Kubernetes scheduling: Taxonomy, ongoing issues and challenges
C Carrión - ACM Computing Surveys, 2022 - dl.acm.org
Continuous integration enables the development of microservices-based applications using
container virtualization technology. Container orchestration systems such as Kubernetes …
container virtualization technology. Container orchestration systems such as Kubernetes …
Deep learning workload scheduling in gpu datacenters: A survey
Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …
development of a DL model is a time-consuming and resource-intensive procedure. Hence …
Deep learning workload scheduling in gpu datacenters: Taxonomy, challenges and vision
Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …
Automatic policy generation for {Inter-Service} access control of microservices
Cloud applications today are often composed of many microservices. To prevent a
microservice from being abused by other (compromised) microservices, inter-service access …
microservice from being abused by other (compromised) microservices, inter-service access …
[HTML][HTML] Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture
Artificial intelligence (AI) research and market have grown rapidly in the last few years, and
this trend is expected to continue with many potential advancements and innovations in this …
this trend is expected to continue with many potential advancements and innovations in this …
Online evolutionary batch size orchestration for scheduling deep learning workloads in GPU clusters
Efficient GPU resource scheduling is essential to maximize resource utilization and save
training costs for the increasing amount of deep learning workloads in shared GPU clusters …
training costs for the increasing amount of deep learning workloads in shared GPU clusters …
On a Meta Learning-Based Scheduler for Deep Learning Clusters
J Yang, L Bao, W Liu, R Yang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep learning (DL) has become a dominating type of workloads on AI computing platforms.
The performance of such platforms highly depends on how distributed DL jobs are …
The performance of such platforms highly depends on how distributed DL jobs are …
[PDF][PDF] DynamoML: Dynamic Resource Management Operators for Machine Learning Workloads.
MC Chiang, J Chou - CLOSER, 2021 - scitepress.org
The recent success of deep learning applications is driven by the computing power of GPUs.
However, as the workflow of deep learning becomes increasingly complicated and resource …
However, as the workflow of deep learning becomes increasingly complicated and resource …
Making distributed edge machine learning for resource-constrained communities and environments smarter: contexts and challenges
The maturity of machine learning (ML) development and the decreasing deployment cost of
capable edge devices have proliferated the development and deployment of edge ML …
capable edge devices have proliferated the development and deployment of edge ML …
Distributed artificial intelligence: review, taxonomy, framework, and reference architecture
Artificial intelligence (AI) research and market have grown rapidly in the last few years and
this trend is expected to continue with many potential advancements and innovations in this …
this trend is expected to continue with many potential advancements and innovations in this …