Gcnscheduler: Scheduling distributed computing applications using graph convolutional networks
M Kiamari, B Krishnamachari - … of the 1st International Workshop on …, 2022 - dl.acm.org
We provide a highly-efficient solution to the classical problem of scheduling task graphs
corresponding to complex applications on distributed computing systems. A number of …
corresponding to complex applications on distributed computing systems. A number of …
Graph convolutional network-based scheduler for distributing computation in the internet of robotic things
Existing solutions for scheduling arbitrarily complex distributed applications on networks of
computational nodes are insufficient for scenarios where the network topology is changing …
computational nodes are insufficient for scenarios where the network topology is changing …
Parameterized Task Graph Scheduling Algorithm for Comparing Algorithmic Components
J Coleman, RV Agrawal, E Hirani… - arXiv preprint arXiv …, 2024 - arxiv.org
Scheduling distributed applications modeled as directed, acyclic task graphs to run on
heterogeneous compute networks is a fundamental (NP-Hard) problem in distributed …
heterogeneous compute networks is a fundamental (NP-Hard) problem in distributed …
Jupiter: a networked computing architecture
Modern latency-sensitive applications such as real-time multi-camera video analytics
require networked computing to meet the time constraints. We present Jupiter, an open …
require networked computing to meet the time constraints. We present Jupiter, an open …
Comparing Task Graph Scheduling Algorithms: An Adversarial Approach
J Coleman, B Krishnamachari - arXiv preprint arXiv:2403.07120, 2024 - arxiv.org
Scheduling a task graph representing an application over a heterogeneous network of
computers is a fundamental problem in distributed computing. It is known to be not only NP …
computers is a fundamental problem in distributed computing. It is known to be not only NP …
Readys: A reinforcement learning based strategy for heterogeneous dynamic scheduling
In this paper, we propose READYS, a reinforcement learning algorithm for the dynamic
scheduling of computations modeled as a Directed Acyclic Graph (DAGs). Our goal is to …
scheduling of computations modeled as a Directed Acyclic Graph (DAGs). Our goal is to …
Benchmarking and comparison of the task graph scheduling algorithms
The problem of scheduling a parallel program represented by a weighted directed acyclic
graph (DAG) to a set of homogeneous processors for minimizing the completion time of the …
graph (DAG) to a set of homogeneous processors for minimizing the completion time of the …
DGCQN: a RL and GCN combined method for DAG scheduling in edge computing
B Qin, Q Lei, X Wang - The Journal of Supercomputing, 2024 - Springer
Edge computing is an emerging paradigm that enables low-latency and high-performance
computing at the network edge. However, effectively scheduling complex and …
computing at the network edge. However, effectively scheduling complex and …
GCN-based reinforcement learning approach for scheduling DAG applications
Applications in various fields such as embedded systems or High-Performance-Computing
are often represented as Directed Acyclic Graphs (DAG), also known as taskgraphs. DAGs …
are often represented as Directed Acyclic Graphs (DAG), also known as taskgraphs. DAGs …
Looking beyond {GPUs} for {DNN} scheduling on {Multi-Tenant} clusters
Training Deep Neural Networks (DNNs) is a popular workload in both enterprises and cloud
data centers. Existing schedulers for DNN training consider GPU as the dominant resource …
data centers. Existing schedulers for DNN training consider GPU as the dominant resource …