TY - UNPB
T1 - Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes
AU - Birman, Yoni
AU - Ido, Ziv
AU - Katz, Gilad
AU - Shabtai, Asaf
PY - 2020/7/1
Y1 - 2020/7/1
N2 - Multi-objective task scheduling (MOTS) is the task scheduling while
optimizing multiple and possibly contradicting constraints. A
challenging extension of this problem occurs when every individual task
is a multi-objective optimization problem by itself. While deep
reinforcement learning (DRL) has been successfully applied to complex
sequential problems, its application to the MOTS domain has been stymied
by two challenges. The first challenge is the inability of the DRL
algorithm to ensure that every item is processed identically regardless
of its position in the queue. The second challenge is the need to manage
large queues, which results in large neural architectures and long
training times. In this study we present MERLIN, a robust, modular and
near-optimal DRL-based approach for multi-objective task scheduling.
MERLIN applies a hierarchical approach to the MOTS problem by creating
one neural network for the processing of individual tasks and another
for the scheduling of the overall queue. In addition to being smaller
and with shorted training times, the resulting architecture ensures that
an item is processed in the same manner regardless of its position in
the queue. Additionally, we present a novel approach for efficiently
applying DRL-based solutions on very large queues, and demonstrate how
we effectively scale MERLIN to process queue sizes that are larger by
orders of magnitude than those on which it was trained. Extensive
evaluation on multiple queue sizes show that MERLIN outperforms multiple
well-known baselines by a large margin (>22%).
AB - Multi-objective task scheduling (MOTS) is the task scheduling while
optimizing multiple and possibly contradicting constraints. A
challenging extension of this problem occurs when every individual task
is a multi-objective optimization problem by itself. While deep
reinforcement learning (DRL) has been successfully applied to complex
sequential problems, its application to the MOTS domain has been stymied
by two challenges. The first challenge is the inability of the DRL
algorithm to ensure that every item is processed identically regardless
of its position in the queue. The second challenge is the need to manage
large queues, which results in large neural architectures and long
training times. In this study we present MERLIN, a robust, modular and
near-optimal DRL-based approach for multi-objective task scheduling.
MERLIN applies a hierarchical approach to the MOTS problem by creating
one neural network for the processing of individual tasks and another
for the scheduling of the overall queue. In addition to being smaller
and with shorted training times, the resulting architecture ensures that
an item is processed in the same manner regardless of its position in
the queue. Additionally, we present a novel approach for efficiently
applying DRL-based solutions on very large queues, and demonstrate how
we effectively scale MERLIN to process queue sizes that are larger by
orders of magnitude than those on which it was trained. Extensive
evaluation on multiple queue sizes show that MERLIN outperforms multiple
well-known baselines by a large margin (>22%).
KW - Computer Science - Machine Learning
KW - Computer Science - Cryptography and Security
KW - Statistics - Machine Learning
M3 - פרסום מוקדם
BT - Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes
ER -