TY - CHAP
T1 - Graph Neural Network-Based Coflow Scheduling in Data Center Networks
AU - Guo, Zehua
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - In this chapter, we introduce DeepWeave, a DRL framework to generate coflow scheduling policies. DeepWeave works for both the intra-coflow scheduling and inter-coflow scheduling. To improve the inter-coflow scheduling ability in the job, DeepWeave employs a GNN to process directed-acyclic graph information. DeepWeave learns from the historic workload trace to train the neural networks of the DRL agent and encodes the scheduling policy in the neural networks, which make coflow scheduling decisions without expert knowledge or a pre-assumed model.
AB - In this chapter, we introduce DeepWeave, a DRL framework to generate coflow scheduling policies. DeepWeave works for both the intra-coflow scheduling and inter-coflow scheduling. To improve the inter-coflow scheduling ability in the job, DeepWeave employs a GNN to process directed-acyclic graph information. DeepWeave learns from the historic workload trace to train the neural networks of the DRL agent and encodes the scheduling policy in the neural networks, which make coflow scheduling decisions without expert knowledge or a pre-assumed model.
UR - http://www.scopus.com/inward/record.url?scp=85139829816&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-4874-9_5
DO - 10.1007/978-981-19-4874-9_5
M3 - Chapter
AN - SCOPUS:85139829816
T3 - SpringerBriefs in Computer Science
SP - 53
EP - 65
BT - SpringerBriefs in Computer Science
PB - Springer
ER -