GARLSched: Generative adversarial deep reinforcement learning task scheduling optimization for large-scale high performance computing systems
Efficient task scheduling has become increasingly complex as the number and type of tasks
proliferate and the size of computing resource grows in large-scale distributed high …
proliferate and the size of computing resource grows in large-scale distributed high …
A deep reinforcement learning approach to resource management in hybrid clouds harnessing renewable energy and task scheduling
The use of cloud computing for delivering application services over the Internet has gained
rapid traction. Since the beginning of the COVID-19 global pandemic, the work from home …
rapid traction. Since the beginning of the COVID-19 global pandemic, the work from home …
Deep learning-based job placement in distributed machine learning clusters with heterogeneous workloads
Nowadays, most leading IT companies host a variety of distributed machine learning (ML)
workloads in ML clusters to support AI-driven services, such as speech recognition, machine …
workloads in ML clusters to support AI-driven services, such as speech recognition, machine …
Drag-jdec: A deep reinforcement learning and graph neural network-based job dispatching model in edge computing
Z Yu, W Liu, X Liu, G Wang - 2021 IEEE/ACM 29th International …, 2021 - ieeexplore.ieee.org
The emergence of edge computing eases latency pressure in remote cloud and computing
pressure of terminal devices, providing new solutions for real-time applications. Jobs of end …
pressure of terminal devices, providing new solutions for real-time applications. Jobs of end …
AutoSched: An Adaptive Self-configured Framework for Scheduling Deep Learning Training Workloads
Modern Deep Learning Training (DLT) schedulers in GPU datacenters are designed to be
very sophisticated with many configurations. These configurations need to be adjusted …
very sophisticated with many configurations. These configurations need to be adjusted …
SCHED²: Scheduling Deep Learning Training via Deep Reinforcement Learning
Today's companies and organizations build GPU clusters for efficient deep learning training
(DLT). However, the inherent heterogeneity of DLT workloads makes it challenging to …
(DLT). However, the inherent heterogeneity of DLT workloads makes it challenging to …
DCloud: deadline-aware resource allocation for cloud computing jobs
With the tremendous growth of cloud computing, it is increasingly critical to provide
quantifiable performance to tenants and to improve resource utilization for the cloud …
quantifiable performance to tenants and to improve resource utilization for the cloud …
[HTML][HTML] Optimizing task offloading and resource allocation in edge-cloud networks: a DRL approach
Edge-cloud computing is an emerging approach in which tasks are offloaded from mobile
devices to edge or cloud servers. However, Task offloading may result in increased energy …
devices to edge or cloud servers. However, Task offloading may result in increased energy …
A deep reinforcement learning-based task scheduling algorithm for energy efficiency in data centers
Cloud data centers provide end-users with a wide range of application scenarios, including
scientific computing, smart grids, etc. The number and size of data centers have rapidly …
scientific computing, smart grids, etc. The number and size of data centers have rapidly …
Enhancing generalization of computation offloading policies in novel mobile edge computing environments by exploiting experience utility
T Ren, J Niu, Y Qiu - Journal of Systems Architecture, 2022 - Elsevier
Recent years have witnessed the booming development of mobile devices (MDs), along
with the surging popularity of mobile applications. Despite the unceasing progress of MDs …
with the surging popularity of mobile applications. Despite the unceasing progress of MDs …