Complexity / 2020 / Article / Tab 1

Research Article

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

Table 1

Notations in the scheduling system model.

NotationMemoType

The duration of period of task schedulingModel parameter
The duration of period of resource optimizationModel parameter
The priority function to estimate the priority of task iFunction
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 optimizationVariable
The state, action, and reward vector for task scheduling agentVariable
The state, action, and reward vector for resource optimization agentVariable
, Calibration parameters to adjust the influence of average task priority and active virtual machine proportionModel parameter
, Calibration parameters to tune the proportion of the active virtual machine and the proportion of idle virtual machinesModel 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
MThe number of virtual machines in cloud serverModel parameter
KThe ratio of to Model parameter
,,,The hyperparameter of A2C algorithmHyperparameter

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