Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System
Algorithm 1
Hybrid ES-ACO task scheduling algorithm.
Input 1: Set of subtasks, that is, T1, T2, T3, T4,…, Tk. 2. Set of virtual machines, that is, Vm1, Vm2, Vm3, Vm4,…, Vmq
Output: Mapping of the tasks to set the Vms (optimal schedule)
Step 1: Initialize the set of ant colonies
Step 2: Set the parameters of ACO
Step 3: Initialize the set of subtasks, that is, T1, T2, T3, T4,…, Tk
Step 4: Initialize the set of virtual machines, that is, Vm1, Vm2, Vm3, Vm4,…, Vmq
Step 5: Compute pheromone value
Step 6: Submit the Vm list, which was created successfully in the data center, and set of tasks to the cloud broker
Step 7: For 1 to Q do( )
Step 8: Generate nucleus Q[i]
Step 9: Initialize nucleus agent randomly
Step 10: End for
Step 12: Rm = 0
Step 13: Define the fitness function ft (Rm)
Step 14:
Step 15: Compute Gbest and Pbest
Step 16: While (Max iterations X) //
Step 17: Update the pheromone, that is, monitor the status of resources using the following equation
Step 18:
Step 19:
Step 20: i = 0
Step 21: Compute the fitness value for each nucleus of Q[i]
