haxnovr / osmotic-algo

Osmotic Distributed Computing Algorithm for Edge Applications

Osmotic Computing Framework

This repository implements a framework for simulating task scheduling in an osmotic computing or fog cluster computing environment. It leverages a hybrid scheduling approach that combines Round Robin (RR) and Shortest Job Next (SJN) algorithms to optimize task allocation and execution across a network of fog servers.

Key Features:

• Task Generation: Randomly generates tasks with varying execution times and server affinities (representing preference for specific fog servers).
• Worker Simulation: Creates a pool of fog servers with on/off states.
• Hybrid RR-SJN Scheduling: Implements a two-phase scheduling strategy:
  • Phase 1: Round Robin (RR): Initially distributes tasks equally among available servers using RR.
  • Phase 2: Shortest Job Next (SJN): Applies SJN scheduling to remaining tasks on each server, prioritizing those with the shortest execution times.
• Osmotic Layer: Provides a mechanism for task assignment based on server affinities (not implemented in the current version).

Getting Started

1. Clone the Repository:

   git clone https://github.com/your-username/osmotic-computing-framework.git

2. Install Dependencies: Ensure you have Python 3.x and any additional libraries required (check requirements.txt if present).
3. Run the Script:

   python main.py

User Input:

The script prompts for the following user inputs:

• Time Quantum: Define the time slice for each task in RR scheduling.
• Total Servers: Specify the number of fog servers in the network.
• Total Tasks: Indicate the number of tasks to be generated.

Output:

The script displays the following information:

• Worker (server) details: server number and status (active/inactive).
• Task details: task ID, execution time, and server affinity (if implemented).
• Task execution order: Lists the order of task execution on each server, including task ID, execution time, and server number.

Implementation Details:

• Task Class: Represents a task with an ID, execution time, and (optional) server affinity.
• Worker Class: Represents a fog server with an application number and status.
• osmoticLayer Class: Currently serves as a placeholder for future osmotic layer functionality.
• round_robin Function: Implements the Round Robin scheduling algorithm.
• shortest_job_next Function: Implements the Shortest Job Next scheduling algorithm.
• hybrid_rr_sjn Function: Combines RR and SJN scheduling for task allocation.
• randomProcessGenerator Function: Generates random tasks with varying execution times and server affinities.
• randomWorkerGenerator Function: Creates a pool of fog servers with random initial states.
• printWorkerInfo Function: Displays information about individual fog servers.
• printTask Function: Prints details of all generated tasks.

Future Enhancements:

• Implement the osmotic layer to consider server affinities during task assignment.
• Incorporate support for different task types and priorities.
• Explore other scheduling algorithms or develop a hybrid approach that adapts dynamically.
• Add visualization tools to depict task execution and resource utilization.