Machine learning approaches to IoT security: A systematic literature review
With the continuous expansion and evolution of IoT applications, attacks on those IoT
applications continue to grow rapidly. In this systematic literature review (SLR) paper, our …
applications continue to grow rapidly. In this systematic literature review (SLR) paper, our …
A systematic survey of data mining and big data analysis in internet of things
Y Zhong, L Chen, C Dan, A Rezaeipanah - The Journal of …, 2022 - Springer
Abstract The Internet of Things (IoT) is an emerging paradigm that offers remarkable
opportunities for data mining and analysis. IoT envisions a world where all smartphones …
opportunities for data mining and analysis. IoT envisions a world where all smartphones …
Custom scheduling in Kubernetes: A survey on common problems and solution approaches
Z Rejiba, J Chamanara - ACM Computing Surveys, 2022 - dl.acm.org
Since its release in 2014, Kubernetes has become a popular choice for orchestrating
containerized workloads at scale. To determine the most appropriate node to host a given …
containerized workloads at scale. To determine the most appropriate node to host a given …
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 …
Runtime performance prediction for deep learning models with graph neural network
Deep learning models have been widely adopted in many application domains. Predicting
the runtime performance of deep learning models, such as GPU memory consumption and …
the runtime performance of deep learning models, such as GPU memory consumption and …
Joint optimization with DNN partitioning and resource allocation in mobile edge computing
With the rapid development of computing power and artificial intelligence, IoT devices
equipped with ubiquitous sensors are gradually installed with intelligence. People can enjoy …
equipped with ubiquitous sensors are gradually installed with intelligence. People can enjoy …
Graft: Efficient inference serving for hybrid deep learning with SLO guarantees via DNN re-alignment
Deep neural networks (DNNs) have been widely adopted for various mobile inference tasks,
yet their ever-increasing computational demands are hindering their deployment on …
yet their ever-increasing computational demands are hindering their deployment on …
Uncovering energy-efficient practices in deep learning training: Preliminary steps towards green ai
Modern AI practices all strive towards the same goal: better results. In the context of deep
learning, the term" results" often refers to the achieved accuracy on a competitive problem …
learning, the term" results" often refers to the achieved accuracy on a competitive problem …
Autofl: A bayesian game approach for autonomous client participation in federated edge learning
Given that devices (ie, clients) participating in federated edge learning (FEL) are
autonomous and resource-constrained in nature, it is critical to design effective incentive …
autonomous and resource-constrained in nature, it is critical to design effective incentive …