Open Source C++ Router Firmware for Mac

High-performance routing engine written in C++14 designed to run on OpenStreetMap data. There are several services available via HTTP API, C++ library interface and NodeJs wrapper. Nearest, snaps coordinates to the street network and returns the nearest matches. Route finds the fastest route between coordinates. Table computes the duration or distances of the fastest route between all pairs of supplied coordinates. Match snaps noisy GPS traces to the road network in the most plausible way. Trip solves the Traveling Salesman Problem using a greedy heuristic. Tile generates Mapbox Vector Tiles with internal routing metadata. To quickly try OSRM use our demo server which comes with both the backend and a frontend on top. The easiest and quickest way to setup your own routing engine is to use Docker images we provide. We base our Docker images (backend, frontend) on Debian and make sure they are as lightweight as possible.

Being meticulously optimized for speed and memory footprint, µWebSockets is fast enough to do encrypted TLS 1.3 messaging quicker than most alternative servers can do even unencrypted, cleartext messaging. Furthermore, we partake in Google's OSS-Fuzz with a ~95% daily fuzzing coverage with no sanitizer issues. LGTM scores us flawless A+ from having zero CodeQL alerts and we compile with pedantic warning levels. µWebSockets is written entirely in C & C++ but has a seamless integration for Node.js backends. This allows for rapid scripting of powerful apps, using widespread competence. We've been fully standards compliant with a perfect Autobahn|Testsuite score since 2016. Companies with everything to lose rely on µWebSockets on a daily basis - we power the trading APIs of Bitfinex.com & Kraken.com, handling volumes of multiple billions of USD every day. Other companies include Trello, where µWebSockets is serving their 50 million users with real-time board updates.

The Common Open Research Emulator (CORE) is a tool for emulating networks on one or more machines. You can connect these emulated networks to live networks. CORE consists of a GUI for drawing topologies of lightweight virtual machines, and Python modules for scripting network emulation.

The shortest path problem is solved by many methods. Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. The basic idea of the Ant System is that virtual ants are exploited for finding paths with a specific property, e.g., short distance between physical nodes, in the same way nature guides physical ants. Development takes place at https://github.com/zfoxer/AcoPath

The shortest path problem is solved by many methods. Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. Learning Automata (LA) are adaptive mechanisms requiring feedback from the executing environment to converge to certain states. In the context of network routing, LA residing at intermediate nodes along a path, exploit feedback from the destination node for reducing, e.g., path's length. According to topology’s resources like the node and edge numbers, the proper number of iterations must be used. More iterations lead to paths with higher probability of being optimal but more computing resources are consumed. Development takes place at https://github.com/zfoxer/LaPath

Program will send wake on lan packet to machine on ethernet using the MAC address. A configuration file MWakeOnLan.ini can be used to hold name/ethernet values for easy command line execution. The configuration file consists of name=etheraddress pairs. A # marker is used for comment lines Flags: -l | List All Name/Value Pairs Example 1: C:> MWakeOnLan 00:2E:27:4B:20:34 Example 2: C:> MWakeOnLan server1 server2

The shortest path problem is solved by many methods. Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. The basic idea of Particle Swarm Optimization is the emulation of the social behaviour of, e.g., a flock of birds, as a stochastic optimisation method. Specifically, a particle is an entity representing a solution in the search space. Several particles cooperate inside an algorithmic flow to occupy positions close to the best solution. Development takes place at https://github.com/zfoxer/PsoPath