Welcome to the Cloud Computing Scheduling project! This project provides a cloud computing scheduling algorithm for efficient processing of Big Data using Memetic Algorithms. The implementation is in Java and utilizes the CloudSim framework for cloud computing simulation. Additionally, the project includes a comparison with the Genetic Algorithm (GA) for performance evaluation.

• Project Overview
• Getting Started
• Usage
• Simulating and Comparing Algorithms
• Contributing
• License

This project addresses the challenge of efficiently scheduling tasks in a cloud computing environment for Big Data processing. It employs Memetic Algorithms, a combination of genetic algorithms and local search techniques, to optimize task scheduling. The implementation is in Java and uses the CloudSim simulation framework.

• Cloud computing scheduling algorithm tailored for Big Data processing.
• Implementation in Java for flexibility and platform independence.
• Integration with CloudSim for cloud computing simulation.
• Comparative analysis with the Genetic Algorithm (GA) for performance evaluation.

Before you begin, ensure you have the following prerequisites:

• Java Development Kit (JDK) installed on your system.
• Familiarity with cloud computing concepts and Big Data processing.

1. Clone this repository:

2. git clone https://github.com/yourusername/cloud-scheduling-memetic.git cd cloud-scheduling-memetic

3. Set up your Java development environment, ensuring compatibility with CloudSim.

1. Configure Input Data:

• Prepare input data and task parameters specific to your cloud computing and Big Data processing scenario.
• Modify the Java code to customize input parameters and cloud infrastructure settings.

2. Run the Scheduling Algorithm:

• Execute the Java application to run the Memetic Algorithms-based cloud computing scheduling.
• The algorithm will optimize the scheduling of tasks across cloud resources.

3. Evaluate Performance:

• Analyze the performance metrics obtained from the scheduling algorithm.
• Compare the results with those obtained from the Genetic Algorithm (GA) for performance evaluation.

This project allows for simulating and comparing the performance of the Memetic Algorithms-based scheduling with the Genetic Algorithm (GA). To conduct these simulations:

1. Locate the GA simulation code (e.g., ga_simulation.java) within the project.
2. Configure input parameters, genetic operators, and constraints in the GA simulation code.
3. Execute the GA simulation to evaluate its scheduling performance.
4. Compare the results with those obtained from the Memetic Algorithms-based scheduling.

We encourage contributions to this project! If you'd like to contribute or report issues, please follow our Contribution Guidelines.