Machine learning (ML) tasks are one of the major workloads in today's edge computing
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …

Edge computing has become popular in the last decade and will advance in future to
support real-time actionable analytics at the devices. One of the fundamental problems for …

In edge computing scenarios, the distribution of data and collaboration of workloads on
different layers are serious concerns for performance, privacy, and security issues. So for …

Deep learning (DL) training jobs bring some unique challenges to existing cluster
managers, such as unpredictable training times, an all-or-nothing execution model, and …

Application workload scheduling in hybrid Cloud-Edge-IoT infrastructures has been
extensively researched over the last years. The recent trend of containerizing application …

Edge computing brings computing and storage resources closer to (mobile) end users and
data sources, thus bypassing expensive and slow links to distant cloud computing …

In edge computing, computation offloading of video stream tasks and collaboration
processing among edge nodes is a huge challenge. The previous research mainly focuses …

Efficiently scheduling deep learning jobs on large-scale GPU clusters is crucial for job
performance, system throughput, and hardware utilization. It is getting ever more …

The cloud-edge collaborative inference demands scheduling the artificial intelligence (AI)
tasks efficiently to the appropriate edge smart device. However, the continuously iterative …

Collaborative edge computing (CEC) is a promising technology for supporting latency-
sensitive Internet of Things (IoT) applications by distributing computation tasks among edge …