A Deep Reinforcement Learning Approach to the Optimization of Data Center Task Scheduling

Table 1

Notations in the scheduling system model.

Notation	Memo	Type
	The duration of period of task scheduling	Model parameter
	The duration of period of resource optimization	Model parameter
	The priority function to estimate the priority of task i	Function
	The start time of task scheduling which also represents the ID of period of task scheduling (the period is also called time slot)	Variable
	The start time of resource optimization which also represents the ID of period of resource optimization	Variable
	The state, action, and reward vector for task scheduling agent	Variable
	The state, action, and reward vector for resource optimization agent	Variable
,	Calibration parameters to adjust the influence of average task priority and active virtual machine proportion	Model parameter
,	Calibration parameters to tune the proportion of the active virtual machine and the proportion of idle virtual machines	Model parameter
,	The sum of the execution time of tasks arriving in period and the sum of the execution time of tasks not executed in period	Variable
,	The number of tasks arriving in period and the number of tasks not executed in period	Variable
M	The number of virtual machines in cloud server	Model parameter
K	The ratio of to	Model parameter
,,,	The hyperparameter of A2C algorithm	Hyperparameter