SYSTEM AND METHOD FOR REDUCING STATE SPACE IN REINFORCED LEARNING BY USING DECISION TREE CLASSIFICATION

摘要

An automatic scaling system and method for reducing state space in reinforced learning for automatic scaling of a multi-tier application uses a state decision tree that is updated with new states of the multi-tier application. When a new state of the multi-tier application is received, the new state is placed in an existing node of the state decision tree only if a first attribute of the new state is same as a first attribute of any state contained in the existing node and a second attribute of the new state is sufficiently similar to a second attribute of each existing state contained in the existing node based on a similarity measurement of the second attribute of each state contained in the existing node with the second attribute of the new state.

主权项

1. A method for reducing state space in reinforced learning for automatic scaling of a multi-tier application, the method comprising:
receiving a new state of the multi-tier application to be added to a state decision tree for the multi-tier application, the new state including a first attribute and a second attribute; placing the new state in an existing node of the state decision tree only if the first attribute of the new state is same as the first attribute of any state contained in the existing node and the second attribute of the new state is sufficiently similar to a second attribute of each existing state contained in the existing node based on a similarity measurement of the second attribute of each state contained in the existing node with the second attribute of the new state; and executing the reinforced learning using the state decision tree with the new state to automatically scale the multi-tier application.