We present the Deep-EAS scheduler that learns efficient energy-aware scheduling strategies for workloads with different characteristics.

The Deep-EAS scheduler is presented, inspired by recent advances in applying deep reinforcement learning for resource management problems, ...

In this paper, inspired by recent advances in applying deep reinforcement learning for resource management problems, we present the Deep-EAS scheduler that.

Esmaili and Pedram developed Deep-EAS scheduler using deep reinforcement learning that performs energy-aware scheduling for workloads having different ...

Jan 2, 2020 · Bibliographic details on Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning.

Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning. A. Esmaili, and M. Pedram. CoRR, (2019 ).

Oct 22, 2024 · This paper investigates an energy-aware and low-latency oriented computing task scheduling problem in a Software-Defined Fog-IoT Network. First, ...

In this paper, we construct an energy consumption model based on resource utilization and a reinforcement learning model for energy-efficient scheduling under ...

Nov 1, 2023 · This paper proposes an energy-efficient scheduling scheme for multi-path TCP (MPTCP) in heterogeneous wireless networks, aiming to minimize energy consumption.

This study addresses these gaps by presenting an energy-efficient flexible job Shop scheduling with multi-autonomous guided vehicles (EFJS-AGV).