Fragmented Task Scheduling for Load-Balanced Fog Computing Based on Q-Learning

Algorithm 1

Q-Learning Based Load Balanced Task Scheduling.

Input:

Output: Delay violations, security violations, optimal Q-table values and policy

1 while All IoT tasks are scheduled do

2 Obtain by slicing the IoT task based on security requirement and task size ;

3 Use policy Equation (16) to select action ;

4 Get response time of selected fog node using (10).

5 Get of selected fog node .;

6 Calculate reward using (14)

7 Use (17) to update the Q-table;

8 Update the available resource state , system states , time step

9 end while