23 citations · 34 across the 8 of their papers we have counts for
10 papers
Masked Contrastive Representation Learning
Yuchong Yao, Nandakishor Desai, Marimuthu Palaniswami
Masked image modelling (e.g., Masked AutoEncoder) and contrastive learning (e.g., Momentum Contrast) have shown impressive performance on unsupervised visual representation learnin…
Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions
Mohammad Goudarzi, Marimuthu Palaniswami, Rajkumar Buyya
Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT applica…
Online Slice Reconfiguration for End-to-End QoE in 6G Applications
Dibbendu Roy, Aravinda S. Rao, Tansu Alpcan +3
End-to-end (E2E) quality of experience (QoE) for 6G applications depends on the synchronous allocation of networking and computing resources, also known as slicing. However, the re…
Achieving AI-enabled Robust End-to-End Quality of Experience over Radio Access Networks
Dibbendu Roy, Aravinda S. Rao, Tansu Alpcan +2
Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The End-to-end (E2E…
A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments
Mohammad Goudarzi, Marimuthu Palaniswami, Rajkumar Buyya
Fog/Edge computing is a novel computing paradigm supporting resource-constrained Internet of Things (IoT) devices by the placement of their tasks on the edge and/or cloud servers.…
Achieving QoS for Real-Time Bursty Applications over Passive Optical Networks
Dibbendu Roy, Aravinda S. Rao, Tansu Alpcan +2
Emerging real-time applications such as those classified under ultra-reliable low latency (uRLLC) generate bursty traffic and have strict Quality of Service (QoS) requirements. Pas…