autonomous Agents

A tool for testing and optimizing Cloudflare IPs using ITDOG nodes across Chinese ISPs, automatically selecting best-performing IPs and updating Cloudflare Worker configuration.

Network optimization problem solved using Gurobi Python

Solving linear programming using python optimizer interface. This repo allows you to add multiple decision vars and constraints etc. in a very easy way.

Formulate the network optimization problem as a discrete model, identifying mathematically the variables and constraints associated with the network. Formulate (mathematically) and solve a non-linear optimization problem based on real (or realistic) world data.

Network-flows: a c++ command line tool for network optimization problems

Implementation of 2 apps, using Mininet as a Virtual Network Simulator and OpenDaylight Software-Defined-Networking Controller.

In this project, I built several optimization models to determine production level, manage shipment, and maximize thesis points. I also built queueing models to analyze queueing systems in a hospital and made recommendations to meet the criteria, and simulation models to analyze system performance under uncertainty.

4 sample solutions on Network optimization problems.