[PDF][PDF] Deepweave: Accelerating job completion time with deep reinforcement learning-based coflow scheduling
P Sun, Z Guo, J Wang, J Li, J Lan, Y Hu - Proceedings of the Twenty-Ninth …, 2021 - ijcai.org
To improve the processing efficiency of jobs in distributed computing, the concept of coflow
is proposed. A coflow is a collection of flows that are semantically correlated in a multi-stage …
is proposed. A coflow is a collection of flows that are semantically correlated in a multi-stage …
Flexible cyclic queuing and forwarding for time-sensitive software-defined networks
Time-Sensitive Networking (TSN) is emerging to support critical real-time applications in
Industry 4.0. Recent proposals leverage Cyclic Queuing and Forwarding (CQF) to achieve …
Industry 4.0. Recent proposals leverage Cyclic Queuing and Forwarding (CQF) to achieve …
Online routing and scheduling for time-sensitive networks
Recent proposals leverage Time-Aware Shaper (TAS) to achieve precise transmission in
Time-Sensitive Networking (TSN). However, most of the proposals require the information of …
Time-Sensitive Networking (TSN). However, most of the proposals require the information of …
Glint: Decentralized federated graph learning with traffic throttling and flow scheduling
Federated learning has been proposed as a promising distributed machine learning
paradigm with strong privacy protection on training data. Existing work mainly focuses on …
paradigm with strong privacy protection on training data. Existing work mainly focuses on …
Co-Scheduler: A coflow-aware data-parallel job scheduler in hybrid electrical/optical datacenter networks
To support higher demand for datacenter networks, networking researchers have proposed
hybrid electrical/optical datacenter networks (Hybrid-DCN) that leverages optical circuit …
hybrid electrical/optical datacenter networks (Hybrid-DCN) that leverages optical circuit …
A distributed framework for task offloading in edge computing networks of arbitrary topology
An important issue in an edge computing (EC) network is to increase the utilities of the end
users concurrently accessing the computation resources. In this paper, we consider the task …
users concurrently accessing the computation resources. In this paper, we consider the task …
Coflow scheduling in data centers: routing and bandwidth allocation
In distributed computing frameworks like MapReduce, Spark, and Dyrad, a coflow is a set of
flows transferring data between two stages of a job. The job cannot start its next stage unless …
flows transferring data between two stages of a job. The job cannot start its next stage unless …
Information cofreshness-aware grant assignment and transmission scheduling for internet of things
The proliferation of Internet of Things (IoT) applications has prompted the continuous
increase of research efforts in recent years. In light of the diversified use cases and service …
increase of research efforts in recent years. In light of the diversified use cases and service …
Optimizing task placement and online scheduling for distributed GNN training acceleration
Training Graph Neural Networks (GNN) on large graphs is resource-intensive and time-
consuming, mainly due to the large graph data that cannot be fit into the memory of a single …
consuming, mainly due to the large graph data that cannot be fit into the memory of a single …
Online scheduling of coflows by attention-empowered scalable deep reinforcement learning
With the abstraction of parallel data transmission flows being a coflow, data transmissions in
large-scale computing jobs can be modeled by a coflow directed acyclic graph (coflow DAG) …
large-scale computing jobs can be modeled by a coflow directed acyclic graph (coflow DAG) …