Differential Grey Wolf Load-Balanced Stochastic Bellman Deep Reinforced Resource Allocation in Fog Environment

Algorithm 2

Stochastic Bellman Gradient Deep Reinforcement Learning-based Resource Allocation.

	Input: Dataset “,” task “,” fog nodes “”
	Output: Energy minimized optimal resource allocation
(1)	Begin
(2)	For each dataset “” with task “” and fog nodes “”
	//State space
(3)	Load balancer acquires the input from fog environment “”
(4)	Mathematically formulate data size as in equation (16)
(5)	Mathematically formulate waiting time as in equation (17)
(6)	Mathematically formulate queue length as in equation (18)
	//Action space
(7)	For each action “” with the consolidated state “”
(8)	If task “” generated by fog node “” is executed locally
(9)	Then “”
(10)	Else “”
(11)	End if
(12)	If Task “” generated by fog node “” is executed on the host node
(13)	Then “”
(14)	Else “”
(15)	End if
(16)	If Task “” generated by fog node “” is executed by neighbor
(17)	Then“”
(18)	Else “”
(19)	End if
	//Reward function
(20)	For each action “” with the consolidated state “” and task “” generated by fog node “”
(21)	Total all the obtained rewards as in equation (19)
(22)	Measure stochastic bellman gradient optimality function as in equation (20)
(23)	End for
(24)	End for
(25)	End