Research Article
Differential Grey Wolf Load-Balanced Stochastic Bellman Deep Reinforced Resource Allocation in Fog Environment
Algorithm 1
Differential Evolution-based Grey Wolf Optimization.
| Input: Dataset “” task “,” fog nodes “” | | Output: Latency-minimized optimal load balancing | (1) | Initialize “,” “ ,” “” | (2) | Begin | (3) | For each dataset “” with task “” and fog nodes “” | (4) | Formulate cloud data centers with “” numbers of servers “” as in equations (1) and (2) | (5) | Model “” processing different numbers of tasks “” as in equations (3) and (4) | (6) | Update positions of wolves or the computing nodes by handling overutilization of fog as in equations (5) and (6) | (7) | Handle under-utilized fog detection using equations (9–11) | (8) | Estimate the final position vectors of the current individual as in equations (12–14) | (9) | Handle migration between virtual machines as in equation (15) | (10) | Return optimal load balancing fog computing nodes | (11) | End for | (12) | End |
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