1 research outputs found
Reinforcement Learning for Optimal Load Distribution Sequencing in Resource-Sharing System
Divisible Load Theory (DLT) is a powerful tool for modeling divisible load
problems in data-intensive systems. This paper studied an optimal divisible
load distribution sequencing problem using a machine learning framework. The
problem is to decide the optimal sequence to distribute divisible load to
processors in order to achieve minimum finishing time. The scheduling is
performed in a resource-sharing system where each physical processor is
virtualized to multiple virtual processors. A reinforcement learning method
called Multi-armed bandit (MAB) is used for our problem. We first provide a
naive solution using the MAB algorithm and then several optimizations are
performed. Various numerical tests are conducted. Our algorithm shows an
increasing performance during the training progress and the global optimum will
be acheived when the sample size is large enough