The invention discloses a cloud resource online scheduling method for distributed
machine learning tasks. The method comprises the following steps: firstly, starting from each time period, enabling acloud
resource broker to observe a price function of each resource of each
data center, and the data volume of each task needing to be trained; calculating the sum of all costs generated in the distributed
machine learning task scheduling process; representing the sum with integral
linear programming; decoupling the relationship between every two adjacent time periods of the relaxed
linear programming through a regularization method; converting an online planning problem of the whole T moment which is difficult to process into independent linear planning at each moment. In this way, real-timedecision making can be carried out without depending on future information; and finally, the designed independent
rounding method is adopted to solve the deployment scheme and the
data migration scheme of the computing node and the parameter
server of each
machine learning task at each moment, so that the total cost sum is minimum on the basis of ensuring the
task completion effect, and the scheduling effect is optimized.