In the realm of edge computing, the increasing demand for high Quality of Service (QoS),
particularly in dynamic multimedia streaming applications (eg, Augmented Reality/Virtual …

Many real-time applications (eg, Augmented/Virtual Reality, cognitive assistance) rely on
Deep Neural Networks (DNNs) to process inference tasks. Edge computing is considered a …

The emerging edge computing infrastructure provides distributed and heterogeneous
resources closer to where data is generated and where end-users are located, thereby …

Managing tail latency is a primary challenge in designing large-scale Internet services.
Queuing is a major contributor to end-to-end tail latency, wherein nominal tasks are …

Edge computing is an emerging technology that aims to include latency-sensitive and data-
intensive applications such as mobile or IoT services, into the cloud ecosystem by placing …

Edge computing has recently gained momentum as it provides computing services for
mobile devices through high-speed networks. In edge computing system optimization, deep …

Federated Learning (FL) empowers multiple clients to collaboratively learn a model,
enlarging the training data of each client for high accuracy while protecting data privacy …

Despite the demand for realtime deep learning applications such as video analytics at the
edge, resource-constrained edge devices can largely not process video streams at their …

Currently, huge amounts of data are produced by edge device. Considering the heavy
burden of network bandwidth and the service delay requirements of delay-sensitive …

Latency-sensitive and data-intensive applications, such as IoT or mobile services, are
leveraged by Edge computing, which extends the cloud ecosystem with distributed …