EdgeTuner: Fast scheduling algorithm tuning for dynamic edge-cloud workloads and resources
Edge-cloud jobs are rapidly prevailing in many application domains, posing the challenge of
using both resource-strenuous edge devices and elastic cloud resources. Efficient resource …
using both resource-strenuous edge devices and elastic cloud resources. Efficient resource …
A2C-DRL: Dynamic Scheduling for Stochastic Edge-Cloud Environments Using A2C and Deep Reinforcement Learning
J Lu, J Yang, S Li, Y Li, W Jiang… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Resource management challenges frequently manifest in systems and networks as tough
online decision tasks, for which the proper solution is dependent on an understanding of the …
online decision tasks, for which the proper solution is dependent on an understanding of the …
TapFinger: Task placement and fine-grained resource allocation for edge machine learning
Machine learning (ML) tasks are one of the major workloads in today's edge computing
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …
Tailored learning-based scheduling for kubernetes-oriented edge-cloud system
Kubernetes (k8s) has the potential to merge the distributed edge and the cloud but lacks a
scheduling framework specifically for edge-cloud systems. Besides, the hierarchical …
scheduling framework specifically for edge-cloud systems. Besides, the hierarchical …
Online dispatching and fair scheduling of edge computing tasks: A learning-based approach
The emergence of edge computing can effectively tackle the problem of large transmission
delays caused by the long-distance between user devices and remote cloud servers. Users …
delays caused by the long-distance between user devices and remote cloud servers. Users …
Task placement and resource allocation for edge machine learning: a GNN-based multi-agent reinforcement learning paradigm
Machine learning (ML) tasks are one of the major workloads in today's edge computing
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …
Deep adversarial imitation reinforcement learning for QoS-aware cloud job scheduling
Although cloud computing is one of the promising technologies for online business services,
how to schedule real-time cloud jobs with high quality of service (QoS) is still challenging …
how to schedule real-time cloud jobs with high quality of service (QoS) is still challenging …
[HTML][HTML] Deep reinforcement learning-based task scheduling in iot edge computing
Edge computing (EC) has recently emerged as a promising paradigm that supports resource-
hungry Internet of Things (IoT) applications with low latency services at the network edge …
hungry Internet of Things (IoT) applications with low latency services at the network edge …
Imitation learning enabled fast and adaptive task scheduling in cloud
Studies of resource provision in cloud computing have drawn extensive attention, since
effective task scheduling solutions promise an energy-efficient way of utilizing resources …
effective task scheduling solutions promise an energy-efficient way of utilizing resources …
Service management and energy scheduling toward low-carbon edge computing
Edge computing has become an alternative low-latency provision of cloud computing thanks
to its close-proximity to the users, and the geo-distribution nature of edge servers enables …
to its close-proximity to the users, and the geo-distribution nature of edge servers enables …