Scheduling of tasks in distributed environments, like cloud and grid computing platforms,
using deadlines to provide quality of service is a challenging problem. The few existing …

Cloud providers place tasks from multiple applications on the same resource pool to
improve the resource utilization of the infrastructure. The consequent resource contention …

Scientific research and development campaigns are materialized by workflows of
applications executing on high-performance computing (HPC) systems. These applications …

The demand for distributed applications has significantly increased over the past decade,
with improvements in machine learning techniques fueling this growth. These applications …

The success of cloud computing makes an increasing number of real-time applications such
as signal processing and weather forecasting run in the cloud. Meanwhile, scheduling for …

Cloud computing has become an important paradigm in which scalable resources such as
CPU, memory, disk and IO devices can be provided to users to remotely process their …

Containers simplify the packaging, deployment and orchestration of diverse workloads on
distributed infrastructure. Containers are primarily used for web applications, databases …

With widespread applications in image recognition, language translation, computer vision
and other areas, deep learning (DL) have been proliferating over the past decade …

After the advent of the Internet of Things and 5G networks, edge computing became the
center of attraction. The tasks demanding high computation are generally offloaded to the …

Multiple applications that execute concurrently on heterogeneous platforms compete for
CPU and network resources. In this paper, we consider the problem of scheduling …