Federated learning (FL) is a transformative approach to Machine Learning that enables the
training of a shared model without transferring private data to a central location. This …

Complex data-parallel job contains task dependency information defined as Directed Acyclic
Graph (DAG). For convenience, the DAG presented data-parallel jobs are named as DAG …

In the past decade, Internet of Things (IoT) technology has been widely used in various
applications in daily life. Currently, IoT applications primarily depend on powerful cloud data …

In Mobile Edge Computing (MEC), the network's edge is equipped with computing and
storage resources in order to reduce latency by minimizing communication with remote …

Workload characterization is critical for resource management and scheduling. Recently,
with the fast development of container technique, more and more cloud service providers …

Existing Cloud deployments usually perform automated scheduling and rescheduling based
on Quality of Service (QoS) objectives. Services are migrating towards Quality of Experience …

The concept of the Internet of Things has widely used in various applications in life from
home to big cities that have a variety of edge devices to facilitate edge computing. This …

Reducing power consumption with tolerable performance degradation is a fundamental
challenge in today's containerized High Performance Computing (HPC). Most Dynamic …

Managing a multi-tenant network presents formidable challenges to managed service
providers (MSPs) as they endeavour to provide exceptional service quality to clients …

The workload co-location, such as deploying offline analysis workloads with online service
workloads on the same node, has become common for modern data centers. Workload co …