This review covers research on the topic of mixed criticality systems that has been published
since Vestal's 2007 paper. It covers the period up to end of 2021. The review is organised …

Benefiting from rich resources and virtualization technologies, hybrid cloud has emerged as
a promising solution to processing large-scale scientific workflow applications for users in a …

This review covers research on the topic of mixed criticality systems that has been published
since Vestal's 2007 paper. It covers the period up to end of 2021. The review is organised …

For deep learning cloud computing platforms, file system is a fundamental and critical
component. Hadoop distributed file system (HDFS) is widely used in large scale clusters due …

With the drastic development of Internet of things, the number of connected mobile users
(MUs) is increasing at an unprecedented speed. The increasing popularity of MUs has …

Previous work focuses on the problem of energy for Imprecise Mixed-Criticality Systems on
multiprocessor platforms, which provides degraded services for low criticality tasks in the …

Cloud computing offers resources to users for computing and storage needs on the policy
pay-what-you-use model. A private cloud is an excellent cost-saving option because the …

Energy-aware mixed-criticality (MC) real-time scheduling has attracted the attention of many
researchers. However, they only focus on the conventional MC task model, in which all low …

Neural networks are increasingly applied into real-time and embedded Artificial Intelligent
(AI) systems like autonomous driving system. Such resource-constrained systems cannot …

Previous studies focus on the classical mixed-criticality task model with preemptive dynamic
priority scheduling in which all low-criticality (LO) tasks are dropped in the high-criticality …