This project is a simulation of various distributed computing algorithms implemented in Scala using the Akka framework. We use lightbend telemetry to monitor the performance of the simulation. The project includes the following features:

• Automated simulation of distributed algorithms based on a simulation plan
• Support for message-passing and shared memory algorithms
• Integration with Lightbend Telemetry for monitoring the performance of the simulation
• Visualization of the simulation results using Lightbend Telemetry on Prometheus and Grafana
• Integration tests for the simulation
• Unit tests for the shared memory algorithms

Before you begin, ensure you have met the following requirements:

• You have installed version 17 of Java, supported version of Scala, and sbt.

1. Clone the repository to your local machine using git clone.
2. Navigate to the project directory using cd <project-directory>.
3. Create a lightbend.sbt file in the project root directory with your Lightbend Telemetry credentials. For more information on obtaining these credentials and setting up Lightbend Telemetry, refer to the official documentation.
4. Run sbt in the command line to start the sbt shell.
5. Inside the sbt shell, execute clean compile run <simplan.json> to clean the project, compile the source code, and run the project with the specified simulation plan.

This project includes both unit tests and integration tests. To run them, use the following command in the sbt shell:

sbt clean compile test

• Test cases for all algorithms (Integration Testing Based on Log Analysis). Directory src/test/scala/com/distcomp/mutex.
• Test cases for Shared Memory Actors (Unit Testing checks for correctness of shared memory) Directory src/test/scala/com/distcomp/sharedmemory.

• Reads simulation plan from a JSON file
• Sends message to initialise the network for simulation
• Waits for nodes to register themself, get ready for simulation
• Changes node behaviour based on simulation plan
• Sends message to start simulation
• Waits for simulation to complete
• Weight throwing for termination detection (Slight Variation)
• Moves to next simulation plan

• Ricart Agarwal
• Ricart Agarwal Carvalho Extension
• Raymonds Algorithm
  • Echo Algorithm for Spanning Tree Building
• Agrawal El Abbadi
  • Strongly Accurate Failure Detector (Based on heartbeats)

• Peterson's Algorithm (Two Process)
  • Uses a custom actor for maintianing shared memory (Petersons Shared Mem Actor)
• Peterson's Algorithm (Tournament for N Processes)
  • Uses a custom actor for maintianing shared memory (Peterson Tournament Shared Mem Actor)
• Bakery Algorithm
  • Uses a custom actor for maintianing shared memory (Bakery Shared Mem Actor)
• Test and Set Lock Algorithm
  • Uses a custom actor for maintianing shared memory (Test and Set Shared Mem Actor)
• Test and Test and Set Lock Algorithm
  • Uses a custom actor for maintianing shared memory (Test and Test and Set Shared Mem Actor)

Simulation Plan

• Chang Roberts
• Franklin
• Dolev Klawe Rodeh
• Tree Election
• Echo Election

Election Plan

• Chandy-Misra Algorithm with Djikstra-Scholten Termination
• Merlin-Segall Algorithm
• Touegs Algorithm - Floyd Warshall Algorithm in Distributed Setting
• Frederickson Algorithm

Routing Plans

The below image shows the telemetry data for the simulation run with the mutexsimplan.json simulation plan.