Skip to main content
This guide will walk you through the process of setting up the CloudSim-HO-Research-V2 environment, running a simulation, and analyzing the results.

​
Prerequisites

Before you begin, ensure you have the following installed:
  • Java Development Kit (JDK) 11 or higher.
  • Maven 3.6+ for building the project.
  • Git for version control.

​
1. Clone the Repository

Start by cloning the research framework to your local machine. 
git clone https://github.com/puneet-chandna/cloudsim-ho-research-V2.git
cd cloudsim-ho-research-V2

​
2. Build the Project

Use Maven to clean and build the project. This will download all necessary CloudSim Plus dependencies and compile the Hippopotamus Optimization algorithm. 
mvn clean install
If you encounter dependency issues, try running mvn clean install -U to force an update of snapshots.

​
3. Run the Experiment

You can run the full experimental suite using the provided scripts. These scripts automatically handle classpath configuration and execution. 
./run-experiment.sh

​
What happens when you run this?

  1. Configuration Loading: The system reads src/main/resources/Config.properties.
  2. Scenario Generation: It creates VM and Host lists based on the defined scenarios (Micro, Small, Medium).
  3. Optimization: The HippopotamusOptimization algorithm iteratively searches for the best VM-to-Host mapping.
  4. Simulation: CloudSim Plus simulates the execution of Cloudlets on the placed VMs.

​
4. Analyze Results

Once the simulation completes, results are generated in the results directory.
FileDescription
execution_log.csvdetailed logs of every simulation step.
scenarios/*Specific metrics for each scenario (Latency, Power, Cost).
convergence.csvTracks the fitness score over optimization iterations.

​
Understanding the Output

The console output provides real-time feedback on the optimization progress: 
[INFO] Starting HO optimization for 100 VMs and 20 hosts...
[INFO] Iteration 0: Best fitness = 0.8543
...
[INFO] Iteration 100: Best fitness = 0.1234
[INFO] Optimization completed.
A lower fitness score indicates a better balance of resource utilization, power consumption, and SLA adherence.