US Patent Application 17722042. METHOD AND SYSTEM FOR PERFORMING DOMAIN LEVEL SCHEDULING OF AN APPLICATION IN A DISTRIBUTED MULTI-TIERED COMPUTING ENVIRONMENT USING REINFORCEMENT LEARNING simplified abstract
METHOD AND SYSTEM FOR PERFORMING DOMAIN LEVEL SCHEDULING OF AN APPLICATION IN A DISTRIBUTED MULTI-TIERED COMPUTING ENVIRONMENT USING REINFORCEMENT LEARNING
Organization Name
Inventor(s)
William Jeffery White of Plano TX (US)
METHOD AND SYSTEM FOR PERFORMING DOMAIN LEVEL SCHEDULING OF AN APPLICATION IN A DISTRIBUTED MULTI-TIERED COMPUTING ENVIRONMENT USING REINFORCEMENT LEARNING - A simplified explanation of the abstract
- This abstract for appeared for US patent application number 17722042 Titled 'METHOD AND SYSTEM FOR PERFORMING DOMAIN LEVEL SCHEDULING OF AN APPLICATION IN A DISTRIBUTED MULTI-TIERED COMPUTING ENVIRONMENT USING REINFORCEMENT LEARNING'
Simplified Explanation
The abstract describes a method for managing a distributed multi-tiered computing environment. The method involves breaking down a service dependency graph associated with a scheduling job, assigning compute and network units to tasks, creating a Q-table using reinforcement Q-learning, determining critical and max learned paths, calculating start times for each task, and generating scheduling assignments.
Original Abstract Submitted
Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes decomposing, by a local controller associated with an DMC domain, a service dependency graph associated with a scheduling job; assigning normalized compute units and normalized network units to tasks included in the service dependency graph; generating a Q-table using the service dependency graph and reinforcement Q-learning; calculating a critical path and a max learned path using the Q-table and the service dependency graph; calculating the earliest start time and the latest start time for each task using the service dependency graph and the max learned path to obtain a plurality of earliest start time and latest start time pairs for each task; and generating scheduling assignments using the plurality of earliest start time and latest start time pairs for each task.
