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Yangming Zhao
Researcher at State University of New York System
Publications - 69
Citations - 1042
Yangming Zhao is an academic researcher from State University of New York System. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 12, co-authored 50 publications receiving 631 citations. Previous affiliations of Yangming Zhao include Nanjing University & University of Science and Technology of China.
Papers
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Proceedings ArticleDOI
Rapier: Integrating routing and scheduling for coflow-aware data center networks
Yangming Zhao,Kai Chen,Wei Bai,Minlan Yu,Chen Tian,Yanhui Geng,Yiming Zhang,Dan Li,Sheng Wang +8 more
TL;DR: This work presents Rapier, a coflow-aware network optimization framework that seamlessly integrates routing and scheduling for better application performance, and demonstrates that Rapier significantly reduces the average coflow completion time.
Proceedings ArticleDOI
Availability-aware mapping of service function chains
TL;DR: This paper defines an optimal availability-aware SFC mapping problem and presents a novel online algorithm that can minimize the physical resources consumption while guaranteeing the required high availability within a polynomial time.
Journal ArticleDOI
Towards Practical and Near-Optimal Coflow Scheduling for Data Center Networks
TL;DR: This paper finds that minimizing the average CCT of a set of coflows is equivalent to the well-known problem of minimizing the sum of completion times in a concurrent open shop, and derives a 2-approximation algorithm for coflow scheduling.
Journal ArticleDOI
Offloading Tasks With Dependency and Service Caching in Mobile Edge Computing
TL;DR: Wang et al. as discussed by the authors studied how to efficiently offload dependent tasks to edge nodes with limited (and predetermined) service caching, and designed an efficient convex programming based algorithm (CP) to solve this problem.
Proceedings ArticleDOI
Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing
TL;DR: In this article, the authors propose an efficient federated learning with hierarchical aggregation (RFL-HA) problem, which divides the edge nodes into K clusters by balanced clustering.